OSOZ World

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2020/2021 world

TECHNOLOGY VS. COVID-19

The coronavirus pandemic has accelerated the deployment of telemedicine and virtual care. What’s next?

THE FUTURE IS IN DOUBT

Robots with synthetic empathy, AI embedded in daily lives, health data analysis. Ethical challenges for humanity.

HEALTHCARE RE-IMAGINED

Interviews with digital health leaders.

Eric Topol, Rasu Shrestha, John P. Kotter, Maneesh Juneja, Denise Silber.

Integrating healthcare Caring for Patients

KAMSOF T Next generation healthcare IT solutions

WWW.KAMSOFT.PL

Nota bene

Understanding the transformation In discussions on the digital transformation of healthcare, the emphasis is usually put on digitization. The context of transformation is neglected. In reality, it is not about the implementation of IT systems in hospitals or e-health solutions such as e-prescription, but a profound reformation of the current model of healthcare, also in areas that seemingly have little to do with digitization.

Health in a schematic framework

Artur Olesch Redakcja Czasopisma OSOZ Polska OSOZ World  redakcja@osoz.pl Czytaj bieżące wydanie w aplikacji mobilnej OSOZ News

If everything worked well in healthcare, there would be no need for major changes. But it does not.

Health systems around the world are in deep crisis. Expenditures are rising due to an aging population and an increasing burden of chronic non-infectious diseases such as diabetes, cardiovascular diseases and mental illnesses. The requirements and expectations of patients are also growing, as are inequalities in access to health services. There is a shortage of doctors and nurses in almost every corner of the world. These are not just data and statistics, but facts leading to human suffering and deaths that could be prevented if an efficient and effective health system was built.

Such a wide set of problems requires radical action. Small modifications, larger financial outlays, more effective medicines and more modern medical technologies are no longer enough. We need to transform the organization of health care, the way doctors work, the role of the patient as well as planning and implementing health policy. A chance for a new opening in healthcare has appeared quite recently, along with computerization, development of artificial intelligence systems, robotics and the Internet. We are talking about the fourth technological revolution that has changed the way we live, communicate and participate in society. And the most significant acceleration came with the advent of smartphones – miniature computers at hand. For medicine, this discovery may not be as unequivocally reflected in treatment effects as the invention of antibiotics, but it gives us tools of unimaginable potential.

It is not technology, but possibilities The whole health care journey through digitization started with a simple concept: replacing paper records with their digital form. The aim was to facilitate the work of doctors, streamline internal administrative processes in health care facilities, and make it easier to account for the costs of medical services. The first IT systems for hospitals were created based on the functionality focused on these priorities.

Along with this, we could see rapid development of IT companies focused on the development of software for health care. It was not until the popularization of access to the Internet, personal computers and then smartphones that the digitalization of health care began to leave the four walls of hospitals.

Completely new possibilities such as telemedicine and remote medical consultations have emerged.

They gave rise to the assumption of modern healthcare in the era of digitization. The terminology has also evolved – today we are talking about ‘digital health’, and less often about ‘e-health’. The difference is that the first concept focuses on patient-centered health care processes, while the second focuses on IT solutions. In the meantime, new technologies have emerged, including robotics, artificial intelligence, big data – practical solutions driving a paradigm shift in health care.

Care is replacing treatment This new health care is moving away from the classic role of the patient, hospital and doctor. We are talking about a transformation process that will not take several years, but several decades. One thing is certain: we are only at the beginning of a long road, and before the first benefits for patients can be seen, we will have to do the grassroots work to create a uniform ecosystem of health data.

The target station of this transformation is to move away from medicine based on treatment of a sick patient – which is reactive medicine – towards prevention and holistic, personalized care of every citizen – health risk management. The patient is no longer a disease entity, a unit of account in the reim-

bursement scheme, but an entity co-responsible for health. A system of services is being built around the patients to involve them in the health care process, so that health and wellbeing are the result of cooperation between the patient and the doctor, and not the task of the doctor acting in a paternalistic role. To provide the services quietly in the background of everyday life, without absorbing our attention. Many models have been developed on the basis of these assumptions, such as coordinated care or care focused on the patient’s needs. All of them are linked by a single denominator of digitization: informational coupling of health care processes and smooth flow of patient data through the entire health care ecosystem. This, in turn, is only possible when the data is in digital form and is therefore not trapped in data silos, i.e. paper files locked in the place where they were created.

Data is knowledge that is missing today. Doctors make decisions based on residual facts, having no control over what happens to the patient when he or she leaves the doctor’s office. Similarly, the patient makes decisions based on guesses and intuition. The doctor is called when the first symptoms of the disease occur, sometimes developing for years. It is often too late for classical medicine to help.

Ministries of health, when planning specific actions in the field of population health or investments, are guided by historical data that tend to change quickly. In such an inefficient model, money leaks through growing holes. As a result, the system requires ever-increasing budgets, which do not translate into quality of services. Even rapid progress in the field of new medicines and treatment technologies has not allowed dealing with lifestyle diseases.

It will not be an easy mission Several fundamental technological and strategic problems need to be overcome in order to achieve idealistic digital health. The digitalization of healthcare developed rapidly in the initial period of the IT revolution, but it was progress without rules, data exchange standards or strategies. Many systems collecting data in different formats were created in this way, which makes it impossible to exchange it.

On the other hand, new technologies are constantly emerging, such as mobile health apps, which remain outside the sphere of regulation. This chaos is slowly getting organized, but it will be a long time before we can talk about interoperability of systems and exchange of medical data. And this is a prerequisite for moving to a higher level of digital health – connected health systems.

This shows that politics is not keeping up with the wave of technology triggered by the fourth technological revolution, it cannot actively stimulate and formulate the development of, for example, artificial intelligence, but it rushes to frame what the technology industry will bring to the market. This does not facilitate the sustainable development of technology, but may lead to the emergence of new problems undermining the benefits of digitization, from ethical controversies to the widening digital divisions in society.

Leaders, technology and a different point of view It is also important to remember that transformation, although driven by the development of technology, means a change in the way we think about health. This change has to be implemented at the patient level, and its stimulation will be triggered by a pro-innovative health policy. When we talk about shifting the focus from treatment to prevention, it is necessary to take into account the whole health care infrastructure or funding directed towards hospitality and drug reimbursement. This is almost like the transition from a fossil fuel economy to clean energy from renewable sources. Every transformation entails a change in social structures, a shift in employment, and requires large investments, education, vision and leadership. However, the role of politics is not only to regulate the status-quo, but to actively formulate a new reality. New applications – systems based on artificial intelligence or robots – are not just additional tools, but solutions influencing social processes. It should be remembered that we are talking about health, where the focus is placed on different values than those governing other branches of the economy. One of them is health as a fundamental right of the patient. For the next decades to pass under the sign of approaching such a goal, the digital transformation of health care must be under the sign of wise digitization as a factor stimulating the transformation.

» The new health care should move away from the classic role of the patient, hospital and doctor.«

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Technologies built in good faith Stop disrupting healthcare!

How to verify health apps so doctors could prescribe them How does Finland use health and social data for the public benefit?

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innovations

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Technologies that help fight the coronavirus Precision medicine. When machines become smarter than doctors New study confirmsvirtual reality can becomea new painkiller 3 Learnings From Stanford Storing medical informationbelow the skin’s surface

in practice

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GDPR during the crisis How to prepare medical workforce for digital health?

Using AI To Predict Breast Cancer And Personalize Care This Robot Knows How To Communicate To Support Patients With Chronic Illness Facebook has launched new healthcare features

reports

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Help me, robot!

8 necessary steps towards digital transformation Demystifying Algorithms The Rise of the Data-Driven Physician

new ideas

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Don’t fake it till you make it Becoming Hyperaware Explore Digital Health in Asia Our future with algorithms Plastic touch Taming the change Artificial Intelligence to put the care back in healthcare Objectivity with no empathy: how symptom checkers can help patients?

Digital disruption is not something post-apocalyptic

Health totalitarianism The future of healthcare. Will medicine become data science?

Telemedicine benefits during covid-19 pandemic. But is it here to stay?

Strengthening digital health literacy in society What the radiologist need to know about artificial intelligence

conferences

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Digital health literacy is an essential capacity to master in everyday life For patients, wearables are fantastic tools to manage health and well-being Components of digitalization: evidence, knowledge and technology AI will help surgeons to orchestrate the work and data Data For All. Not For Sale How to build a smart hospital?

Digital health needs to be embedded in the conception of the health system Becoming a self-doctor in the era of wearables

How to ensure human touch in digital healthcare driven by AI solutions?

Cyber-medicine & humans. 7 new concerns about digital healthcare The risks of basing digital health strategy on industry hype and alluring prototypes It is not enoughto just have a good idea or a nice implementation in one place Rethinking Workforce Skills To Become Ready For Future

WHAT’S YOUR OPINION?

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Stay at home. Technology will take care of everything else Where are the long-awaited benefits of digitization?

Digital health 2020 Culture, UX/UI, education, accessibility. Digitalization’s biggest barriers Unlocking the potential of digitalization by purposeful redesign of clinical processes Robots in healthcare: machines, creepy dolls, therapists or social companions?

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Technologies built in good faith Your mobile phone vibrates, displays notifications and makes sounds. Time to take the pill prescribed by the doctor; go to the gym to burn a specific number of calories; walk for an hour to supplement deficient vitamin D; go to bed to sleep long enough; start meditating to reduce stress. Can the apps of the future be effective, but also transparent, ethical and based on values important for people instead of using psychological tricks?

Dopamine as a source of market success An effective app does not let you forget about it. It must fight for our limited attention while competition from oth-

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er apps grows. The most successful apps use the principles of behavioral psychology to get us hooked. Dopamine increases automatically every time someone likes our photo on Facebook or Instagram. The

level of this happiness neurotransmitter is regulated in the brain by the dopamine system. The more likes under a selfie, the greater the satisfaction. When a message on Twitter is popular, it increases our sense of worth and the impression of social acceptance. This is the main reason behind the success of social media.

Other mobile apps must also play on our emotions and needs - otherwise they end up in the recycling bin like the vast majority of downloaded apps.

This power of technology over us is ethically ambiguous and can lead to many negative effects in real life. This is different in the case of health apps.

They are mainly about turning a single

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healthy behavior into a permanent habit.

Our mobile phone is becoming a source of motivation and discipline, reminding and helping us to maintain proper weight and regularly run or go to the gym.

Fixing errors of the technological revolution Unhealthy habits are not easy to change.

Our brain does not feel equal pleasure from eating a hamburger with fries and having a salad. The former provides a large dose of energy and rewards us with a feeling of pleasure. For many of us, an evening in front of the TV on the couch is more attractive than exercising in the gym. Both a healthy meal and training give results after a few weeks, which does not motivate us. The eternal hunger of the brain has its advantages - the evolution programmed this energy-absorbing organ to consume calories that guarantee survival and development. But calories were not so easy to obtain in the past - you had to hunt for meat or look for and gather edible plants. Today, we can get everything in the store across the street, without restrictions, which leads to energy imbalance, causing an epidemic of obesity and overweight.

The same goes for sleep - specialists recommend that a healthy rest should last 7 to 9 hours. But in the world of many possibilities and artificial light, sleep has become a waste of time. However, we still need it as much as 10,000 years ago. We are built this way as humans.

This new technology is to help us return to healthy habits. In other words: we are trying to fix the negative effects of the first (steam energy), second (electricity) and third (computerization) industrial revolution with the innovations of the fourth industrial revolution (artificial intelligence). As it turns out, all shortcuts and trends are as illusory as the wonderful power of vitamin supplements. Is the solution that provided by digital therapies in the form of apps which do not have direct side effects like drugs?

Coach or manipulator?

Today, the physical activity of an average city dweller is negligible. We don’t have to walk - we drive cars. We don’t have to hunt - we spend the whole day working at our desk, and spend the money earned on food and other goods. We don’t have to go to sleep when it gets dark - the Internet and television ensure our entertainment in artificial light. Progress will continue to disturb the structure of the

» Technologies that primarily help maintaining health and preventing disease should be made responsibly and in harmony with human nature.«

human body and brain, which just cannot keep up. Like in the case of climate change, nobody expects that everyone will stop eating meat, drive cars, and fly on vacation all of a sudden. Damage that has already been done can be repaired by creating alternative energy sources, human and environmentally-friendly smart technologies. It is a pity that we have realized this so late.

The process should be similar in healthcare and there is still time to develop new solutions wisely. Technologies that primarily help maintaining health and preventing disease should be made responsibly and in harmony with human nature, their autonomy as a social unit, and freedom. Without resorting to dubious psychological tricks and mechanisms that control us instead of helping us. In an ethical way, with the participation of doctors and teams of medical experts in a given field. The question arises, what would an ethically developed mobile app look like in practice to optimally care about physical activity? Should it sense our emotions and ask about our needs, moods and worries? Understand our laziness and convince us instead of imposing daily limits to complete, often

leading to frustration when the objective was not achieved? Or maybe it would allow you to compete not only virtually, but also in the real world, making it easier to find a partner for morning jogging or the gym? Ultimately, it would decide by itself to enter the sleep mode after the new sporting habit has been established for good. So as not to unnecessarily burden the user with digital noise. Perhaps it should include other responsibilities, such as looking after children, and suggest other alternatives to the gym? There are countless ideas.

The balance between human and technology In the age of mass digitization, we must ask ourselves what we expect from technology. Because there are plenty of examples of irresponsible revolutions, with the abovementioned climate change at the forefront. In my opinion, mobile apps designed in a responsible and harmonious way will become more trusted and popular over time. This is because they offer values that are close to us. These will be more and more important, as in an increasingly complex world people want to have control over technology instead of being controlled. There is nothing wrong with the developers making money on mobile health apps. But such tools should be transparent and based on scientific knowledge. After all, what good does it do when the obsession with diet caused by an app leads to unhealthy and excessive use of the phone to see if we are within the daily norm of calories consumed. The new generation of mobile apps will remind you of the need to return to healthy, high-quality organic food produced locally, which not only tastes better, but also guarantees sustainable farming and supports sustainable entrepreneurship. In the world tired of apps persistently fighting over our attention (and their survival on the market), “digital biological health” is the future.

One of the oldest applications installed on my smartphone is Forest. Its function is to limit time spent with the phone. The more time I spend offline without using my phone, the faster my virtual forest grows. A certain number of points finances the planting of a real tree.

This is the real value that makes me go back to Forest with pleasure, without intrusive notifications, likes or false sense of social responsibility and acceptance.

Value-based technologies have a future. 

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Stop disrupting healthcare!

Digital technologies are disrupting healthcare. Startups are shaping the future of healthcare. Patients are now empowered. The revolution has come. Digital health is a hot topic now. We often use big words to underline the importance of the upcoming changes. The consequences are already destructive.

led to doctors’ frustration, now the hype around AI is creating unrealistic expectations.

I’ve been attending numerous conferences and following the discussions on digital health. It’s time to change our narrative in digital health. Here is my list of the overused words that we should all avoid:

Words have the potential to create a state of urgency, to convince, to motivate.

People need grand visions to make significant and important changes. Martin Luther King’s speech, “I have a dream,” had a massive impact on society in the early 60s. Today’s leaders also use words to inspire.

disruptive, disruption

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But when the process of change has started, it’s time to get to work instead of repeating the same phrases. Some of the overused expressions create hype and unrealistic hopes that can’t be fulfilled.

They force unprepared reforms that crash into reality. In healthcare, the results of this “push” strategy are dramatic: EHR

Disruptive technologies. They are coming. Like the Death Star in Star Wars or the White Walker in Game of Thrones.

The new order will replace the old one.

Disruption means “a major disturbance, something that changes plans or interrupts some event or process.” Healthcare

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won’t be disrupted, it will be supported and augmented by innovative technologies. If you want to explore “disruptive healthcare technologies,” Google will show you over 90,000,000 results.

cus too much on technical aspects, while forgetting about social determinants.

I think that a far more significant challenge now is to manage the change process.

revolution

e-health

Healthcare, get ready! There will be suffering and many victims. Paternalistic medicine will be defeated forever. Better times come. Revolution is “a forcible overthrow of a government or social order, in favor of a new system.” What will this utterly new system look like?

Who are the winners, and who are the losers? Digital health is about transformation, evolution, but not a revolution.

There are some exceptions in medicine when “revolution” is the right word. For example, when the science won the battle against bacteria once Alexander Fleming discovered antibiotics.

This word means „healthcare services provided electronically via the Internet.”

This term is very often misused. It the broader meaning, „digital health” is always a better choice.

potentially AI will potentially save many lives. AI can potentially improve patients outcomes. VR can possibly help to reduce pain. Apps can likely support patients with chronic diseases. Instead of predicting the future, we should rather show what apps, VR, AR, AI, and other technologies already CAN and DO. Enough studies and examples are showing the benefits of digital technologies.

interoperability If you want to sound like an expert, you should include this word in your dictionary – it sounds complicated and professional. Without diminishing the importance of interoperability, this word is too often used as an excuse, reasoning that „digitalization is a big challenge.” When it comes to digital health, we used to fo-

exponential technologies 1-2-4-8-16. Exponential growth means that something doubles in capability or performance in each subsequent period.

Now, technologies like AI, VR, robotics, and machine learning, for example, are also exponential. I agree this word sounds simply intriguing and better than synonyms like “aggressive,” “expanding,” or even “epidemic.” Isn’t it better to name modern technologies instead of using this kind of mysterious word?

mind-shift The digitalization of healthcare requires, first of all, a new mindset. We tend to think in an old way. But now it’s time to change our attitudes and think progressively. People are different, grow, and live in different environments, have different experiences and opinions. I would say that we have to “convince” rather than “plant a new attitude in people’s minds.” Anyway, saying “we need a new mind-shift” means both everything and nothing.

be an enabler of positive changes. But healthcare doesn’t need democratization. Healthcare is not an authoritarian regime. Healthcare needs more equity and equality, has to be accessible and affordable, transparent, and fair.

patient empowerment Patients are the best experts of their own health - they should have the possibility to protect their health, and co-decide with their doctor on the therapy options.

However, having access to data stored in electronic health records doesn’t guarantee the “empowerment of the patient.”

Many other issues must be included: health and digital health literacy or patients’ needs (some prefer external help).

Linking “digitalization in healthcare” directly with “patients’ empowerment” is a huge misconception.

AI/Robots won’t replace doctors I agree, they won’t. I’ve heard this declaration recently at every digital health conference. But is it the most relevant issue we should be talking about now? I’m aware it is always repeated to gain doctors’ trust. But, in fact, I also agree with Eric Topol that AI will restore medicine.

We should discuss how AI can make healthcare more precise, help patients, support doctors and eliminate medical errors, and speed up the development of new drugs. Using the argument that AI can’t replace doctors makes the discussion negative (focusing on threats), instead of positive (focusing on opportunities). 

democratization I understand the concept of the „democratization of healthcare” and fully support it. I also agree digitalization can

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How to verify health apps so doctors could prescribe them Health apps help patients with diabetes control their blood glucose levels, assist those with back pain in performing exercises, and even offer therapeutic sessions based on scientifically proven techniques to people suffering from anxiety or depression. Mobile apps can heal. And since they encourage patients to follow the doctor’s instructions, in some cases, they can prove to be as effective as medication. Should they, therefore, be recommended by doctors and reimbursed by health insurers?

In November, the German parliament adopted a new law, which is expected to accelerate digitalisation in healthcare.

The new regulations also include an option of reimbursing mobile medical apps.

This makes Germany the first country in

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the world where a doctor will be able to prescribe a smartphone app just like a drug. However, progressive regulations raise an essential question: How to access the quality, usability and value for patients of the health apps?

Professional help or well-being-oriented tools?

Although only several years have passed since smartphones entered the market, health apps have already gained immense popularity. There are currently over 330,000 different mobile health apps.

Yet, the vast majority of them have nothing to do with medical sciences. They are typically fitness apps whose purpose is motivating the users to play sports, monitoring their physical exertion, or helping them lose weight or abide by their diet.

Applications developed together with or by the medical professionals and based on scientific evidence constitute only a tiny percentage of all apps called “health apps”. An example of such an app is Kaia. It was designed to help patients

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with back pain by guiding them through the entire rehabilitation process while measuring the results at the same time.

Another is Woebot – a chatbot developed based on cognitive-behavioural therapy, which helps patients suffering from light depression change their negative thought patterns and perception of reality.

Until now, all apps that even remotely concerned the subject of health, e.g. ones facilitating healthy eating, were classified as “health apps”. But as the digital tools began to resemble medical devices more and more, the term “digital therapeutics” came into play. Digital therapies are usually designed using evidence-based medicine. And here is where problems begin to emerge. How can we confirm the effectiveness of such applications? When it comes to medicine, the process is unambiguous. It is necessary to conduct highly-regulated clinical trials while abiding by all required safety guidelines and go through the entire process of introducing the drug onto the markets in all countries individually. In the case of applications, however, such guidelines do not exist.

Their developers are guided by subjective criteria only.

For many applications, a certification obtained in a procedure meant for medical devices may serve as proof of their high quality. In Europe, the CE marking guarantees that a product is safe for patients (“indicates conformity with health, safety, and environmental protection standards for products sold within the European Economic Area”). Many applications, primarily those based on artificial intelligence, have already been approved for use as medical products.

As the competition on the mobile health market increases, having a certification mark is becoming an advantage that helps convince investors and users. An increasing number of laws is being introduced in Europe to ensure that using such digital tools is safe. The high penalties provided for in the GDPR (General Data Protection Regulation) are meant to guarantee that any user data collected by them will be processed appropriately. In turn, on May 26, 2020, the Medical Devices Regulation (MDR), adopted by the European Parliament in May 2017, will enter into force as well. The MDR will cover numerous digital solutions.

Without going into the explicit details of the new law – software designed to monitor physiological processes, and therefore all applications measuring life parameters, will be classified as either

» Health apps offer great benefits for patients. Especially in preventive health, they could become a cure for the non-communicable diseases epidemic.« class IIa or IIb software, provided that the information being monitored is vital to the patient’s life. Systems providing the information required to make diagnostic or therapeutic decisions will be classified as IIa. If the data is critical for the patient’s well-being, the software will be marked as a class III product. Any other apps will still be designated as class I. Note that in this case, class I indicates the lowest level of risk associated with using the app while class III means the highest.

What about clinical tests?

Since prescription drugs must go through clinical trials, why should medical apps not meet the same standards? The answer: practically, clinical trials for apps make no sense. Research on new drugs may take years, and it certainly requires enormous financial resources. As such, only large pharmaceutical companies can afford it. Health apps are often developed by small startups which, despite occasionally having generous backing of wealthy sponsors, cannot afford to undergo this type of a complicated research process. Another critical issue is the fact that digital technologies tend to age very rapidly. An application which is not updated and developed further for a period of just 2 to 3 years becomes obsolete. At the same time, it typically takes around 8 to 10 years for a drug actually to be introduced onto the market following its invention.

It seems that the most sensible solution would be to develop guidelines for

application developers and then follow up by creating an external validation system operated by an independent institution. The British healthcare system has already adopted such an approach, and the NHS now maintains its own Apps Library. Only solutions that successfully pass the screening process based on clearly defined criteria can be included in it. Thanks to this, the app’s creators know whether they want to create just another “lifestyle” app or one that meets the very demanding NHS guidelines. Therefore, as of March 2019, the NHS Apps Library contained only 79 solutions. Even though this is a rather unimpressive number, the patients can be sure that all these solutions have been precisely tested.

Case studies of different evaluation criteria The criteria used by the NHS Apps Library include efficiency, medical safety, data protection, usability and availability, data interoperability, and technical stability. Several or even dozens of questions were prepared to evaluate them, with an appropriate number of points awarded for each answer.

Until today, several different rating systems have been created. The “Mobile Application Rating Scale (MARS)”, developed in 2016, primarily uses qualitative assessments such as commitment, functionality, aesthetics, information quality, objective quality assessment, and application characteristics. Another solution called Enligt, a methodology created by a group of scientists for apps used in psychiatry applications, takes into account such factors as usability, graphic design, user involvement, content, therapeutic impact, objective overall assessment, the app’s value as part of coordinated therapeutic activities, trustworthiness and reliability, evidence-based effectiveness, data protection, level of privacy as well as external verification of the app’s data protection mechanisms and verbal recommendations.

The American Psychiatric Association also adopted proprietary evaluation methods that are focused on elements such as data security/privacy, benefits/effectiveness – clinical evidence, ease of use, and interoperability. On the other hand, in Germany, the Bertelsmann Foundation synthesised various approaches and created the AppQ – a 9-point scale for transparent assessment of health apps’ quality.

Although many of these approaches use similar criteria, i.e. data security, us-

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» We have to separate well-being and fitness apps from evidencebased therapeutic interventions.«

increase the patients’ engagement in their own health and reinforce preventive healthcare practices as a result. How does one measure aesthetics or ease of use? These factors would be rated differently by a young person who is familiar with new technologies than a person who did not grow up in the age of digitisation (digital natives). And how do you measure the motivational factor, which depends heavily on the individual personality traits of the user? Considering the above, focusing primarily on concrete criteria, such as data security, data exchange in the healthcare system and interoperability seems very reasonable.

New concepts still have to be developed ability, privacy and interoperability, it is never that easy to objectively assess the software. The utility factor alone is challenging to measure. Is an app which allows patients to access electronic medical records useful and does it add value? After all, access to information can

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Subjective criteria would thus require testing conducted by users representing the target groups of given solutions.

However, we have already established that we wish to avoid approaching a clinical trial-like methodology. On the other hand, users of the Android and Apple store can already rate apps – the highestrated ones enjoying better positioning in

search results and greater trust. Theoretically, this kind of evaluation can be manipulated. The solution proposed by the Bertelsmann Foundation, however, includes forming a special coordinating commission. Here comes another problem: can we develop objective criteria for apps? Their impact on patient behaviour always depends on the patient’s personal characteristics, expectations and needs. For some, even a seemingly ordinary fitness app can lead to lifestyle changes that help to avoid cardiovascular diseases.

Once an app passes the evaluation and verification process, doctors will be able to recommend such mobile health solutions with ease as there will be no concerns regarding their personal responsibility. As of today, it is difficult to expect them to do so since they are responsible for their patients’ health and cannot rely on unverified methods. Besides, introducing prescription apps is such a significant change in health care traditions that we are talking about a real cultural transformation that may well take years to be accepted for good. 

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How does Finland use health and social data for the public benefit?

Finland is not only the happiest country in the world but also leads the way in access to health and social data for research and innovation. Creating a fair, transparent and prosperous economy based on data requires smart legal ecosystem and strong support from the citizens.

Karolina Mackiewicz MyData Global

‘Groundbreaking’ legislation Better innovation opportunities, quicker access to comprehensive ready-combined data, smoother permit procedures needed for research – those are some of the benefits for society, academia or business announced by the Ministry of Social Affairs and Health of Finland when the Act on the Secondary Use of Health and Social Data was introduced.

It came into force on 1st of May 2019.

According to the Finnish Innovation Fund SITRA, which was involved in the development of the legislation and carried out the pilot projects, it’s a ‘groundbreaking’ piece of legislation. It’ not only effectively introduces a one-stop-shop for data but it’s also one of the first, if not

the first, implementations of the GDPR (the EU’s General Data Protection Regulation) for the secondary use of data in Europe.

The aim of the Act is “to facilitate the effective and safe processing and access to the personal social and health data for steering, supervision, research, statistics and development in the health and social sector”. A second objective is to guarantee an individual’s legitimate expectations as well as their rights and freedoms when processing personal data. In other words, the Ministry of Health promises that the Act will help eliminate the

administrative burden in access to the data by the researchers and innovative businesses while respecting the privacy of individuals and providing conditions for the ethically sustainable way of using data.

Leading the way in the data economy In 2017, Tekes – a Finnish government agency that activated and funded research and development projects for companies, universities and research units (now Business Finland) – estimated that, in 2030, the data economy might account for 30% of Finland’s GDP. There

The aim is to balance privacy with innovation and to » ����������������������������������������������������� introduce a human-centric approach to the data.« OSOZ World 2020

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Finland is taking the leader’s position in linking information and knowledge management to digitisation, experimentation, openness and integration of services.

Photo: Roadmap Towards a Fair Data Economy (source: Sitra Fund)

is excellent potential, which might be missed if the access to the data is limited or if the information is misused.

When it comes to access to the data, it is estimated that 60+ national and regional registries in Finland collect the health and social data of some sort. Some of the regional registries are in the planning phase; thus, it’s challenging to provide the precise number here. However, 60+ suggests that the data needed for some research or innovation might be difficult to obtain if located in more than one place.

What about the risk of data misuse?

Health and social data are considered sensitive data. At the same time, it’s valuable information that can support healthcare and social welfare processes and systems, development of new medicines or technology promoting health.

The Act addresses those challenges. It supports the Finnish vision of the fair economy based on data, promoted in Europe by SITRA (check A Roadmap for a Fair Data Economy here). Thanks to the legislation and substantial leverage from the various ministries, organisations and businesses, Finland has a chance to be a pioneer in data economy in the EU. The fact that it is the most digitally advanced nation in Europe, accord-

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ing to the European Commission’s Digital Economy and Society Index (DESI) published in June, only helps. In DESI, Finland ranked highest in digital public services and human capital and finishing first in several key subsectors, including 5G and e-health services.

How will the legislation work in practice?

The Act came into force in May 2019, which marked the beginning of the transition period. During that time, the data permit authority which will grant the data permits in a centralised manner will be established. It should begin operations next year. It’s important to mention that the access to data will be controlled and only the results of the analytics will be used externally. The data will stay secure, and all processing will happen in a safe user environment.

If all goes well – and it does not seem there will be bumps on the road – the Act will speed up the permit-granting processes for planning, research and innovations, unify currently fragmented decision procedures and develop Findata − a one-stop-shop for data. Thanks to this internationally unique legislation, Finland is taking the leader’s position in linking information and knowledge man-

agement to digitisation, experimentation, openness and integration of services.

Is it perfect?

The limitation of the Act is that it applies to the health and social data collected already in the registries and that the consent for the data use is centralised and beyond the individual’s control. The personal health and social data generated daily during exercise, shopping and other activities is another asset that could be used by the public and private sector to improve the life and health of the people.

This can only be achieved with a cohesive and transparent data infrastructure and protections that balance privacy with innovation and must be based on the human-centric approach to the data, where the individual has the power to decide how their data is being used. The idea was explored by the Digital Health Revolution project (2014–2018) and is now the critical topic of the MyData Global, a non-profit organisation based in Helsinki. It also aims to “empower individuals with their personal data, thus helping them and their communities develop knowledge, make informed decisions, and interact more consciously and efficiently with each other as well as with organisations.” 

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Technologies that help fight the coronavirus In order to fight the coronavirus epidemic, health care systems take all possible measures, starting from quarantining patients, through closing schools, and ending with canceling mass events. What has also turned out to be helpful are new technologies, especially telemedicine, robotics and artificial intelligence. Here are a few examples.

Telemedicine Airborne diseases quickly spread in densely populated areas. In the last few weeks, we have observed the spread of such a disease in a globalized world, where it can easily travel across borders, with more and more outbreaks. Not all countries are capable of taking as farreaching steps as China, where multi-million population centers have been isolated.

Anyway, it also applies to the common cold and flu viruses, which are contracted by millions of people every year.

According to the data gathered by the

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World Health Organization, up to 5 million cases of the flu are reported around the world every year. 650,000 people die.

Paradoxically, a sick patient going to a doctor’s appointment may infect several other people along the way, for example on public transportation or in the waiting room. In Wuhan, where health centers are literally besieged, the chances of getting to a doctor are severely limited. No wonder that the biggest telemedical platform, “Ping An Good Doctor� recorded a 10-fold increase in the number of users in the first weeks of the epidemic. The number of consultations has risen 9-fold

and in accordance with data published in mid-February, it has exceeded 1.1 billion visits. Obviously, determining whether we are dealing with the flu or the coronavirus requires testing in a medical center. Nevertheless, teleconsultations make it possible to initially assess the general condition of the patient and take the first steps. Moreover, if it is necessary, doctors from other regions of the world can be asked to handle such consultations whenever the health care system is burdened as extremely as is currently the case in Wuhan in China. In extreme cases, a patient with a suspected coronavi-

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person has an elevated temperature, and someone might have already taken medications to lower it.

The sick are always quarantined, regardless of whether they need intensive hospital treatment or develop only a mild disease, because they may still infect others. When it comes to the scale of the Covid-19 outbreak in China, doctors and nurses, who are overburdened with work, also become infected. This is why disinfecting robots which use ultraviolet light are placed in hospital wards. Telepresentation systems make it possible to complete the ward round without the physical presence of a doctor by the patients’ beds.

In one of the hotels turned into quarantine wards, a robot delivers food doorto-door. The patients just need to send a massage via WeChat, an application that is very popular in China. For China, the epidemic is a test for the country’s innovativeness. Many companies have started intensive work on updated versions of autonomous robots, which can for example replace people who patrol the streets and industrial infrastructure or those who deliver goods.

Artificial intelligence rus infection could be examined at home, provided that his or her general condition is good. After all, staying at home is the best prevention. What can also prove to be useful are new services, such as mobile applications for online shopping or ordering food with delivery.

Mass adaptation of wearables, such as smart watches and bands, which regularly collect data on health parameters (including body temperature) could help detect the first symptoms of the disease and notify sanitary services. This could help, because so far, few people have such devices. In this case, it would be possible to make a diagnosis as soon as the first symptoms appear, which would significantly limit the spread of the epidemic.

Robotics The simplest tool in fighting the Covid19 epidemic is a contactless digital thermometer. Every day, the media show photos of medical workers or police officers measuring the body temperatures of passengers at airports from a distance.

The result is available immediately, so it is possible to screen out people with a fever. However, it needs to be noted that the efficiency of this method is limited, for example because not every sick

Zhongnan Hospital of Wuhan University was the first to use AI to fight the disease. To quickly diagnose patients reporting the symptoms of Covid-19, the hospital uses software developed by the Infervision startup. The system is based on artificial intelligence and analyzes lung X-rays to search for changes typical for patients infected with the virus.

A few dozen Chinese hospitals have already implemented the system, which is an immense help to health facilities besieged by thousands of patients. In the case of the coronavirus, quick diagnosis is key, and this is where artificial intelligence comes in. The system improves itself by examining hundreds of thousands of medical scans of both healthy and sick patients. In this way, it can recognize typical early changes in lungs, even if it is still an early stage of the disease.

Nowadays, health care systems have better access to data and solutions used to monitor epidemiological phenomena.

Ten years ago, it would have been impossible to compile maps showing the spread of the virus. John Hopkins University from the USA is the leading developer of technologies used in Geographic Information Systems (GIS). Thanks to machine learning and data mining, the system gathers data from social media sites

and publicly available resources to create interactive maps. In this way, it is possible to implement appropriate preventive measures well in advance. Today, we can precisely monitor the development of the epidemic. Apart from prevention, it is very important for another reason. Society is well-informed, which enables us to avoid the worst-case scenario, which is mass panic. The World Health Organization encourages scientists, public institutions, private companies and governmental health agencies all over the world to share available data about Covid-19.

Since we are faced with the threat of a world pandemic, only joint actions can be effective.

Much has also been said about how AI could help develop a vaccine or a cure for the coronavirus. And it will surely be put to this use, so that we can come up with an effective method of fighting the disease as soon as possible. As many scientists emphasize, for now the priority is to understand the virus. But one thing is for sure: today, we have at our disposal technologies which make us better prepared.

We can react to epidemiological threats in a shorter time and monitor them more precisely. 

» Contactless temperature measurement, telepresentation systems, teleconsultations, disinfecting robots… Today, we are better prepared to fight the epidemic than 10 years ago.«

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Precision medicine. When machines become smarter than doctors In order to even begin fixing it, healthcare requires honesty. And today, we finally have access to tools such as artificial intelligence, which make it possible to avoid diagnostic and medical errors. Humans often make mistakes, but AI rarely does.

Ashamed of imperfection A doctor’s diagnosis may be wrong. And, just like in the case of other industries, it sometimes is. The problem is, however, that in the case of healthcare, this topic is a taboo that most people tend to avoid like fire. This is a mistake. Although

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medicine requires precision, no doctor is an infallible oracle. Several years of studies, which are followed by long internships, are an unquestionably rigorous training process for anyone who would like to protect the lives and well-being of others. From the very onset of their ca-

reers, such people follow the Hippocratic oath and do everything in their power to help those in need, and, above all, do no harm. The fact that the outcomes of their actions are sometimes far from those intended is a result of inherent human weaknesses that no person can escape, i.e. the limited capacity regarding memorising and processing information, as well as making biased decisions, and, occasionally, being guided by the wrong principles. In healthcare, this may lead to severe errors, the scale of which largely remain unknown.

We only ever rely on estimates: 1,500,000 people died worldwide due

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» Artificial intelligence has no limitations characteristic of the human brain.«

to an incorrect diagnosis or a complete lack thereof. A recent study by John Hopkins University suggests that every year, 250,000 people die due to medical errors in the United States alone. Other reports include figures reaching up to 440,000 deaths annually. As such, this is the third most common cause of death in the world, right after cancer and heart and circulatory system diseases. Nonetheless, it is a disproportionately rarely discussed issue. Back in the day, this topic was avoided since little could be done to improve the state of affairs, apart from introducing checklists and safety procedures.

Accuracy beyond the capabilities of the human brain But today, a new ally has arrived on the scene. One that can help us diagnose patients and prepare treatments with the utmost precision. Artificial intelligence has no limitations characteristic of the human brain. It can absorb infinite amounts of data, analyse and compare it meticulously, and draw conclusions based on the entirety of the available knowledge.

More importantly, AI can do all of this with speed and precision that no human can. Machines can perform calculations while people can feel and think creatively. It is computers that should be given

some of the competencies in regard to diagnosis and treatment, as they have access to the aggregate data concerning all 30,000 of the known diseases, whereas a typical doctor can only recognise up to 500 of them. A doctor and an AI can form a team that will be able to finally eliminate a plethora of mistakes and omissions plaguing the healthcare sector by combining hard scientific evidence with intuition and elements of humanism. Hence, there exists no rational justification to delay the widespread use of AI tools in the world of medicine, where they will be able to minimise the scale of human errors significantly. Medical errors should be at the very end of the statistics of causes of death.

Trust the algorithms Technology companies all around the world are working tirelessly on ever-improving artificial intelligence tools for medical applications. An excellent example of such tools is symptom checkers, which are AI-based systems that analyse the patient’s symptoms and search for their potential causes in medical databases. The primary advantage of such tools is their ability to access up-to-date knowledge quickly. The downside is the fact that they still cannot access patients’ medical records and, quite predictably, cannot carry out physical examinations either.

Regardless of these limitations, these tools have already allowed some patients to discover the type of medical condition that they had been suffering from, often for years, without a proper diagnosis available. The second category of AI solutions is clinical decision support systems. Artificial intelligence can suggest the best therapy options which have worked well for other patients. For example, IBM Watson offers such advice in cancer treatment. And although its use has not yet resulted in a significant medical breakthrough, its effectiveness will undoubtedly rapidly increase with the progressing digitisation of healthcare data and the improving information exchange process between individual medical centres.

Refusing to use the best available methods which can save human lives is quite simply unethical. Similarly, it is just as immoral to sweep the problem of diagnostic and medical errors under the proverbial rug, especially at a time when we finally have the tools needed to reduce their frequency significantly. 

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New study confirms virtual reality can become a new painkiller

After putting on VR goggles, patients leave reality and enter a breathtaking, unreal world. The distraction is so enormous that brain seems not to able process other sensations, like pain for example. A new study shows that hospitalised patients using VR report a drop in pain scores.

To check if VR can be used to reduce severe pain, researchers conducted a randomised comparative-effectiveness trial. The 120 adults in the study were admitted to Cedars-Sinai Medical Center for a variety of ailments including orthopaedic problems, gastrointestinal diseases and cancer. All of the patients had an average pain score of at least three out of 10 during the 24 hours prior to participating in the study.

Half of the patients were given VR goggles with a variety of relaxing and

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meditative experiences to choose from.

They were advised to use the headsets three times a day for 10 minutes per session – and as needed for breakthrough pain – over three days.

The other half of the patients were instructed to tune their in-room TVs to the health and wellness channel, which included guided-relaxation content such as yoga and meditation. They also were asked to view the channel three times a day for 10 minutes per session and as needed for breakthrough pain. Several

times a day, nurses asked all the patients in the study to rate their pain using the standard zero to 10 scale.

The study’s findings showed the ondemand use of VR resulted in statistically significant improvements in pain compared to the TV group, with patients in the VR group averaging 1.7 points lower on the pain scale. When researchers analysed findings from the subgroup of patients with the most severe baseline pain of seven or above, VR patients averaged three points lower than the TV group.

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�������� ����������������� is still »Virtual Reality underused in pain management.«

„This is our largest and most ambitious VR study to date,” Spiegel said. „Our results support previous research that VR can meaningfully reduce pain using a nonaddictive, drug-free treatment for people experience a range of different pain conditions.”

In Spiegel’s previous study of shortterm VR therapy, completed in 2017, the results published in JMIR Mental Health showed patients using VR reported a 24% drop in pain scores.

The current study underscores that VR can be an effective tool to add to traditional pain-management protocols. „Virtual reality is a mind-body treatment that is based in real science,” Spiegel said. „It does more than just distract the mind from pain, but also helps to block pain signals from reaching the brain, offering a drug-free supplement to traditional pain management.”

Several patients have found VR so helpful in managing pain that they are now using it regularly at home. One patient, Joseph Norris, said he has suffered

Many studies show that VR absorbs the patient’s attention so that the pain sensations get reduced. It helps patients with chronic pain, children during some unpleasant medical tests, and also women during childbirth.

from chronic pain for 30 years after undergoing radiation treatment to his hip and pelvic area. The former U.S. Air Force lieutenant colonel took pain medication for 20 years until he began searching for alternative methods.

Norris tried VR six months ago, and today he uses his VR headset once a week. Norris said, „VR is a tool I use to successfully divert attention away from

my pain, and it helps me reinforce my breathing pattern.”

Spiegel and his team are currently involved in a study following patients using VR in their homes for 60 days. „I believe that one day soon VR will be part of every doctor’s tool kit for pain management,” Spiegel said.  Source: Cedars-Sinai

Copyright © 2020 by K AMSOFT S.A.

Publisher: OSOZ World K AMSOFT S.A. 1 Maja 133 40-235 Katowice | Poland Internet: www.osoz.pl E-mail: contact@osoz.pl Tel.: +48 32 209 07 05

Editor-in-chief: Artur Olesch Design: Piotr Chamera Print: INFOMAX, Katowice Graphics: freepik.com Published: October 2020 ISBN: 978-83-940479-1-7

All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests, write to the publisher, at the e-mail address: contact@osoz.pl.

Although the author and publisher have made every effort to ensure that the information in this report was correct at press time, the author and publisher do not assume and hereby disclaim any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from negligence, accident, or any other cause.

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3 Learnings From Stanford Marius Mainz spent six months as a visiting student researcher at Stanford’s Medicine Center for Digital Health. He shares with us three key takeaways.

From April to September 2019, I spent 6 months as a visiting student researcher at Stanford’s Medicine Center for Digital Health. Stanford offers a fascinating environment of knowledge and innovation, allowing faculty, students, and researchers to provoke and disrupt the status quo in all areas. To support its vision of in-

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terpreting and enhancing the transformation of the human experience in a rapidly changing world, Stanford is permanently changing and adapting to do so. Examples for 2019 are the opening of the new $2.1 billion state-of-the-art hospital or the launch of the Institute for HumanCentered Artificial Intelligence.

The Center for Digital Health is part of that transformation as well and wants to advance digital health through collaboration and exploration. During my stay, I got exposed to so many great experiences, people, and trends that I would like to share the three most important things I’ve learned with you!

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MINDSET: Every challenge is an opportunity to level up The university provides an infrastructure to thrive, to test ideas, and to engage with opportunities. People at Stanford look at problems differently. Instead of thinking, “This does not work! “one is asking: “How can we make it work? “. Complaining will not change the situation for the better; it requires your active participation.

Innovation is at the core of almost everything at Stanford. The power to transform demands that you be part of the change. Your ability to change, evolve, unlearn, and learn anew ist the critical principle for turning problems into solutions. So if you want to change and affect change, it is your gentle pressure relentlessly applied over time.

This mindset melted together with the diversity of cultures, experiences, and skills in Silicon Valley, makes Stanford a unique and special place like no other.

Move fast WITHOUT breaking things Some months ago, the Center for Digital Health moved into the office building in which Theranos was once located.

The formerly beloved biotech unicorn and its spectacular downfall hold a mirror up to Silicon Valley’s “fake it till you make it”attitude. The scandal forced everyone to understand that hype and ambition should not stand over the actual product and not over someone’s health and life.

“Move fast and break things “does not work for healthcare. But the Center for Digital Health and other Stanford’s groups successfully proved that it is possible to move fast without breaking things! When Apple partnered with Stanford University on the Apple Heart Study, the whole infrastructure had to be set up for this massive, unprecedented virtual study in a short amount of time.

Stanford managed to enrol 419.297 participants and demonstrated beside the ability of wearable technology detecting atrial fibrillation, that a fast implementation of that new virtual study design is feasible.

PATIENTS as partners Amazon learns from every click, Tesla from every mile. What about hospitals or other healthcare institutions? Do they learn from every patient?

Stanford’s significant advantage is the understanding that you have to win patients as partners through trust and education to improve healthcare. Including patient voices and perspectives at different events like Medicine X or giving them feedback on their study data are just some examples to achieve that goal.

Patient groups and communities are vital for the research context and will get even more influence in the future. Ask yourself: What do patients want? Wisdom resides within patients that are inaccessible for people who don’t have the disease. You have to engage with people who have it.

Most pharma and research institutions do not do it well. There is still much improvement in the user experience on the scientific side. Create a seamless end to end experience through engagement. Engagement needs trust. Trust needs transparency. 

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Storing medical information below the skin’s surface MIT engineers have developed a way to store medical information under the skin, using a quantum dot dye that is delivered, along with a vaccine, by a microneedle patch. The dye, which is invisible to the naked eye, can be read later using a specially adapted smartphone.

Every year, a lack of vaccination leads to about 1.5 million preventable deaths, primarily in developing nations. One factor that makes vaccination campaigns in those nations more difficult is that there is little infrastructure for storing medical records, so there’s often no easy way to determine who needs a particular vaccine.

MIT researchers have now developed a novel way to record a patient’s vaccination history: storing the data in a pattern of dye, invisible to the naked eye, that is delivered under the skin at the same time as the vaccine.

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“In areas where paper vaccination cards are often lost or do not exist at all, and electronic databases are unheard of, this technology could enable the rapid and anonymous detection of patient vaccination history to ensure that every child is vaccinated,” says Kevin McHugh, a former MIT postdoc who is now an assistant professor of bioengineering at Rice University.

The researchers showed that their new dye, which consists of nanocrystals called quantum dots, can remain for at least five years under the skin, where it emits near-infrared light that can be de-

tected by a specially equipped smartphone.

McHugh and former visiting scientist Lihong Jing are the lead authors of the study, which appears today in Science Translational Medicine. Ana Jaklenec, a research scientist at MIT’s Koch Institute for Integrative Cancer Research, and Robert Langer, the David H. Koch Institute Professor at MIT, are the senior authors of the paper.

An invisible record Several years ago, the MIT team set out to devise a method for recording vaccination information in a way that doesn’t require a centralized database or other infrastructure. Many vaccines, such as the vaccine for measles, mumps, and rubella (MMR), require multiple doses spaced out at certain intervals; without accurate records, children may not receive all of the necessary doses.

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» Can the medical record of the future be stored within the patient’ s skin in a minimally invasive way.« “In order to be protected against most pathogens, one needs multiple vaccinations,” Jaklenec says. “In some areas in the developing world, it can be very challenging to do this, as there is a lack of data about who has been vaccinated and whether they need additional shots or not.”

To create an “on-patient,” decentralized medical record, the researchers developed a new type of copper-based quantum dots, which emit light in the near-infrared spectrum. The dots are only about 4 nanometers in diameter, but they are encapsulated in biocompatible microparticles that form spheres about 20 microns in diameter. This encapsulation allows the dye to remain in place, under the skin, after being injected.

The researchers designed their dye to be delivered by a microneedle patch rather than a traditional syringe and needle.

Such patches are now being developed to deliver vaccines for measles, rubella, and other diseases, and the researchers showed that their dye could be easily incorporated into these patches.

The microneedles used in this study are made from a mixture of dissolvable Specialized dye, delivered along with a vaccine, could enable “on-patient” storage of vaccination history (photo credit: Second Bay Studios).

sugar and a polymer called PVA, as well as the quantum-dot dye and the vaccine.

When the patch is applied to the skin, the microneedles, which are 1.5 millimeters long, partially dissolve, releasing their payload within about two minutes.

By selectively loading microparticles into microneedles, the patches deliver a pattern in the skin that is invisible to the naked eye but can be scanned with a smartphone that has the infrared filter removed. The patch can be customized to imprint different patterns that correspond to the type of vaccine delivered.

“It’s possible someday that this ‘invisible’approach could create new possibilities for data storage, biosensing, and vaccine applications that could improve how medical care is provided, particularly in the developing world,” Langer says.

Effective immunization Tests using human cadaver skin showed that the quantum-dot patterns could be detected by smartphone cameras after up to five years of simulated sun exposure.

The researchers also tested this vaccination strategy in rats, using microneedle patches that delivered the quantum dots along with a polio vaccine. They found that those rats generated an immune re-

sponse similar to the response of rats that received a traditional injected polio vaccine.

“This study confirmed that incorporating the vaccine with the dye in the microneedle patches did not affect the efficacy of the vaccine or our ability to detect the dye,” Jaklenec says.

The researchers now plan to survey health care workers in developing nations in Africa to get input on the best way to implement this type of vaccination record keeping. They are also working on expanding the amount of data that can be encoded in a single pattern, allowing them to include information such as the date of vaccine administration and the lot number of the vaccine batch.

The researchers believe the quantum dots are safe to use in this way because they are encapsulated in a biocompatible polymer, but they plan to do further safety studies before testing them in patients.

“Storage, access, and control of medical records is an important topic with many possible approaches,” says Mark Prausnitz, chair of chemical and biomolecular engineering at Georgia Tech, who was not involved in the research. “This study presents a novel approach where the medical record is stored and controlled by the patient within the patient’s skin in a minimally invasive and elegant way.”

The research was funded by the Bill and Melinda Gates Foundation and the Koch Institute Support (core) Grant from the National Cancer Institute.  Source: MIT

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GDPR during the crisis Does the greater good, which here is the fight against coronavirus, justify the circumvention of personal data protection rules? Is it appropriate to refer to the GDPR, hindering at the same time initiatives aimed at the health of an individual and society? During the COVID-19 pandemic, balancing the “public good� and privacy requires a broader view of law adopted under completely different conditions than those which we are currently experiencing.

the rights of the individual. Thus, many countries quickly introduced compulsory temperature checks in public places, applications used for controlling purposes, compulsory home quarantine, or the collection of data on the state of health and location in order to determine the risk of infecting others with coronavirus. Many of them raise concerns about fraud and the limitation of privacy rights, and may be taken to court.

Data processing in line with social good There is no denying that during such a crisis as the COVID-19 pandemic, many tools are created in a way that can raise many concerns in the light of the GDPR

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provisions in force since May 2018. The government institutions use the excuse of a higher-order need such as health and a principle of social good that is above

Does the end, namely health, justify the means interpreted as initiatives undertaken in order to fight the COVID-19 pandemic? Yes and no. The statement of

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Wojciech Wiewiórkowski, the European Data Protection Supervisor (EDPS), issued in connection with COVID-19, includes several operating guidelines to follow in the current crisis. As a matter of fact, today many organizations are forced to make decisions quickly, and they have no time to carry out legal analyses or refine the solutions in order to ensure privacy, which usually take months.

At the beginning of the statement we read that, although the processing of data entails high responsibility, there is also responsibility for not using tools that could help in the fight against the pandemic. In simple terms, the protection of personal data should not be an argument blocking the implementation of solutions that can save human lives in a critical situation.

The GDPR explicitly states that the processing of personal data should be designed in such a way as “to serve humanity” and that the “right to protect personal data is not an absolute right” and it should be “considered in relation to its function in society and, therefore, it has to be balanced with other fundamental rights in accordance with the principle of proportionality.” The processing of personal data – even sensitive health data – is legitimate in those cases when it is necessary due to “substantial public interest”, on the basis of the European Union or Member State law, in proportion to the intended purpose. The European Data Protection Supervisor points out that this is not a creative interpretation of the law or its bending, but a quote from the GDPR text.

The GDPR also allows the processing of sensitive data when it is necessary due to public interest in the scope of public health. An example is the protection against serious cross-border threats to health, which the coronavirus pandemic has proved to be.

There are also calls for the suspension of the Data Protection Act or its amendment in the light of the current crisis.

However, the GDPR is not an obstacle to such actions or an excuse (“we are not effective because we are constrained by law”).

“Even if we consider that a non-typical manner of data processing would interfere with the right to privacy and data protection, it may still be necessary in exceptional circumstances, such as that in which we have all been living over the past few weeks,” emphasizes Wojciech Wiewiórkowski. He points out that the

objective of the European Data Protection Supervisor is to ensure that all measures taken at the European and national level, concerning non-standard solutions in the scope of the use of data during the COVID-19 pandemic, are temporary (discontinued when the threat is over), limited (precise determination of the purpose and people having access to data) and purposeful (determination of the use of data collected and processed, but also deletion of these data after the return to normality).

Disputable technologies of population tracking The EDPS also mentions that the GDPR does not prevent the processing of personal data when health care authorities consider it necessary to fight the pandemic. In the statement, there is also the issue of applications monitoring the movement of smartphone users, due to which it is possible to detect contact with a person with confirmed coronavirus infection and thus to send a warning. According to the EDPS, the use of a temporary transmission identifier and Bluetooth technology to track contacts appear to be a form that allows the preservation of privacy and the protection of personal data. However, people working on technological tools to fight the pandemic should design them in line with the principle of privacy at the design phase (privacy by design).

Balance between measures and objectives Despite these indications, the use of arguments based on “public interest” may potentially lead to lodging of appeals against solutions or decisions which, at first sight, seem to be in contradiction with the GDPR. Therefore, choosing the balance between measures and objec-

tives should be the general rule. At this point, it is worth quoting the President of the Court of Justice, judge Koen Lenaerts, who stated that the law “limits the authorities in exercising their powers, requiring the preservation of balance between the measures applied and the purposes intended (or the results achieved)”.

In 2016, the European data protection authorities developed a list of requirements concerning supervisory mechanisms that interfere with privacy and data protection law. The subsequent judgments of the Court of Justice of the European Union confirmed the reasoning used by the data protection authorities and, as a result, four important pillars of accepted actions were identified. They are as follows: − the requirement that the processing should be based on clear, precise and accessible rules; − demonstration of the necessity and proportionality with regard to the legitimate objectives pursued; − existence of an independent oversight mechanism as well as − availability of effective remedies to the individual.

Personal data may be processed exclusively for specified legitimate purposes.

The guidelines published by the European Data Protection Supervisor clearly show that the COVID-19 pandemic cannot lead to the circumvention of the currently applicable law, but at the same time the applicable law should not hamper initiatives important from the point of view of social interest, in this case – health. The right to the protection of personal data is not an absolute right and the processing of personal data must serve the people. The most important thing is the balance between the measures and objectives, as well as the transparency of the whole process. 

» GDPR does not prevent the processing of personal data when health care authorities consider it necessary to fight the pandemic.«

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How to prepare medical workforce for digital health?

Digital transformation is influencing the competences of doctors and nurses, the way of communication with the patient and how we care for our own health. How are the roles and responsibilities of medical staff changing? What kind of new skills will be required in health care in the age of digitisation?

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A completely new health care model All stakeholders in the health sector agree that health care must evolve towards the patient-centric model focusing on a patient’s individual needs, respecting the social and economic ecosystem. Once, the treatment process was much easier. Whenever the patient felt

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ill, he or she went to the doctor and received a prescription or referral for further treatment. Nowadays, this model is more complex, and it evolves towards prevention and continuous patient care.

Access to new options of digital health care helps to break with the traditional care model. Today, the patient has access to such information and data that a few years ago could only be obtained during a doctor’s interview.

Besides, new technologies based on artificial intelligence, the Internet of Things, telemedicine and mobile solutions enter the market every day. New sources of information such as genetic information and wearable medical devices, which have to be considered in diagnosis and therapy, are available. The task of ensuring that the workforce has the appropriate digital skills is essential if any kind of successful digital transformation is to be seen. Digitalisation in health care will also help to deal with the problem of the shortage of medical staff. According to calculations made by Health Education England, the National Health Service will need 190,000 more clinicians by 2027 unless demand is reduced through better productivity and service transformation. It is impossible to obtain such a workforce either from the field of education and training or recruitment of a trained workforce from elsewhere.

Investing in a staff’s skills will be crucial for the full benefit from the digitalisation of health care. IT tools, methods of joint decision making, skilful collaboration in therapeutic teams and online communication with patients all need to be implemented to traditional medicine.

However, the vast majority of health care staff are not ready for it. Medical informatics constitutes a small part of college classes. It is challenging to find time to acquire knowledge in the field of digital health during working hours. A considerable change in the curriculum is needed so that doctors and nurses entering the labour market have not only excellent medical preparation but can also integrate their new skills into current practice. Across the field, health care staff still need to improve their skills in the era of new technologies. The problem was recognised by the National Health Service in the United Kingdom by establishing the NHS Digital Academy. Its goal is to train 300 Chief Clinical Information Officers. These leaders are expected to develop a strong understanding of digital technology and increase their knowledge of using it to improve clinical decision making, the quality of patient care as well as a high standard in terms of the outcome. The resulting qualification is to ensure that they are ready to set a strategy for digital innovation.

Staff training addresses the concerns

Applying technologies such as digital stethoscopes or algorithms for ECG signal analysis will cause some traditional methods including, for example, auscultation of the patient or interpretation of ECG results, to be no longer practised.

Artificial intelligence can improve efficiency and precision in health care. Consequently, this may lead to an increase in the other competencies such as empathy, personal contact with the sick, communication and care. Medical algorithms will be used on a daily basis. The feeling of anxiety will go away as soon as doctors learn how to use the full potential of AI. Artificial intelligence can surely facilitate routine activities, allowing doctors to focus on contact with the patient.

Thanks to the integrated data system, the GP, being the only doctor who treats the patient, now becomes a part of the team.

The specialist physician will be involved only when there is a risk of a disease occurring after the analysis of the data by the algorithms. However, cooperation with a patient who will gain the status

The best results of implementing innovations can come from the successful integration within a health care organisation rather than the adaptation of new solutions. The technology is completely pointless until the staff overcome the fear of innovation. There are many key reasons why people resist change. They result from psychological resistance to making a change in particular situations and are frequently connected with technology-related negative experiences. They are of no exception, bearing in mind the difficult process of the implementation of electronic medical records which, at the initial stage of development had, and sometimes still has, an unfriendly user interface. At the time of adopting digitalisation, medical staff can still experience the frustration that comes from the feeling that their time is wasted on keyboard tapping. Such internal resistance can be overcome easily by educating people before new technologies are acquired by clinical practice.

The Machine – your new teammate

of a partner in the process of treatment and prevention of the disease might be an even greater challenge. The data collected from patients at home will become a more valid source of information which doctors must learn to include. The interpretation of health needs in a wider family, social, environmental and professional context is needed.

In addition to improving the skills of clinicians, an area for completely new professions in health care needs to be found, such as clinical data analysts, medical software engineers, digital medicine specialists or digital transformation and data management directors. However, the change has to start with the leaders, mainly hospital and clinic directors.

They make organisational and management decisions and have the greatest impact on the vision, digital development and investment of a medical entity.

New professional competencies will be required, such as medical data processing and privacy protection. Telemedicine, a patient-friendly solution, is an excellent example of doing away with the common false belief that the patient receives the best diagnosis during the medical interview process. Medical staff, including nurses, need to know how to conduct a remote interview in the best way to recognise the earliest symptoms of a disease. A typical working day of a GP will be divided into clinic visits and computer shifts. One day telemedicine will become the standard in health care – provided trust in medical technology continues to grow.

Mathematical formula decides about the quality of treatment The implementation of electronic medical records and the digitisation of all medical and administrative data mean one more change. From now on, using intuition in management of quality and effects of treatment will become more precise and under better control. However, it raises new implications. We will face the challenge of developing care models incorporating elements of ethics and social justice. The more accurately we can measure the results and control the processes, the more necessary the standards of conduct that currently do not exist will be. It can be illustrated with the examples of the relationship between the improvement of health, survival outcome and economic aspect of therapy, or the need of implantation of a hip replacement in relation to the quality of

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life, costs of further rehabilitation, care and expectations of the patient. Today medical care prices include the costs of technology and labour. Frequently, side effects and implications for the rest of the patient’s life as well as the need for related medical and social services are not taken into account. Accordingly, health economics will intensively develop, examining the relation of care costs to treatment results, quality and effects from the perspective of each patient individually.

Because of the access to new data, a feefor-service reimbursement model which

promotes the quantity of services will be changed into value-based care which focuses on the quality of care. The fact that value-based reimbursements are calculated by using numerous measures of quality and the results of the treatment and prevention means a radical transformation of health care.

Under the new models, providers are incentivised to use new ways of diagnosis, focus on prevention rather than treatment, engage patients and use data analytics. They are supposed to provide the patient with coordinated and individu-

al care. All these new methods offer a unique opportunity to transform health care, which is never going to be similar to the one which today’s medicine and nursing students are familiar with. This discrepancy means that new staff will enter the labour market with the fear of innovation, and the potential of the latest technologies will be wasted. This change is not only about learning how to use a program to create electronic medical records. It is so deep that a transformation of education in medicine is required. 

» The technology is completely pointless until the staff overcome the fear of innovation.«

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Using AI To Predict Breast Cancer And Personalize Care A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep-learning model that can predict from a mammogram if a patient is likely to develop breast cancer as much as five years in the future. Trained on mammograms and known outcomes from over 60,000 MGH patients, the model learned the subtle patterns in breast tissue that are precursors to malignant tumors.

MIT Professor Regina Barzilay, herself a breast cancer survivor, says that the hope is for systems like these to enable doctors to customize screening and prevention programs at the individual level, making late diagnosis a relic of the past.

Although mammography has been shown to reduce breast cancer mortality, there is continued debate on how often to screen and when to start. While the American Cancer Society recommends annual screening starting at age 45, the

U.S. Preventative Task Force recommends screening every two years starting at age 50.

“Rather than taking a one-size-fitsall approach, we can personalize screening around a woman’s risk of developing cancer,” says Barzilay, senior author of a new paper about the project out today in Radiology. “For example, a doctor might recommend that one group of women get a mammogram every other year, while another higher-risk group might get supplemental MRI screening.” Barzilay is the Delta Electronics Professor at CSAIL and the Department of Electrical Engineering and Computer Science at MIT and a member of the Koch Institute for Integrative Cancer Research at MIT.

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The team’s model was significantly better at predicting risk than existing approaches: It accurately placed 31 percent of all cancer patients in its highestrisk category, compared to only 18 percent for traditional models.

Harvard Professor Constance Lehman says that there’s previously been minimal support in the medical community for screening strategies that are riskbased rather than age-based.

“This is because before we did not have accurate risk assessment tools that worked for individual women,” says Lehman, a professor of radiology at Harvard Medical School and division chief of breast imaging at MGH. “Our work is the first to show that it’s possible.”

Barzilay and Lehman co-wrote the paper with lead author Adam Yala, a CSAIL PhD student. Other MIT co-authors include PhD student Tal Schuster and former master’s student Tally Portnoi.

How it works Since the first breast-cancer risk model from 1989, development has largely been driven by human knowledge and intuition of what major risk factors might be, such as age, family history of breast and ovarian cancer, hormonal and reproductive factors, and breast density.

However, most of these markers are only weakly correlated with breast cancer. As a result, such models still aren’t very accurate at the individual level, and

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The model detected » ������������������� patterns too subtle for the human eye to detect.« many organizations continue to feel riskbased screening programs are not possible, given those limitations.

Rather than manually identifying the patterns in a mammogram that drive future cancer, the MIT/MGH team trained a deep-learning model to deduce the patterns directly from the data. Using information from more than 90,000 mammograms, the model detected patterns too subtle for the human eye to detect.

“Since the 1960s radiologists have noticed that women have unique and widely variable patterns of breast tissue visible on the mammogram,” says Lehman.

“These patterns can represent the influence of genetics, hormones, pregnancy, lactation, diet, weight loss, and weight gain. We can now leverage this detailed information to be more precise in our risk assessment at the individual level.”

Making cancer detection more equitable The project also aims to make risk assessment more accurate for racial minorities, in particular. Many early mod-

els were developed on white populations, and were much less accurate for other races. The MIT/MGH model, meanwhile, is equally accurate for white and black women. This is especially important given that black women have been shown to be 42 percent more likely to die from breast cancer due to a wide range of factors that may include differences in detection and access to health care.

“It’s particularly striking that the model performs equally as well for white and black people, which has not been the case with prior tools,” says Allison Kurian, an associate professor of medicine and health research/policy at Stanford University School of Medicine. “If validated and made available for widespread use, this could really improve on our current strategies to estimate risk.”

Barzilay says their system could also one day enable doctors to use mammograms to see if patients are at a greater risk for other health problems, like cardiovascular disease or other cancers. The researchers are eager to apply the models to other diseases and ailments, and especially those with less effective risk models, like pancreatic cancer.

“Our goal is to make these advancements a part of the standard of care,” says Yala. “By predicting who will develop cancer in the future, we can hopefully save lives and catch cancer before symptoms ever arise.”  Source: MIT – Massachusetts Institute of Technology

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This Robot Knows How To Communicate To Support Patients With Chronic Illness Challenging treatment plans Catalia Health’s software incorporates expertise in psychology, artificial intelligence, and medical treatment plans to help patients manage their chronic conditions. The result is a sophisticated robot companion that uses daily conversations to give patients tips, medication reminders, and information on their condition while relaying relevant data to care providers. The information exchange can also take place on patients’ mobile phones.

Heart failure patients first brought Mabu into their homes about a year and a half ago as part of a partnership with the health care provider Kaiser Permanente, who pays for the service. Since then, Catalia Health has also partnered with health care systems and pharmaceutical companies to help patients dealing with conditions, including rheumatoid arthritis and kidney cancer.

Treatment plans for chronic diseases can be challenging for patients to manage consistently, and many people don’t follow them as prescribed. Mabu’s daily conversations help not only patients but also human, as they make treatment decisions using data collected by their robot counterpart.

Robotics for change Catalia Health uses artificial intelligence to help Mabu learn about each patient through daily conversations, which vary in length depending on the patient’s answers.

The smart Mabu robot, made by startup Catalia Health, help patients manage chronic diseases at home. The most innovative part of the solution lies behind the robot’s large blue eyes and is based on AI mixed with psychological sciences.

“A lot of conversations start with ‘How are you feeling?’ similar to what a doctor or nurse might ask,” the founder and CEO of Catalia Health – Cory Kidd explains. “From there, it might go off in many directions. There are a few things doctors or nurses would ask if they could talk to these patients every day.”

For example, Mabu would ask heart failure patients how they feel if they have shortness of breath, and about their weight.

“Based on patients’ answers, Mabu might say ‘You might want to call your doctor,’ or ‘I’ll send them this information,’ or ‘Let’s check in tomorrow,’” Kidd says.

Last year, Catalia Health announced a collaboration with the American Heart Association that has allowed Mabu to deliver the association’s guidelines for patients living with heart failure.

“A patient might say ‘I’m feeling terrible today’ and Mabu might ask ‘Is it one of these symptoms a lot of people with your condition deal with?’ We’re trying to get down to whether it’s the disease or the drug. When that happens, we

do two things: Mabu has a lot of information about problems a patient might be dealing with, so she’s able to give quick feedback. Simultaneously, she’s sending that information to a clinician — a doctor, nurse, or pharmacists — whoever’s providing care.”

“In a clinical setting, if we talk about a doctor with good bedside manner, we don’t mean that he or she has more clinical knowledge than the next person, we simply mean they’re better at connecting with patients,” Kidd says. “I’ve looked at the psychology behind that – what does it mean to be able to do that? – and turned that into the algorithms we use to help create conversations with patients.”

Many studies have found that communicating with someone in person, as opposed to over the phone or online, makes that person appear more trustworthy, engaging, and likeable.

“What I found was when we used an interactive robot that you could look in the eye and share the same physical space with, you got the same psychological effects as face-to-face interaction,” Kidd says. 

Source: MIT

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Photo: Facebook

Facebook has launched new healthcare features Facebook, a leading social network with 2,41 billion active users worldwide, is taking the next step towards the healthcare market. Last month, Mark Zuckerberg‘s company introduced the “Preventive Health” tool that connects people to health resources, allowing setting reminders to schedule tests, mark when tests are completed, and more.

Likes for health Many of today’s leading health threats aren’t ones that science or medicine can solve alone. Changing trends in communication and unequal access to care mean there is a need for new solutions and partnerships to overcome these global challenges.

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For example, blood shortages. Every few seconds, someone in the world needs blood. But people often aren’t aware of shortages and don’t know where to donate. To address this, Facebook launched a feature in the US, India, Brazil, Bangladesh and Pakistan that makes it easy to sign up as a donor on Facebook and get

notified when nearby blood banks are in need. So far, more than 50 million people have signed up to donate. According to the company’s blood bank partners in India and Brazil, 20% of voluntary, walkin blood donors are coming from Facebook.

Another area Facebook is exploring is preventive health. Tens of millions of people in the US are missing out on recommended preventive care, according to the Centers for Disease Control and Prevention. To address the problem, Facebook is working with US health organisations to offer a new Preventive Health tool that connects people to health resources and checkup reminders. The initial focus is on the top two leading caus-

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es of death in the US: heart disease and cancer, as well as the flu, a seasonal illness that affects millions each year. The resources available in the tool are provided by the American Cancer Society, the American College of Cardiology, the American Heart Association and the Centers for Disease Control and Prevention – organisations recognised for their education and expertise in these areas.

How Preventive Health Works In the US, people can search for Preventive Health in the Facebook mobile app and find out which checkups, such as cholesterol tests or mammograms, are recommended by these health organisations based on the age and sex they provide. Reminders for flu shots will also appear at the appropriate time of year. The tool allows people to mark when tests are completed and set reminders to schedule future tests. People can also learn more about each checkup and find affordable places to receive care.

Most of the preventive measures recommended by the cooperating health organisations, such as blood pressure tests, are free of charge with insurance coverage. To help a considerable group of people without insurance, Preventive Health offers a way to find Federally Qualified Health Centers near them. These centres are located in underserved areas and provide care to everyone, regardless of their ability to pay. People can also use Preventive Health to find convenient locations that offer flu shots, such as grocery stores, pharmacies and urgent care clinics.

“Heart disease is the number one killer of men and women around the world, and in many cases, it is 100% preventable. By incorporating prevention reminders into platforms people are accessing every day, we’re giving people the tools they need to be proactive about their heart health.” – says Richard Kovacs, MD, President of the American College of Cardiology.

Privacy issues

» Besides Preventive Health tool, Facebook also introduced functions allowing to find a blood donor.«

“We’ve contributed our content and resources to the Facebook Preventive Health tool to empower Americans to take the first step to know about and take action to lower blood pressure, blood sugar and cholesterol, each of which has been shown to increase the chance of a longer, healthier life and reduce the likelihood of a heart attack or stroke.” – emphasises Eduardo Sanchez, MD, Chief Medical Officer for Prevention at the American Heart Association.

Facebook claims, that although Preventive Health allows to set reminders for future checkups and mark them as done, it doesn’t provide the company, or the collaborating health organisations, access to user’s test results. Personal information about activity in Preventive Health is not shared with third parties, such as health organisations or insurance companies, so it can’t be used for purposes like insurance eligibility. Facebook also declares that it doesn’t show ads based on the information provided in the Preventive Health section.

Preventive Health and Blood Donations are just two of the healthcare-related tools Facebook offers. Other examples are Disease Prevention Maps that aim to help nonprofits and universities working in public health get ahead of disease outbreaks, plan vaccination campaigns and reach vulnerable communities more effectively.

In the press release issued on October 28th, the company also mentions that many people turn to health support groups on Facebook after being diagnosed with or while managing a health condition. Patients can there connect with others who have had similar health experiences and find information and support.  Source: Facebook (Newsroom)

One of the main reasons people don’t get screened for cancer is that they don’t realise their own risk. Facebook wants to change it (photo: Facebook).

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Help me, robot!

They help the disabled, entertain patients in hospitals, and support therapies for autistic children. But from robots in healthcare, we expect much more than just manual help. We want human touch. Robots augmented with social abilities should react to our emotions and create authentic relationships so we can trust them. What care do we want in the future? And how can we involve robots to make health care smarter?

A patient with emotional baggage A small, white robot with big eyes enters the patient’s room. It asks the patient about their mood, provides sufficient information on their condition, carries out a medical interview and checks blood pressure. It analyzes all information through the Artificial Intelligence system. A diagnosis is ready in a few seconds. The pa-

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tient receives a prescription, an exhaustive list of recommendations and a bill.

It seems that an ideal patient visit, during which perfect service and medicine based on facts, i.e. data, should look like this. But an ideal doctor-robot is not able to satisfy the patient’s emotional needs, which are equally important as treating a disease. It does not show emotions and

empathy, and it does not accept any comments. It follows the procedure authoritatively.

Although robots as machines perform well on a production line, interaction, communication, support and empathy are more significant when it comes to medical care. In short, it is more important to meet social needs that are one of the most significant needs on the ladder of all needs. At the same time, they are the most difficult to satisfy, as they are intangible and elusive, and they vary among people. We realize social interactions differently, just like we alleviate hunger through food adjusted to our taste and culture (as long as we can afford this luxury).Everything depends on the living environment, upbringing, personality and priorities. While experiencing disease, all emotions which are present in a human being are brought to a boil. In this

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want them to be able to talk with us and to treat us individually as well as arouse various emotions. The number of robots which are able to do this is increasing. Paro, which resembles a seal, is a favorite among patients suffering from Alzheimer’s disease and dementia. There is an authentic relationship between Paro and patients, which is the same as the relationship between a human being and a real animal, e.g. a dog or a cat. There are other examples similar to this. Moxi, which was designed to help nurses, is an attraction among patients who treat it as something more than a soulless machine.

Also, Pepper is one of the most popular robots, which is able to work at the hospital reception and help in the therapy of autistic children.

The issue of the social skills of robots is the subject of many elaborations. In the PubMed scientific library, upon entering the term ‘social robots’, we obtain 1,400 abstracts. A majority of them examine the potential of their usage in care of people with special needs, such as patients with neurodegenerative diseases or children suffering various diseases. One can also find there critical texts, which draw attention to the dehumanization of social contacts in health care, which is the beginning of the epoch of post-humanism.

Binary healthcare full of empathy

kind of situation, we look for understanding and help, and not only a prescription or treatment.

Social robots are getting better and better at recognizing and reacting to our emotions. Voice assistant Alexa is already capable of intoning utterances, expressing feelings and communicating with a human being in a better way. So far, these are only two emotions, i.e. excitement and disappointment. In a few years, Artificial Intelligence will learn more natural language and voice modulation, so that we will not be able to tell the difference between a chat with a friend and a chat with algorithms. Cameras built into robots, similarly to human eyes, carefully observe and analyze expressed feelings so that they can adjust to them and react individually.

Robots are able to gain our trust only by behaving like a human being. We

Technology is never neutral. The development of robots with social competences will also have far-reaching consequences, which will affect society, social relationships and the definition of a human being. Intelligent machines that are able to recognize our feelings more precisely than another person seem terrifying but also fascinating. The Japanese invention called Gatebox, which is a virtual friend in the form of a hologram, may serve as a good example. This personalized avatar remembers things about its owner better than the best partner. It is curious about how we spent our day, it asks us how we are, and it is happy when we come back from work. Some people consider it to be a sad invention, which demonstrates the dissolution of social bonds. However, for some people, it as an excellent innovation, which helps lonely people, who are not able to establish real connections, have a new companion.

However, today is not the right time to ask questions about what we expect from health care. When the question of what

is important for us as people is asked, I always ask what we mean by ‘we’? Do we have a moral right to judge a lonely, bedridden person, whose only friend and assistant is a robot, because their family does not have time to visit them or prefers to spend the weekend outside the city? Is a companion of an ill child in the hospital in the form of the Huggable robot created by MIT a fraud or support in difficult moments? Everyone values different things. Everything depends on many factors and perspective. Therefore, it is difficult to generalize questions about robotics in healthcare. It is wiser to consider what we want to avoid.

One thing we know for sure is that robots used in health care should not cause harm. Similarly to doctors, in accordance with the ethical principle of Hippocrates primum non nocere, although in a different context. Therefore, robots should not collect information for purposes other than the ones concerning care over patients. They cannot act as a spy standing next to the patient’s bed. Algorithms controlling these type of solutions should be created transparently and ethically. Robots should remain emotionally neutral so as not to manipulate feelings or raise any hope that they can do more than in reality. However, there is a fine line.

Does a machine which holds the patient’s hand or hugs them violate ethical standards? At the moment, robots should not make independent and un-programmed decisions. The reason is simple: the real world is too complex for the perception of an average robot, and even the best algorithm of Artificial Intelligence is not able to analyze a given situation as precisely as the human mind, paying attention to the smallest details and nuances.

Robots should also be neutral in terms of their attitude towards patients (lack of prejudices and partiality) and collect only such data which patients want them to collect.

Synthetic empathy or a virtual lie?

Bart de Witte, the founder of Digital Health Academy, predicts that empathy generated by the Artificial Intelligence systems will become one of the leading trends in few years. People establish relationships based on trust with machines when they understand our emotions and are able to react adequately. Today’s Artificial Intelligence is very limited. At present, many services use other psychological mechanisms in order to increase the use of a given technology. Social me-

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dia, which is addictive because they it is based on the simple principles of the psychology of behaviors and the stimulation of a reward center in the brain, are a good example. Therefore, each ‘like’ generates the feeling of happiness, acceptance and strengthens self-confidence. But in a few years, Artificial Intelligence will enter virtual reality by imitating the behavior of a human being. Robots which act as doctors during telemedical consultations, which do not require a physical meeting with a doctor, can be equipped with synthetic empathy. We will require such robots to establish ideal bonds with us, not to criticize us and to help us feel

better. Will it then turn out that a conversation with another human being, during which we often face criticism or another point of view, is too toxic?

“Technology can always be threat or a chance for humanity. It is us – people – who define the purpose of technology.

Currently, many of the business models digital companies use are based on digital services that compete for users’ limited attention. The modern economy increasingly revolves around the human attention span and how digital services capture that attention,” Bart de Witte, the Founder of the HIPPO AI Foundation and Digital Health Academy says.

“The addiction to social validation and bursts of “likes”, for example is destroying our real life attention spans.

Our brains are drawn to outrage and angry tweets, replacing democratic debate. During the last few years, access to technology’s godlike powers has increased dramatically, while the ancient, Paleolithic impulses of our brains have remained the same. What happens when we add the power of love in the box of god-like technologies and give it to 25year-old nerds, backed up by venture capital? Algorithms are already able to read our emotions much better than we humans do, but as they do not possess con-

» Robots are new labor force which is meant to fill the increasing gap related to the lack of doctors and nurses.«

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sciousness, they won’t have the ability to feel. Empathic behavior will be based on synthetic empathy. Giving robots emotions has been an abiding theme, in literature and pop-culture. When the Tin Man in the Wizard of Oz wanted a heart, he was told that a heart is not judged by how much you love, but by how much you are loved by others. Synthetic empathy will be the most powerful tool developers have ever had access to. Before we all fall in love and enslave ourselves, we need a more open debate on how far we need to regulate this,” Bart de Witte points out.

At this point the question whether we will shape technology or whether technology will shape us arises again. So far, this technology has been formulating a new world order in new technologies.

They are not as controversial as cloning or editing genes about which most countries have similar opinions, introducing legal barriers jointly and severally. The influence of digital innovations is far subtler, long-term and difficult to predict. Perhaps it is equally dangerous.

Experiment on patients It is not possible to clearly separate roles which should be fulfilled in healthcare by robots and by human beings. Medical care provided, for example, by a nurse is the mixture of technical and social tasks.

It is also difficult to think about target models, as technology is changing dynamically and tomorrow today’s ideas may be outdated.

The market does not stand still either.

It experiments with the growing number of the new models of robots implemented in pilot projects. A number of them were created by innovative startups the ambition of which is to develop a revolutionary technology and achieve market success, not to analyze values significant for patients. Most of these projects fail, but projects that pass the market test join other innovations and create the force changing current health care. A good example is the above-mentioned Moxi created by Diligent Robotics. The Zora Robot, which costs about $10,000, is one of the most popular humanoids. It is used in care centers, where it entertains patients, talks with them, dances, exercises, and makes patients laugh.

The evolution of machines-carers has significantly sped up along with the development of Artificial Intelligence and the so-called neural networks. Robots which accompany patients and people

who require special care may learn their needs, habits and gradually adjust their skills.

The GrowMeUp project implemented in the years 2015-2018, which was co-funded by the EU Horizon 2020 program, was one of the largest projects in this respect. The project’s objective was to develop a cheap robotic system which would be able to learn the needs and habits of the elderly, recognize their habits and grow up with them. In this way, robots would be able to compensate the deterioration of the senior’s cognitive abilities and support them in everyday life so that they are active, independent and socially engaged for a longer period of time. Robots would transfer the collected data and knowledge to a data cloud, so that they could learn from each other and create a combined virtual care network.

The project ended up with no success.

Robotics in healthcare is like walking on thin ice. The lack of regulations leads to the uncertainty. Moreover, such projects are rarely funded in public healthcare systems. Therefore, it is difficult to create a stable business model.

This poses the threat that only more affluent people will benefit from robots.

Interestingly, many experts believe that the situation may be completely different. If the mass production of robots results in the fact that they are cheaper labor force than people, poorer patients will be served by machines, and only richer patients will be served by a real human being.

In the middle of extreme scenarios Robots are new labor force which is meant to fill the increasing gap related to the lack of doctors and nurses. It will be relatively easy to teach machines how to take care of patients, change the sheets, bring meals to bed, measure body temperature and control the general health condition. Robots, whose competences will be reduced only to the foregoing skills, will remain only mechanical assistants. If they perform tasks carried out by human beings, we want them to look and behave like human beings. Therefore, such robots as Moxi or Pepper have human faces, big eyes and a broad smile.

Thanks to this, they do not frighten patients but make them feel more comfortable. However, nowadays, it is difficult to imagine that healthcare based on human beings could be exchanged for a model based on robots, where machines take over all of the competences belonging to

doctors and nurses, and take care of patients on their own; at least not at the moment, due to the lack of sufficient technical capabilities. When a few years ago I read about the Paro robot, which helps taking care of patients suffering Alzheimer’s disease, I was delighted by the idea. However, I was extremely disappointed when I held it in my hands. Mechanical, clumsy movements, monotonous murmurs and robotic eye movements turned out to be a very crude form of what real animals can do.

“We hold a tool in our hands, which can be an opportunity for but also a threat to healthcare. In order for technology to become the Trojan Horse introduced by companies providing new technologies for healthcare, we must get involved in its development, participate in research projects, discuss their application with patients, politicians and all stakeholders” – global health expert, professor Ilona Kickbusch, rightly claims. It will take years before robots learn such basics as smooth movement around hospital rooms or starting relatively neutral conversation with patients. Meanwhile, we have the time to focus on the development of the model of intelligent and comprehensive healthcare (‘smart care’ and ‘deep care’), where robots support personnel, assist them in mechanical and physical activities and leave the sphere of communication, empathy and emotions to human carers.

However, we know that human curiosity and willingness to experiment with Artificial Intelligence and technologies will still accelerate newer and newer models of robots imitating human behavior. And there will be nothing wrong with this, as long as they help patients instead of harming them.  Author: this article is based on knowledge I gained during the “Talk to Me – Social Robots in Health Care” conference organized by the Careum Foundation, 13-14 February. Special thanks for inspiring visions and experiences to: prof. Ilona Kickbusch, Jan Ehlers, prof. dr. Oliver Bendel, Kathrin Janowski, prof. dr. Effy Vayena, prof. dr. Marc Oliver Korn, prof. dr. Detlef Günther, Bart de Witte.

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8 necessary steps towards digital transformation Successful digitalization in healthcare depends mostly on change management skills. Even the best technology is not enough. One also must know how to manage change, motivate employees to embrace innovation, and permanently introduce it to the organizational culture.

Hospital and clinic managers often wonder how to implement a new IT system or functionality successfully. In this case, a common mistake is to focus one’s efforts solely on choosing the best technology – the perfect solution will fail if people refuse to accept it. After all, digital transformation is much more than just

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purchasing and installing a new IT system. It is a far-reaching interference in all established processes as well as employee habits and expectations.

Yet, even if the employees – doctors, nurses, administrative staff – are unsatisfied with the current situation and expect change, in reality, they do not want to

modify the way they work. It’s true that “everybody wants a change, but nobody wants to change.” That’s why resistance to new solutions is often so strong that it effectively nullifies the positive effects of even the most crucial reforms.

When it comes to healthcare, one of the best-known organizational change processes that work well is the 8-step Kotter model. It comprises the following stages: Create a sense of urgency regarding the changes among the employees.

Employees need to know that the changes are essential and that there are specif-

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ic reasons behind them. For example, the argument for introducing an online registration app for patients could be that the information hotline is continuously blocked by patients calling to register for a visit, or that the survey results indicate that patients are dissatisfied with how registration by phone works. Such a sense of urgency drives motivation and further mobilizes the employees.

Form a coalition to support change consisting of employees who like modern technology or expect some inefficient processes to be modernized. They will be ambassadors of change in their own professional groups and will proceed to communicate it at all levels of the organizational structure, effectively lobbying for the project among other employees.

Develop a strategic vision and concrete initiatives. A perfect vision is emotionally attractive, strategically wise, and easy to communicate. It provides an idea of what success is and a sense of the direction that should be followed to attain it. For example: “implementing the clinical decision support system will make it easier for doctors to access the most up-to-date scientific knowledge, increasing the quality of our medical services as a result.”

» Digital transformation is much more than just purchasing and installing a new IT system.«

ly quickly. For example, when switching to electronic-only medical records, such evidence could be the result of patient opinion surveys, showing that patients are satisfied with the fact that the doctors can access their full medical history immediately.

Maintain the pace and intensity of your activities. If any problems arise, they should be resolved quickly to prevent them from turning into a source of chronic frustration and dissatisfaction.

Additionally, any improvement ideas

presented by the employees must be verified and the justified ones implemented.

For example, when alerts in the decision support system pop up too often, the system should be recalibrated, as otherwise, the doctors may ignore them.

Promote change until the new replaces the old. Change can only be considered successful once it has been fully absorbed into both the company culture and employee routine. Only when the new system or functionality becomes a standard part of the medical personnel’s daily work will the change become permanent.

In this regard, a well-planned digital transformation process results in a much higher chance of success than chaotic and disorganized actions. Although people do not like changes, by introducing them step by step, we can eventually win their trust and support. In the end, doctors, nurses, and the support staff all want their work to be effective and well-organized. Thus, the employees’ acceptance of new software introduced at their hospital or clinic is never a matter of chance, but rather a direct result of the approach chosen by their managers. 

Communicate the vision and gain support among the staff. Since we already have a well-designed project, it is time to act. Kotter suggests that a bold vision and a specific time, financial, and organizational plan are the foundations for building genuine staff involvement. At this stage, it is worth considering what arguments the plan’s opponents can put forth and modify it accordingly, up to the point where it will be challenging actually to challenge it.

Mobilize everyone to act with the help of change ambassadors. If we are implementing a notification system about the patients’ laboratory results being beyond the accepted norms, we must first thoroughly train both doctors and nurses on how to enter appropriate messages in the IT system.

Celebrate the visible benefits and achievements. Kotter claims that people quickly lose the patience and energy needed to act. Therefore, evidence of the benefits of the changes must appear fair-

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Demystifying Algorithms There has been a lot of hype and hope around algorithms recently. But what can they do and what can they not do? How do they function? Can they feel like humans? Kasia Barczewska, Head of Research and Development at Cardiomatics, takes us on an eye-opening journey through Artificial Intelligence, algorithms and machine learning.

How to explain to a child what an algorithm is and what AI is

An algorithm is a kind of recipe which describes how to solve a problem or how to achieve a goal. The goal is crucial – what exactly we want to do. If we’re going to bake a cake, a cake recipe is an algorithm which defines how to do this, starting from a list of ingredients that we need to have, ending with the temperature of the oven and the baking time. If

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we follow the algorithm, we will obtain a baked cake as a result. If we want to build a castle from blocks, an instruction how to do this is also an algorithm which lets us achieve this goal and get the same building that was on the picture on the box of blocks.

What is AI? A common term at a very high level of abstraction. It is the ability of computers to learn, generalise, use knowledge in practice and make decisions. Behind that popular term, there is just sta-

tistics and mathematical modelling. How can this be explained to a child? I would say: “AI is how my laptop can learn things like you do, my dear child. It learns much more slowly and needs much more examples than you do.”

What does the process of creating an algorithm look like?

So you ask about the algorithm of an algorithm. Essentially, it is a very creative process during which an algorithm designer is a child sitting with all the blocks in the world and is trying to figure out how to build a castle. The designer has to decide which blocks should be used and what steps should be taken to create that building. The effect of her or his work is both a beautiful castle and a book with instructions for others who would like to build it in the same way.

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» Algorithms are only as good as the data they use in the learning process.« Another example can be a Japanese origami master who wonders how to make a crane from a square piece of card.

The effect of his efforts is an instruction for other origami hobbyists who can fold the card in the same way.

Generally, there are a few steps which are very important in this process. The algorithm designer has to: – Specify the goal. For example, “I want to build a castle/bake a cake”,” I want to create a crane”, “I want to detect atrial fibrillation.”

– Define the set of assumptions/tools that can be used: “I have 200 blocks with specific shapes and colours/eggs, floor and apples”, “I have one square piece of card”, “I have a database of 24-hour recordings from 100 patients.”

– Define exactly what the result of the algorithm should be: “I will build a Howl’s Moving Castle from blocks/ I will bake an apple pie”, “I will make an origami crane”, “The result would be 0 or 1 depending on whether or not atrial fibrillation was detected in a 24hour ECG signal.

– Do the research: how do others do this?

– List the steps that should be taken to achieve the goal. For example: writing down the recipe, noting pseudocode how the signal will proceed, which models will be used, which machine learning methods will be used to teach the models the proper statistics, which metrics will be applied in the evaluation process.

– Determine how you will do these steps. “I will build my castle in my room, listening to music/where is the baking tray?”, “I will fold the paper by hand”, “I will write code in Python, train models on a GPU and save the best one in an hdf5 file.”

– Evaluate/review the whole process.

“Oops, the castle collapsed”, “I have to change the project”, “I have forgotten to add sugar!” It’s a crane that looks like a penguin.”

– Test your model on new data and calculate the evaluation metrics. Analyse the new parameters and try to under-

stand what they mean. Compare with other models or with state-of-the-art methods.

When the algorithm is ready, you feed it with data, and what happens next?

The computer can start learning. Computers process bytes of data to find patterns and get statistical parameters in which their knowledge will be stored.

I can observe the learning process by looking at learning curves, relaxing with a coffee and waiting till it is finished.

Sometimes, the model is ready before the coffee machine has finished grinding the coffee. But, in some cases, gigabytes of data and complicated learning strategies make the learning process several days or weeks long.

When the model is ready, it must be evaluated on data that was not used in the training process. I have to assess if it does what it was designed to do. And, if it doesn’t, I have to review the whole learning process.

Do we know step-by-step how the algorithm makes conclusions or transforms data?

Yes, we know. Everything depends on our decisions: on the data that we prepared to feed it, on the model that we chose, and on the learning strategy that we decided to apply at the beginning of the process. The computer’s knowledge is stored in statistical parameters that we can visualise at every step of the learning process. Of course, the simpler the algorithms are, the easier it is. The more sophisticated the algorithms, the more complicated it gets.

What is machine learning?

These are the best practices of how to train your models to predict effectively in real cases. You can imagine machine learning like a set of tools that engineers have to teach a computer and to evaluate its learning process. These tools are used in several steps: at the beginning, to prepare the

material that will be used in learning, then to train and evaluate an algorithm. Among the “preprocessing tools”, an engineer has methods of data exploration, methods of data augmentation, a division of data into training and testing sets, selection methods to show/highlight only the most essential features in the data of the algorithm and facilitate the learning process. Then she or he can choose from a wide range of models or model architectures, which are the central part of the algorithm. Depending on the model, a proper learning strategy should be selected: What is going to be minimised or maximised by the algorithm during the learning process? How fast should an algorithm be learning? An engineer can use several different models and compare their results using evaluation metrics. The basis of all these tools is statistics and mathematics. All of them are stored in open source libraries and tutorials so that every engineer in the world can use them to train her/his model or to improve them.

When machines learn, do they multiply the mistakes in the data they use?

Algorithms are only as good as the data they use in the learning process. If there are systematic mistakes in the labelling of the data, the algorithm will learn them and will make the same mistakes in reality. That’s why it is essential, especially in medical applications, to consult the right labels (diagnosis) with many specialists and to gather a range of knowledge from training materials.

The problem of transparency is a problem with many levels of abstraction. At the higher level, people can personify “AI algorithms”, not knowing what they are, when they are simply a collection of statistical and mathematical models. At the lower level, the most popular algorithms are currently criticised for a lack of transparency because of millions of parameters which they have, and also for their entirely data-driven approach to learning, without the application of explicit rules or laws, which is an entirely different approach to learning than humans have. This criticism is good because it forces researchers to explore more and develop different methods.

Can AI do things without our control which could be dangerous for patients?

They are just tools in human hands. As long as they are used in good faith, they

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can support the doctor’s work. Of course, even in good faith, there is a place for human error: for example, in poorly prepared data given to an algorithm in training. Fortunately, in good practice, the final point of developing a model/an algorithm is used – an evaluation, which should be done very carefully. At this stage, we can assess if our method works as it should, and, if it doesn’t, we have to review the whole process.

How precise are algorithms? Can they forget things like people do?

The engineer’s role is to prepare the data accurately and to choose the best machine learning tools for the modelled phenomenon. If she/he forgets about something in the learning process, the data used in the training is unrepresentative of the phenomena, and the algorithm will not work well in reality. It will not forget, but it will not know that there are other distributions of that phenomena.

Algorithms model the knowledge only in the way they were shown in the learning process.

What will algorithms never be able to do?

To love, to have family, to meet friends, to be curious and have hobbies, to relax and drink coffee. But thanks to algorithms, humans will have more time for doing these lovely things.

How will algorithms change medicine?

Dramatically! They will give doctors the possibility to make a holistic diagnosis based on automatically processed data from different examinations. They will find relationships between patient data and show these relationships to doctors.

They will shorten the time between examination and diagnosis. They will give the doctor time to talk with the patient and to think about therapy. They will reduce waiting times for specialists. They will allow an increase in the number of detected cases of a given disease by making the fast analysis of long-term signals possible. They will facilitate examinations in places in the world where there is a lack of doctors.

How to prepare data for algorithms used in healthcare?

The preparation of data is a critical step,

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» Unlike algorithms, humans can generalise their knowledge in a much better way.« especially in medical applications, and must be done very carefully. A model will be as good as the data that was used to train it. Pairs of sample data with proper labels (proper diagnosis) are key to success. Ideally, they should be reviewed by several independent specialists. At this stage, a very close cooperation with doctors is required.

Can AI do something without our permission or control?

If the whole process of developing an algorithm was done well, and the algorithm was tested on the representative dataset, then we know exactly what to expect from this algorithm and how it will work in reality. If we did not pay enough attention to the testing, then different things may happen in a real application, and we will not understand why they happened.

If you ask me as an engineer if a well designed and tested AI algorithm can do something by itself without our permission or control, the answer is “no”. If we let it do things by itself, then yes. But then it is under our control because we expected this result.

Is it possible to build an AI system that will learn human feelings like empathy, compassion, sympathy, etc.?

To detect emotions, definitely yes. To feel emotions like humans? What does a “feeling in the context of a computer” mean?

Making decisions in a specific way, depending on the gathered data? Triggering chemical processes somewhere? Or simulating facial expressions? The question is: if we understand human emotions in

that way, could we describe them to the algorithm? Even if yes, these will only be mathematical models of emotions.

Can AI become more intelligent than people are?

What does it mean, ‘more intelligent’? There are different types of intelligence and different tasks in which we can compare humans and computers. And, of course, there are some cases in which engineers developed algorithms that surpassed humans. Such examples are old board games like “Go” and chess, in which algorithms can beat human masters. In the case of chess, the former chess master, Kasparov, admires the unusual style of how the algorithm plays and notices that humans can learn from new strategies proposed by the computer.

Of course, algorithms managed to reach such levels, because they processed millions of training examples that consist of games between humans. There are also publications in which researchers have shown that algorithms outperform single doctors when compared with diagnoses developed by a group of specialist doctors. This was also thanks to the fact that the algorithm learnt from millions of properly prepared training examples.

On the contrary, humans need only a few training examples to gain the knowledge that is enough to understand a new phenomenon. Humans can generalise their expertise in a much better way, or infer knowledge from just a short definition of a new term. They can associate facts from many different fields without having as many training examples as current algorithms need to defeat a chess master. 

» Algorithms will help doctors to make a holistic diagnosis based on automatically processed data from different examinations.«

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The Rise of the Data-Driven Physician 2020 Stanford Medicine Health Trends Report describes a health care sector that is undergoing seismic shifts, fueled by a maturing digital health market, new health laws that accelerate data sharing, and regulatory traction for artificial intelligence in medicine.

Center of gravity In 2014, the US Food and Drug Administration approved the first artificial intelligence algorithm for medical applications. Since then, the number has multiplied, and by June 2019, the FDA had already passed a total of 46 algorithms.

Digital health funding is increasing at a rapid pace. In 2011, Venture Capital

funds invested $1.1B in medical startups. In 2019, digital health companies raised a total of $7.4B across 394 deals from 627 investors.

Technologies that used to be niche just a few years ago, such as telemedicine, are now becoming more common.

The number of wearables increased from 13% to 33% between 2015 and 2019.

Keeping track of health parameters with, for example, mobile applications is continuously growing in popularity. Electronic medical records, e-prescriptions, and e-referrals are spreading even faster, displacing their paper versions. In some highly developed countries, medical data is only collected in electronic form. The adaptation of digital health

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entails legal changes and mobilizes even the most delayed digital organizations to make changes. Health insurers are beginning to refund the costs of so-called digital therapies based on mobile applications or telemedicine equipment.

If we gathered all the data on innovation and information technologies in health care on a single chart, we would see an exponential trend line. We are approaching the center of gravity, where digitization affects everyone and changes the rules of the whole sector, and even the way health professionals work.

Doctors in a new technology ecosystem In a survey conducted by Stanford Medicine and presented in the 2020 Health Trends Report, 47% of doctors and 73% of medical students admitted that they were taking additional professional courses to be better prepared for further technological developments in health care. The most popular topics are data science, population health management, genetic counseling, programming, and artificial intelligence. Not without reason – the doctor of the future will be not only a specialist in diseases but also a specialist in data management and concluding statistical analyses.

Physicians will be required to cooperate with artificial intelligence systems.

The amount of available data is increasing. The task of AI algorithms will be to analyze the data, but it is the doctor who will make the final decisions, and he or she must know how and where to look for additional information. The growing importance of prevention means that doctors will create statistical models, forecasts, and health scenarios based on the information available in the electronic medical record and the parameters monitored by the patient.

Only 7% of doctors and 14% of students/residents use systems based on artificial intelligence regularly. In the case of telemedicine technologies, these numbers are 39% and 41%, respectively. The survey also examined the value of data provided by patients in the clinical decision-making process. 20% of medical students/residents and 23% of doctors believe that the information collected by patients in health applications is very valuable. Similar results relate to the data collected by wearables. This shows a gap between the expectations of patients who use new technologies to monitor their health and the doctors who

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» Doctors will largely rely on data.«

do not see significant value in the information provided directly by the patient.

One must assume that with time patients will provide more data and expect the doctor to take it into account while making a decision.

The vast majority of medical students and residents (77%) believe that education in medical schools was helpful in preparing for new technologies in health care. The fact that doctors and students are trying to expand their knowledge of issues concerning, for example, artificial intelligence, shows that the education system is falling behind the market trends. Doctors believe that a third of their tasks will be automated in the future.

velopment because they are the end-users, and they cannot be burdened with solutions that disrupt their work instead of helping. In the end, the medical profession must undergo an ideological transformation, from a job focused on medicine to one that has a lot to do with data, statistics, and artificial intelligence. 

Dealing with AI The transformation of the health care industry also means that new players like Google, Apple, or Amazon are entering the sector. Surprisingly, medical professionals see this trend as a chance. The fact that non-healthcare oriented technology companies start to offer healthrelated services to patients is more often perceived positively (49% of doctors and 45% of medical students/residents) than negatively (30% of doctors and 24% of residents).

Doctors will get access to new data sources that can improve treatment outcomes, focus on prevention, precisely diagnose diseases, and treat them in a personalized manner. New forms of medical services will benefit patients as the data collected in their electronic medical records will reflect a complete picture of their health. On the other hand, doctors feel a sense of uncertainty about the future and the development of new technologies.

An imbalance between the passion for working with patients and the administrative burden can be already observed.

As a part of the existing education system, future doctors should start learning new competencies. Doctors must be involved in the process of technology de-

2020 Health Trend Report Download the full report

by Stanford Medicine.

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Don’t fake it till you make it Aline Noizet knows the digital health scene inside out. In an interview, she reveals the biggest mistakes repeated by startups and how to scale the business to succeed.

Aline, imagine that it’s around 1439 and Johannes Gutenberg comes to you and asks about your advice on his startup. He wants to build a printing machine, but he has no money, and it seems like some stakeholders – including monks – won’t be happy to lose a monopoly for writing books. What would you suggest to him?

Don’t give up! If you believe in your idea, be bold, resilient and smart. Explain to the stakeholders how this new technology you want to build can benefit them and how it will make their life bet-

ter. Education is key. Find some partners who believe in your idea, are well connected and are ready to support you to take your plan further. But protect your idea somehow. Craft some numbers: define the benefits for each stakeholder – qualitative and quantitative – and put a dollar sign on them, that will help you attract money.

The church being afraid of losing a monopoly for writing books is analogous to AI today. We always talk about AI replacing doctors, but we communicate less about the fact that AI empowers

doctors by supporting them in the decision-making process, freeing their time and the fact that AI created new jobs that didn’t exist before like data analysts. It’s a mindset change that takes time.

Five hundred sixty-five years later comes to you Elizabeth Holmes from Theranos and also needs help in developing her great idea. Would you believe in her innovation? Why did so many people trust her?

Would I believe in her innovation? I’m not a scientist so I wouldn’t be able to assess the technology itself, but I certainly wanted to believe in it. I remember being in San Francisco in 2014 and seeing the big billboards across the city, promoting Theranos and its promises for the patients and consumers. It was very much

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in line with the innovation wave of the time of doing more with less and improving the patient experience.

Why did so many people believe her?

She positioned herself as the next Steve Jobs, the man who created the revolutionary iPhone. She would be the one revolutionising blood analysis.Then, it’s all about who you know. Being a daughter/son of and being part of certain circles, opens many doors. Many of the investors who put money in Theranos were people she had connections with through her family mainly, with little or limited knowledge about science, relying on what she was saying but without being able to verify the accuracy. She showed people what they wanted to see, like when she was faking demos to pharma companies. It’s the perfect example of the “fake it till you make it” attitude. But the “make it” never happened, and according to people who have some knowledge in the field, it is physically impossible to do so many sophisticated tests with so little blood. I recommend reading the book “Bad Blood” by John Carreyrou and watching the documentary about the Theranos story. It’s very insightful.

You are familiar with the digital health startups scene. There is a lot of enthusiasm, but most of the ideas fail. What are the biggest mistakes of the innovators?

Let me mention the most important ones: Technology/market fit. A lot of people have great technologies and are trying to push them on the market or find a problem on the market that they can solve with their technologies. But there is not always a market fit for their specific solutions, and the motivation is different than someone who is starting from a concrete problem and is trying to solve it and have an impact on people’s lives.

Knowledge of the market. Some entrepreneurs identify a problem on the market but don’t have a good enough understanding of the problem itself. They don’t involve the end-users in the development, resulting in low usage of the solution because it is not what the market is looking for. Validating an idea, talking to the market and involving users in the development phase are essential.

Money management. Many entrepreneurs struggle to manage the money raised and run out of cash, burning it too fast or not spending it wisely. It’s crucial to have a clear roadmap and to work on

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getting traction. The more traction you have, the more you will convince investors to put money or to follow on their initial investment.

Team. Struggle within the founding team, or an unbalanced team will lead to trouble. To have cohesion within the founding team and a shared vision for the future of the company is key to succeed.

Choose your work, wife/husband very wisely. It’s also essential to balance the founding team and be complementary.

With a lot of regulations and a very conservative culture, healthcare seems to be a minefield for those who want to bring a change, revolutionise medicine. Are legislations and reimbursement systems the most significant barriers?

No, I don’t think they are. Things have been moving slowly but surely those past years on this front: FDA has been pretty proactive in the US, clearing innovative solutions, like the first autonomous AI solution (IDX) or digital therapeutics solutions like Reset or Oleena. Europe will have the Medical Device Regulation coming into action in 2020 with more precise rules to follow. Yes, it’s tricky, but those rules are made to ensure patients’ security. Lives are at stake, so it matters. Insurance companies are also moving slowly into the digital health space with more and more companies including digital solutions and services into their package. A classification is missing when it comes to reimbursement of digital solutions, but insurers are finding ways to overcome it internally, like using marketing budget.

I think that the most significant barrier is adoption. End users, mainly patients and healthcare professionals, need to be educated to understand the benefits of those innovative solutions and how they work. Fear of the unknown is widespread: often healthcare professionals see digital solutions as a threat to their job or legacy while the reality is different. Funny enough, the more reluctant is not always the one we think.

We see many older professionals adopting new technologies while young ones stay away from them. Innovative technologies won’t replace healthcare professionals; they will leave behind those who don’t adopt them.

As you say, we are so used to things the way they are that we can’t imagine an app replacing a drug, for instance. That’s where education and medical valida-

tion involving the right actors are needed. User experience is also essential to ensure that the user will be engaged and keep using the app.

You describe yourself as a „digital health connector.” How important is it to make the right connections, meet the right people at the right time to „sell” the idea on the market? Is technology itself the same important as networking?

Having a strong network is critical. I believe more in building long-lasting, genuine relationships rather than meeting the right person at the right time. If you have an established network, selling your idea to the market will be easier as you can tap into your direct network or ask to be introduced through a connection. Trust is key in any relationship, but trust comes with time. It’s easier to ask for help or discuss the commercial relationship with someone you know and trust already and who knows you and your project/idea.

When it comes to fundraising, for instance, it’s better to start talking to investors when you are not looking for money yet – you get to know each other, build a relationship, and when the time comes, you know each other already. If there is a fit, the investment will happen naturally and pretty fast.

Is technology as important as networking? This is going back to your question about Theranos. Connections are everything; it’s who you know! Theranos technology was not working, but Elisabeth had connections, that’s how she made it, and Theranos became a unicorn.

Networking can also help your technology and solution. The more people you meet, the more feedback you get on the market’s need and on your solution itself. The ideal scenario is a mix of the two: to have a reliable technology/product and a vast network to push it to the market.

Could you please give five most essential hints on how to become an effective connector in digital health?

Here we go: • Listen! It’s not about you; it’s about them. You need to understand well what people do or are looking for to make the right connections happen.

Listening requires empathy.

• Be curious! Read, go to conferences and meetups, investigate trends and players. The more you learn and the

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more people you get to know, the better you will be as a connector.

• Be genuine! Connect people because you care, not because you want something in return. Life will pay you back at some point when you less expect it!

• Be selective! Don’t connect just anyone. Recommending or linking the wrong person can shed a bad light on you and affect your credibility.

• Do your homework! When connecting people, explain each party why you think they should be talking and where you see potential synergies, as it may not be noticeable for them and you may see things that they don’t.

Not doing so can be a waste of time for both parties.

Is there a startup that inspired you most recently?

Kaia! They are a multimodal digital therapeutics (DTx) for back pain and other chronic conditions combining physical, psychological and educational elements.

They are one of the pioneers in the field in Europe, ticking all the boxes that make an app a DTx. Not only are they an excellent example for others to follow their path in Europe and learn from them, but they are also available and open to help others and share their lesson learnt.

Kaia listens and adapts the conclusions very well into the philosophy of the company. They have involved the users a lot along the way to make sure the solution fits the market’s needs, continually improving their product. They recently integrated motion sensors to correct users while they do the physical exercises for back pain.

» We see many older professionals adopting new technologies while young ones stay away from them.«

I believe the team itself has a lot to do in the success of the company. I was talking about that with Konstantin the CEO, and he was saying that they are investing a lot in the people, empowering them as much as possible. This is something that they learnt from their previous startup, Foodora, that they successfully sold, before starting the Kaia adventure with the same cofounders. I believe this is the key to success. Your people are your best assets, treat them with respect. Without your team, you are nobody.

Patients can choose among thousands of fitness apps, some trends like digital therapeutics or mental health draw the startup’s scene attention. Are there fields in digital health where the competition is already too big and the niches with great potential?

The healthcare landscape is enormous; there is always space for new solutions to come in. If we look at Europe, each healthcare system is different, which means multiple markets to target. You need to shuffle things around sometimes and look further. Your solution may have a lot of competition in your market but have potential in other markets.

There are fields like online medical appointment booking system which are becoming a bit crowded, with leading players like Doctolib in France – which recently became a unicorn – or DocPlanner, present in 15 countries in Europe and Latin America. It makes it more challenging to enter the markets where those big players have a monopoly. Other busy fields are telemedicine, skin cancer app detection or period trackers for instance.

We are moving towards a more regulated scene, differentiating between fitness apps and medical grades apps, clinically validated. The competition decreases a lot when regulation is involved.

Although the regulation process is tedious and time-consuming, market access is easier, and it also increases the price point of the solution.

As you mentioned, Digital Therapeutics (DTx) is an exciting and promising field and very hot at the moment. It has the potential to disrupt healthcare by offering effective digital treatments available anywhere, anytime. DTx is targeting niche markets like diseases with no or little cure like IBS, multiple sclerosis, etc.

They also have the potential to address different comorbidities at the same time

in a unique solution. Mental health is one of the critical fields targeted by DTx, using Cognitive Behaviour Therapies: Patients can access the treatment from the comfort of their home, without having to travel, they can remain anonymous if they want to, and the cost decreases.

Mental health is a big issue worldwide, and thanks to solutions like telemedicine or DTx, more people are getting access to treatments, and the outcomes have been very positive.

One of the other targets of the DTx solutions has been to reduce the amount of drug consumed. Used with pain management, for instance, it speeds up the recovery period and reduces the hospital stay. DTx, the little sister of digital health, is a field to look at with promising opportunities.

What big tech companies could learn from startups, and what startups should learn from big tech companies?

Big tech companies can learn to be more agile, speeding up the decision-making process and reducing validation layers.

Working with startups can also help them to think out of the box and explore new opportunities for their business, either different business model or new/complementary lines of products. Startups have an education role to help big companies keep up to date with the latest trends on the market and how it can be applied to their businesses. Startups, on the other hand, should learn some processes and structure framework from big tech companies but keeping it light. They can also learn how to scale successfully, learning from the mistakes or experience of the big companies. 

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Becoming Hyperaware Dr. Rasu Shrestha - Chief Strategy Officer and Executive Vice President at Atrium Health - answers bold questions of our readers. Instead of a formal interview, an honest talk about technologies in healthcare.

If a patient gives you a correct answer for a problem, would you rely on the patient’s response, or would you go to a more „respectable” someone who may have a compelling answer?

It’s such a privilege to work in the field of healthcare because we are here to serve human beings truly. That goal of serving is an important concept to grasp – ‘healthcare’ is about the ‘health’ of the patient, and the ‘care’ for the individual and their communities. If we understand that, we will naturally value the opportunity not just to listen and empathize, but also partner with our patients and their circle of trust, and co-chart the pa-

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tient’s personalized journey towards better health and wellbeing. We should, of course, use our professional judgment to bring in additional insights and evidencebased guidelines, but always ensure that the patient is indeed at the center of any decision we make.

How do you get to real digital transformation?

The past two decades have been about moving from analog to digital, where we have replaced paper, film, and folders with their digital substitutes. While substituting analog with digital was an essential first step, real digital transfor-

mation will happen when we genuinely capitalize on the digital assets and connect the digital content from across disparate silos of data repositories and generate new insights that truly move the needle in the way that we’re practicing healthcare. Real digital transformation is about using the power of digital (algorithms, intelligent visualization, smart user interfaces) to allow us to be hyperaware, make more informed decisions, and execute the right actions at the right time in the most impactful ways.

Should we build autonomous AI-based systems in healthcare?

The train has already left the station. AI is here to stay. The real focus needs to be in making sure we have the right guardrails to ensure the proper levels of governance and the right ethical considerations in the pursuit of further embracing AI in healthcare. Done right, AI will

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» Digital health definitely needs to focus more on solving the many challenges of the social determinants of health.«

dramatically increase the ability for clinicians to better understand and interact with the patients and the populations they care for. Additionally, I hope that advancements in AI will also allow clinicians to be more human – giving them back the gift of time, to be able to connect with their patients meaningfully, to think, and to honestly care in an unrushed and empathetic manner.

to use patient-driven biological markers and data points to derive more predictive hypotheses vs. the more traditional trial and error approaches that we are used to.

What are your digital health predictions for 2020?

Should there be criteria for effective digital interventions?

We have so much to do in 2020, as we look ahead into the bold new decade of leveraging tech for good. My ‘starter list’ of what we can look forward to in 2020: • We will start to break out of the AI hype cycle and focus on what’s real (and there’s a lot!).

• We will push forward with digital health solutions that focus on the whole person (vs. “just” the patient).

• We will see massive advancements at the intersection of big data and big pharma (e.g., faster/cheaper/better drug discovery and development).

Yes, we should have better-defined criteria, better governance, and better guidance around digital interventions. There are ongoing efforts that are just slowing taking form around ensuring that automation via artificial intelligence and machine learning are researched, developed, and deployed in a way that vindicates social values of fairness, human autonomy, and justice. I hope to see more work in this area in 2020!

What topics in digital health do you consider as neglected in public debate but will play a considerable role in the future?

Digital health definitely needs to focus more on solving the many challenges of the social determinants of health. These are the real economic and social conditions that truly influence individual and group differences in health status and healthcare outcomes. Digital, done right, has the potential to bridge the many gaps and inequalities that exist – and offer opportunities to have a much more meaningful and sustainable impact on healthcare outcomes.

Can AI find a new drug for cancer?

There’s definite promise in using AI to find cancer (earlier, more accurately) and determine the treatment (faster/cheaper/ better drug development and more accurate and earlier determination of the personalized therapies for any individual).

There are increasing amounts of efforts amongst pharmaceutical, regulatory, and research, and clinical bodies to leverage advancements in AI to accelerate drug development. AI has ushered in a new era of accelerated drug discovery, where we have exponentially increased capabilities

Are you not afraid of the future where we all will be monitored continuously?

Are people ready for such control?

Some argue that we are already in an era of hyper-surveillance. A recent Forbes article cited how in China, there is now “an unconstrained and unlimited surveillance laboratory across Xinjiang, a province with a larger population than 22 of the European Union’s 28 member states.”

Apparently, AI feeds on raw training data and safe haven deployments such as that in Xinjiang, where the technology can be honed and improved. These Orwellian scenarios need to be balanced with efforts to push AI for good and with meaningful conversations and actions around privacy, ethics, and data rights.

Will digital health technologies be a source of new problems and challenges for healthcare worldwide?

We have seen in the last two decades that while adopting digital health technologies such as electronic medical records can indeed make us more efficient in our clinical work through reducing the time required to retrieve charts, improving access to comprehensive patient data, and giving us better ways to manage appointments and prescriptions – these same technologies can also be an impediment to care with challenges of interoperability and clinical workflow. Technology

can and will always be a double-edged sword. The opportunity is for us to engage patients and clinicians in the very design of these solutions, embracing the principles of design-thinking, and creating solutions that increase our efficiency and also delight these end-users.

Will companies like Google or Apple become leading healthcare providers soon?

If they can offer high-quality services, why should people see this trend negatively?

Back in 2017, Apple explored buying a medical-clinical start-up called Crossover Health, as part of a more significant push into healthcare. Some say that Apple also approached other primary care groups, such as One Medical. More recently, in 2019, Crossover Health acquired Sherpaa, a virtual primary care provider with a digital health platform.

Health systems, meanwhile, are upping their game in using digital tools to better the care experience and embrace virtual health to bring more convenient and affordable care to the end-users. The reality is that companies like Google and Apple, as well as health systems like Atrium Health, are trying to meet the evolving needs of the consumer quickly – and a combination of in-person and digital services that up the experience factor for the end-users is exactly what needs to happen. The opportunity for health systems is to rapidly embrace these innovative care models, partner meaningfully with the right players, and balance the efficiency of asynchronous remote care while fostering trust in the long-term relationships with the same provider.

Thank you!  Rasu B. Shrestha, MD, MBA, is executive vice president and chief strategy officer for Atrium Health, one of the most comprehensive and highly integrated not-for-profit healthcare systems in the nation. As a member of the executive leadership team, Dr. Shrestha is responsible for Atrium Health’s enterprise strategy, including planning and tactical direction for the organization’s current strategic roadmap and beyond.

Besides, he spearheads a renewed focus on innovation, launching new healthcare inventions, discoveries, and ideas to benefit our patients and the communities Atrium Health serves.

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i n terviews If a digital health company from Europe wants to enter the Asian healthcare market, what should be considered first, before the decision is made?

I am assuming that the team have done extensive research to understand the Asian healthcare landscape because it is radically different from any other market.

Whereas a European company is familiar with the universal healthcare system that has a central reimbursement mechanism and a vibrant set of potential customers, and the same can be true of the U.S., it is definitely not the same in Asia Pacific. The first thing that should be considered is the fact that it is completely different. The fact that you have had success with your solution in Europe does not automatically mean you have success in Asia Pacific.

And so the first question you should ask yourself is “Do I understand the landscape and do I understand which markets are aligned to the value proposition that I bring to market with my company, and is there a fit?”.

What are the most common prejudices that usually stop companies from making this step and which of them are wrong?

Explore Digital Health in Asia China and Japan are leading the global race of digitalisation. Julien de Salaberry Founder and CEO of Galen Growth Asia, explains how the Asian markets work, their specifics, opportunities and traps.

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This is a tough question. Some of the prejudices, to be honest, are linked to a self-belief that because we have had a reasonable amount of success, and traction in Europe, the same will be true if they take their solution to Asia Pacific.

That is self-belief, but it is also, potentially, erroneous and therefore blindsides you as an organisation.

The other of course is that you assume that these markets being very large in demographics are therefore huge markets.

Another concern, I think, are the semiinformed people in Europe, who with regards to Asia-Pac, is they think it’s too complicated, they think its culturally too tricky, they believe that the language gets in the way, they think that their systems are very different etc. And most of those perceptions are misleading because it is in fact a fast-growing ecosystem that has a great deal of sophistication to it.

If you take digital health as our focus today, 8.2 billion US dollars were invested in digital health in the U.S. last year, whereas 6.4 US billion dollars were invested in digital health in Asia Pacific in the same period. So, hopefully, a testament to the fact that it is incredibly vibrant, it is increasingly sophisticated

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and has the support of government regulators, as well as many corporations, not to mention the many investors. The ingredients are that to be successful, you have to make sure you are the right fit for that market.

How would you describe the Chinese and Japanese digital health market, open for companies from Europe or challenging to explore? What are the most significant barriers and challenges to overcome?

Technically and theoretically, both those markets are open to a foreign company entering them, and there are plenty of examples of that being done. The Japanese health market is very domestic, self-serving and has several barriers to entry that need to be overcome – none other than language, for example, and culture but also a certain resistance to change. The average age of a doctor in Japan is 65.

Most of them run their business practices on paper. The EHR penetration in Japan is very low. Certainly below most OECD countries. But there are tremendous opportunities if you bear in mind that Japan has probably one of the largest proportion of elderly population for an OECD country.

The Chinese market is much larger, less mature but there is strong government support. Again, I would describe it as a reasonably domestic market and what I mean by that is that there is enough to solve in China, for the Chinese digital health ecosystem to focus on its own market rather than, let’s say, going abroad. Therefore there are many opportunities for foreign companies with proper solutions to try and enter the market. In each occasion, or for each of those markets, it is strongly recommended you find a local partner to help you navigate and successfully enter the market. Trying to do so by yourself is usually going to end up in tears.

What are the most significant differences between the European and Japanese healthcare market and which of the factors should be taken into consideration for the companies from Europe that want to join the Japanese market?

While leaving aside culture and language, and looking at the dynamics of the market, the Japanese market is a universal healthcare market, like most European markets with a reimbursement mechanism and a very domestic, nation-

al approach to how they solve issues. So a foreign arrival is not necessarily welcome, so you do need to be aware of that.

Other than that, the steps to enter the market, the steps to get regulatory approval etc. are similarish. But, it is important and imperative for any organisation wishing to enter Japan from Europe, to fully understand that process and to ally themselves with experts who understand the process, regulatory pathway etc. to establish themselves correctly and therefore get traction in the market correctly. One thing that you certainly need to bear in mind is to allow time. This will not be achieved quickly, and market traction will take time, and therefore it needs to be looked at that way rather than a quick solution to a market expansion question.

The interesting thing is that there are a number of local, large corporations that are very interested in foreign technology to import and integrate that within their business models. Be they tech suppliers, be they pharma companies, be they healthcare service providers. There are examples where American digital health companies have built some decent relationships/partnerships for their solution with a large Japanese pharma in order to get market entry. That kind of collabora-

» China has been very clear about how health or patient data is to be handled, which, for example, is forcing a large number of corporations to re-think their strategies.«

tion is undoubtedly beneficial and should be considered as a means of accelerating scale within Japan.

In China, there are already many successful companies embracing digital health and telemedicine. This is, among others, for example, Ping An Good Doctor. Is there a place for solutions made in Europe?

There is, definitely, but it would have to be a solution that is addressing a specific pain point in China. It needs to be a solution that is a model run with the right set of partnerships and allies around it in order to ensure or certainly increase the potential success of implementation within that market. The mistake not to make, I guess, the health warning here, is the fact that if your solution is working well in Europe, it is not a prediction that you will do well in another market like China.

Our guidance usually to most organisations is that you will, no doubt, need to re-think your business model completely, as you enter into China. You will need to do your research to understand what the competitive landscape looks like. The Chinese digital health ecosystem is incredibly innovative and, because of demographics, as well as different regulations, usually has, if it is successful, already quite substantial scale. Again, it is worth trying to enter the market through a partnership. So if you are a very successful French digital health start-up and you have a good relation with someone like an Axa, it may be worth trying to look at the Chinese market through that channel, for example.

Can you please mention one or two examples of companies from Europe that made it to the Asian market and which factors were crucial for the access?

The more visible example of that is, for instance, Babylon, a UK digital health start-up that has established a strong relationship with Prudential, the insurer, and is leveraging that relationship to enter the Asian market. Another UK example is Medopad which has built a relation with Tencent Healthcare and is entering the China market that way. I guess there are many more, but those are the most visible and recent examples, and in each case, the route to greater potential of success has been built through a partnership with a large organisation that has already got the footprint.

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You mentioned two start-ups. So should, in general, start-ups also keep eyes open for the Asian market?

What I am saying is that to monetise in Asia, indeed, as a digital health venture, you are increasing your potential success from a revenue perspective by building a partnership with a large organisation which has already established itself in that market in Asia. And so really that means a B2B2C model with a large player, already with a footprint in the region.

It is not a guarantee of long term success but I guess it certainly increases your potential success of getting scale in Asia.

What are the most common mistakes committed by European companies leading straight to the failure?

It boils down to the lack of research and preparation. It is two things. One, you get the organisation that is building a solution without having done sufficient research as to whether the customers will buy the solution, but this is not unique just to EU companies trying to get into Asia. It is probably common to many digital health start-ups that are building solutions before they really understand whether there is a market for their solution. Concerning explicitly entering into Asia, the most common mistake is not understanding the market you are stepping into and assuming that because it is a mature market in Asia, like Hong Kong or Singapore or Japan that the solution that you are building in Europe is going to have a fit.

In terms of market dynamics similarities, you need to identify whether the pain point you are solving in Europe is a real pain point as well in Asia. The other mistake, of course, is to assume that there is a robust reimbursement mechanism, which exists in Europe but does not necessarily exist in Asia. In fact, the lion share of patients in Asia either selfpay or co-pay for their healthcare, which means that your business model, your revenue model needs to be probably rethought.

Data safety and privacy issues in China raise in Europe many questions. What is your opinion?

I think Europe has a very schizophrenic relationship with data privacy. And we, as an Asia organisation, we have now es-

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tablished our office in Europe, and we are experiencing this on a day-to-day basis. As the world knows, Europe has rolled out GDPR, as its defence mechanism.

I’m not necessarily convinced that it is working to the extent of what was initially designed. I will give you a short example. Most websites in Europe now have a new cookie policy, whereby a user has to approve the use of cookies before you proceed. Generally, the majority of users are clicking yes simply because they want to get to the website, to get to the information they initially wanted. So they are opting in but not necessarily understanding what they are opting into. The schizophrenic relationship is that it is easy to point the finger at China and the perception of their more relaxed, whatever you wish to call it, data safety and privacy issues.

China has been very clear about how health or patient data is to be handled, which, for example, is forcing a large number of corporations to re-think their strategies. China has stipulated that their patient data can never leave the country and you need to be approved to be able to access that data. And so many large international companies that were initially leveraging capabilities in another part of the world on data generated in China are now having to re-think how they address that data and what capabilities they need on the ground in China. Now, it is fair to say that China regulations are very different from European laws. I imagine China will look to tighten some of these going forward, further than they have already but regulations do exist and the Chinese government is generally trying to tread a careful line between leveraging data to the advantage of innovation, as well as maintaining control as to how the data is being used.

Could you please list the most significant opportunities and perils related to launching operations in China and Japan?

They are very different markets and a very different focus. Japan’s primary focus is on elderly care. The fact that the population size is reducing, and therefore the workforce is reducing, they have challenges of needing to service this population from a healthcare perspective with a dwindling tax income, and a dwindling workforce able to provide these services.

And so Japan is very much focusing on technology to address these issues, largely because it is not a very open market when it comes to foreign labour. Some of the challenges I am referring to are not too disimilar to some of the challenges a lot of European countries are facing. China is very different. It has a much less established healthcare framework. It has implemented a universal healthcare, but its per capita pay-out is very, very small and has very large demographics. It is currently 1.4 billion versus, I think, 120 million roughly in Japan, so it is a factor of 10. China has challenges such as primary care and specialist care all rendered out of hospitals. The Chinese government is trying to move primary care out of the hospital context by leveraging technology while, organisations such as Ping An Good Doctor that you mentioned, WeDoctor and many others, are growing fast by being able to support the government, provide primary care services outside the hospital and therefore enable healthcare to be addressed or delivered, or to be provided when it comes to primary care needs.

So the opportunities are significant, without even talking about the disease burden in China, concerning chronic diseases for example mental care, diabetes, cardiovascular, and also smoking etc.

And the peril I think is very similar to all the ones I mentioned already. Whereby, if you do not have the right partner, you have not done the right level of homework and due diligence, you will end up burning a lot of cash getting not very far.

There is one perceived peril or watchout that you need to be aware of which is much more China-related, is the one related to intellectual property, and you need to be aware of that. This potential issue should not be overblown, particularly for digital health in terms of IP protection, as few digital companies have patents. But you should be aware the is a risk. You should, much more importantly, be aware that it is a highly innovative space and therefore you are likely to find competitors in the market in China that already do to some extent what your solution as an EU digital health company is doing and therefore the success will be your business model, not through technology alone. ď Ź

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Our future with algorithms What if we are wrong about AI in healthcare? What if the dream of data-driven medicine will turn into a nightmare of data misuse and restrictive supervision of health 24/7? Interview with Maneesh Juneja, Digital Health Futurist.

What is your most dystopian scenario for data misuse in healthcare in the future, and digitalization in general?

The most dystopian future is one in which our health data from a variety of sources is used to deny us opportunities in life. In those countries where governments provide healthcare, the worst scenario is where you get diagnosed with a specific disease and the government de-

termines that you increased the risk of getting this disease because of your lifestyle choices over the years. As a result, the authorities decide not to offer free or subsidized doctor visits and treatments but charge you full price instead. What about a future where a government limits our ability to travel, buy a home, or change job, simply because of the choices we have made in the past that have negatively impacted our health?

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i n terviews Where does the border between individual freedom and health lie? Should governments have the right to steer people’s health behaviors in the name of better health and well-being?

We are heading for a future where technology will enable a world where every day each of us gets a series of digital nudges which are designed to ensure each of us makes the healthiest choice possible in terms of behavior. While some may feel this is a critical step towards a healthier population, and the ultimate way of enabling behavior change, I would instead expect that we use technology to educate people. To ensure that children from the age of 5 understand the impact of their daily choices on their health so that they can make informed choices at every stage of their lives.

The more data that the big tech companies gather, the more powerful they become. What are the threats to the datadriven economy?

For many, these large datasets are an opportunity to accrue and wield power, by knowing so much about our lives, our preferences, and our activities. Anyone born today is going to have a much larger digital footprint than those born in earlier generations. What if the data being collected about you from your birth is

used as part of an interview process for a job when you are an adult? Could the health data collected about you as a baby be used to predict your risk of developing a certain disease later in life, and your potential employer uses that data as part of the assessment to determine if you are going to be a productive employee or not?

Will we one day find ourselves in a situation like a protagonist of George Orwell’s “1984”, Winston Smith, where we are forced to eat our favorite, but unhealthy and banned food in secret to hide this “crime” from the smart home devices that control everything we do?

It could well be that economic constraints force us to choose a system where our choices are monitored 24 hours a day by the government. To make healthcare sustainable, if we were offered an opportunity of paying a lot more income tax or being tracked 24 hours a day, maybe most of us would choose the latter. We already have people living with eating disorders who eat secretly, and some – who are sharing data from their activity tracker with their health insurance company – is falsifying that data by putting the tracker on someone else. It does sound crazy to imagine a future where people would find ways to go off the grid, just to eat a piece of cake or stay up late watching a

movie, but if there is one thing we should learn from human history, and that is always to expect the unexpected.

What should we do to avoid these pessimistic scenarios, moving us from health democracy to health dictatorship?

So the key to ensuring we don’t end up with a health dictatorship is to have a national conversation about the health of the nation. A conversation that involves everyone across society, to listen and to learn what communities and individuals want, but also what they don’t want.

What does health mean to each of us? Do we end up with a much healthier population but with all joy removed from life, because we no longer have the freedom to choose what we eat, what time we go to bed or how long we walk? I do believe in the power of data to help us make better decisions, but that data has to be used in a fair, transparent, and responsible manner. Or maybe we need to look at the future from a different perspective?

Perhaps the structure of society needs to be overhauled so that it’s easier for us to walk to work or fruit and vegetables are affordable and accessible to everyone?

Rather than limiting our choices, why can’t we build societies where we are presented with the best possible choices to maximize our chance of living a healthy life? 

» I do believe in the power of data to help us make better decisions.«

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Plastic touch The wave of new technologies in healthcare raises many ethical questions.

Can robots supplement the ‘human touch’ of real doctors? Are we able to forgive machines that make medical mistakes? Can a chatbot offer an empathetic, compassionate level of care? An interview with Joanna Bryson, Professor for Ethics and Technology at the Hertie School in Berlin with an affiliation to the Department of Computer Science at the University of Bath.

In healthcare there is a big fear of Artificial Intelligence replacing doctors. Do you think it will be possible to one day build an AI-based system that would be able to make decisions as good as, or even better than doctors do?

There are two really different parts of that question. We already know that we can use AI to carry out better decisionmaking in very specific areas. So, like, you can have a better memory, or you can combine information in a more coherent

way. There is absolutely no question that you can, for specific decisions, build specific systems that might be able to find the right information and suggest appropriate steps. But there is also another part of the process of being a doctor than only making decisions. In fact, part of it is just being accountable for the combination of the information that you use when you are diagnosing some patients. So, for example, Geoffrey Hinton famously said that we didn’t need any more radiologists because, the deep learning is better than human spotting things in X-rays.

And I was just a few months ago asked to the annual meeting of Norwegian radiologists and, apparently, what’s really happening is they are actually getting

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more radiologists hired because every radiologist is more valued. They provide more value now because the AI makes them better at their job. The mistake is thinking that because AI is good at helping you with the decision, that the AI is taking the decision.

There’s a lot more to healthcare than only knowing the right answers. There’s also convincing people to do things and there’s just being, as I said, accountable to the insurance industry, to the medical profession and ultimately only humans can be accountable. Somehow, now you could imagine there was a company that was accountable for the machines they made that were making the diagnosis and people would stay at home like, say, because of the virus that everybody decides to stay home and just send their symptoms over the internet. You can imagine that kind of diagnosis but you can’t imagine an entire healthcare system like that. I mean, there’s just a huge amount of physical interaction with doctors, both in terms of the tests that are taken but also in terms of therapy.

Healthcare is about human touch and empathy. Can we basically teach AI such abilities? Can artificial emotional intelligence supplement human emotional intelligence?

You are never going to have empathy in the machine, based on the machine’s experience. We have trouble generating empathy even to other humans from humans. And machines are far different, so there is no commonality – or there is a very limited one – in experience. However, having said that, what we do see in terms of artificial empathy is using the machines to store the experience of other humans and then bring that experience to a problem. For example, if you’re buying a book or a movie, a system checks other people that have the same preferences as you do and then suggest that they enjoyed this book so you also should. You can call that empathy but it’s interesting because it’s really matching up two different models of people. Although, actually, as an academic, I routinely do that matching between models of students and I’m sure doctors do that too.

They could think: “Okay, I wouldn’t feel that that way but I have another patient who felt this way. Do you feel like that?” So that kind of thing we could potentially be using AI for.

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We could also be helping improving how doctors do their job by giving them access to those kinds of attitudes through the machines.

A lot of organisations, when they get AI, already realise that their employees are a very huge asset. And so, what they want to do is grow, keeping the employees they already know are good, rather than just figure out ways to get rid of them.

What do you think about the so-called ‘synthetic emotions’? A robot with a build-in ‘compassion’ system is a compassionate robot or just a programmed machine?

It’s important to understand that every single thing that’s AI is an artefact that someone built. You can say: “Oh, but it learnt itself.” No, it doesn’t learn itself.

Somebody designed it to learn. And, generally speaking, you know with any software, any system that exists, they not only design it to learn but train it, they adjust the data, they set the parameters, they’ve tried it a million times, they’ve finally found something that kind of works and they’ve released. So, it’s a very, very human process, developing something that is definitely programmed.

When that thing that’s developed doesn’t show compassion, we have to go back to the question about responsibility. I would say the compassion is the compassion of the programmer or the one of the healthcare practitioners that is using it, or the compassion of the organisation towards their developed AI and their compassion towards their customers, or their lack of compassion. But that compassion can be expressed through a machine. I don’t think it makes sense to talk about the machine itself as a compassionate entity but it is the one that’s expresses the compassionate act.

I usually talk about this in terms of reality but compassion is the same. For example, when you choose the moral action it doesn’t necessarily make you a moral agent. Maybe that moral action was the part of your job, you were told what to do. If you program the robot to do something that is compassionate or moral, you could say that the robot is the moral agent. You can make it legally responsible but my arguments are that if you decide to find the responsibility that way, you going to wind up with a very incoherent system of justice that people are going to exploit because the robots

don’t call themselves into existence. It doesn’t make sense for people to think: “Oh, this robot really cares about me”, when they should be thinking: “Oh, my mum really cared about me when she bought the robot,” or “This is a pretty good healthcare plan”, or “It’s a bad healthcare plan.”

Some patients complain that doctors don’t understand their needs. Is it ethically right when a patient trusts the machine more than a doctor because it can recognize more precise the needs using Big Data analyses?

If somebody comes and says: “I’ve looked this up on Google and it seems to me that I’ve got X” doctors will take that seriously and will listen and talk to patient. But they actually know better because they have had a medical education.

We do have evidence that people sometimes take directions from machines, ignoring expert humans because they think machines are infallible.

If you think humans and fallible, then you should realise therefore the machines they build are also fallible. But people just have this myth of perfect machines. I think it’s because we use computation in math, which is logically coherent, accurate.

There may be people that would have better outcomes with a machine just because they don’t get as stressed around the machine. An example is a story about Syrian refugees that preferred to have AI therapists because they felt guilty to tell human the things that they saw – their tragedies were so terrible. They knew they needed help to get through their problems but they didn’t want to talk to humans because they felt like they shouldn’t cause stress to any human. So that’s another interesting reason to not want to talk to a human doctor.

Already today, social robots are used in therapies for people with Alzheimer’s disease or children with autism. Some patients are convinced that, for example, a robotic baby seal is a real animal. Do we cheat patients in this way or the ends justify the means – in this case, I mean patients that feel better, calmer…?

Is it wrong to tell children about Santa Claus? Different people feel very strongly about when we should and shouldn’t tell children different things. And the other end of life is really very different –

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one day children will go out and come to their own conclusions.

I wouldn’t see a reason not give to people that are dying from cancer morphine, I don’t see a reason not to tell somebody, who is in this terminal situation, to make them as happy as they can be. But this is something that is not up to me. This is something that people have to decide individually. I had a friend who died about a week after a plane crash . To my knowledge, she was never told that her brother was killed in the plane crush because she was in Intensive Care.

We expect machines to be perfect. So, will we be able to forgive AI-driven robots that make mistakes and harm patients?

I don’t think forgiveness is the term.

Again, it’s not about the machine, the robot. It’s about the ‘who is actually at fault’? The fault there is in the healthcare system. The point is that you need to look through the system. You need to look through, transparently, who created the system, who is at fault, what is the cause, what is the justifiable risk or if it wasn’t a justifiable risk. Don’t think that because the robots have come up, everything will be re-invented. It’s similar with driverless cars. About two million people per year are killed by cars.

We can bring that down to two hundred thousand. But with autonomous cares, it will be a different two hundred thousand

people. So how are we going to handle this? So, I think that sometimes the AI people have to get over themselves to realise that there is a whole system of government out there handling these kinds of problems.

What digital technology, in general, does is it changes our societies. It changes what we can do and what we can’t do and it changes how much we know. But fundamentally, as people, our needs and desires aren’t really changed that much.

What does worry you most when it comes to Artificial Intelligence in healthcare?

Should we be pessimistic or rather optimistic when it comes to the digital revolution?

In general, I’m mostly worried about the nudging and the division of responsibility and the privacy consideration. We have to make sure that there is a division of responsibility, that the robots themselves aren’t blamed or credited when, in fact, it’s the practitioners that choose to blame them and the corporations that develop them. Other things that worry me are democracy.

How will AI change us as – people and us as – patients?

This is not about the technology itself.

It’s about how the technology is deployed. We have to be more aware about privacy, how is money being invested - is it benefiting everyone. I would expect that the digital revolution will improve outcomes but it has very little to do with the digital technologies and much more with the healthcare policy, and how healthcare is being provided and about making sure that all citizens are properly taken care of. 

» It’s important to understand that every single thing that’s AI is an artefact that someone built.«

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Taming the change In healthcare, we are having to confront our biggest-ever change, which is digitalization. It requires cultural, organizational, and technological shifts.

And a transformation in the mindset of all stakeholders in the health ecosystem. How can we lead these changes? I talked to John P. Kotter, a bestselling author, change management thought leader, business entrepreneur and Harvard Professor.

Digitalization in healthcare is not just a change from paper to computer. It’s a change in how people work, communicate with each other, cooperate. How can we convince people to support digital transformations in healthcare settings, especially since it usually takes years before the first benefits appear?

It’s always important to recognize that you have to produce some immediate benefits. The benefits do not have to be long-term, global goals. But there has to be something that people can see and

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feel, and relate to in a positive way, not just in a neutral, anxious, or angry way.

The more benefits, even small ones, the better. Nobody is going to be convinced by telling them that something is going to happen ten years from now. If the time frame is too long, it can lead to an anxiety or anger mode, or passive resistance. People need facts, need to see that something is moving forward.

It’s crucial to show even the smallest benefits: not words, visions, promises, but the real effects. A change only works when it’s sincere. It’s not about PR or

thinking in terms of “let’s see how we can advertise the project to make it happen.”

And not even about pushing the changes by saying, “let’s just do it.” Good managers are honest with themselves, the same as with the rest of the team.

Let’s analyze a typical case study: after an IT system has been implemented in a hospital, the first problems slowly begin to appear. Doctors get frustrated because they spend more time entering data; the initial enthusiasm slowly fades.

What can we do in this case?

It always depends on the context, the relationship with the doctors and the administration, the internal culture. Making generalizations is tricky. One approach that often works, but not always, is something called “radical honesty.” Don’t try to suppress it or to ignore it, or assume that it’s going to go away after a while.

Instead, you should say, “we are going through what we have heard from others.” Assuming this can be very painful for some. Spelling out exactly what doctors are feeling, confronting the truth that they are wasting time on paperwork in-

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stead of dealing with their patients or focusing on value is the first step. But then you’ve got to start taking action.

And here is where setting the whole transformation upright in the first place becomes essential. Implementing an IT system in a healthcare setting begins not only with the IT administrators, system consultants, or the management team but with those who genuinely want the change. Strong leadership and the participation of the most visionary people are critical. I mean people who see the urgency for the change and think, “we have got to do this, and I am willing to help.” Putting together a group of such people, from doctors and nurses to administrators, will help you communicate the change, creating more of a positive sense of urgency.

One of the keys we found to successful large-scale changes is to engage diverse people from around the organization, at various levels, in all departments.

Don’t try to create a separate transformation hierarchy. It needs to be a more network-based process. The role of the leader is to empower the people, to step forward and to take the initiative to confront the problem and the barriers, to do something with them, be creative, help out.

I’ve witnessed a situation where, after the failed implementation of a large medical IT system, the CEO of the hospital was fired. They spent 1.5 billion dollars on software that hadn’t been adapted because people were not convinced, but nobody felt like expressing their negative feelings. So seemingly everything was fine, but under the surface, there were many hidden, unspoken problems.

Why? Because people weren’t asked for their opinions.

Even when things are going down, and you failed to do the right thing at the prime time, a little radical honesty helps.

It must be followed up by action, such as changes to the system, if required. Setting up an open and engaging culture for discussing problems helps make more people feel empowered to make suggestions, to take action, to help out.

To sum up, significant change happens in societies, in companies or healthcare. And they happen because it’s not about a global managed process. It’s a process where many people are engaged, in any way they can, in their small ways, because they want to.

One of the steps in 8-step change management is creating a sense of urgency.

If I were a hospital manager, would I ask myself how to formulate this urgency to sound credible?

Anything that doesn’t sound incredible is useless. It has to be honest, but remember that urgency in the sense I talked about is both an intellectual and an emotional thing.

From one of the projects I was engaged in, I learned that the word “transformation” is useless. All it did was arouse negative feelings and thoughts.

When you’re trying to create a sense of urgency, you do it through experiences, talks, meetings. It is never abstract. And there is no kind of agenda that I could write down and send out to a thousand organizations, where all they need do is adapt it.

Could you please mention three do’s and three don’ts that every manager should follow when making changes in an organization?

Do not try to execute the big change by appointing a small, select group of supposed experts to drive the process. Just get one thing clear: you’re going to need to win over the hearts and minds of a lot of people. And you don’t want them just not resisting. You want a lot of them helping you.

From the beginning, think in terms of large, diverse groups that need to engage in the game and avoid reducing them to just small, select expert groups. There’s a lot that can be done in a hospital or any business with small elite expert circles.

But they are never going to make any large-scale transformation.

Another piece of advice: big transformations are like massive, social movements. People do them because they want to cooperate, and they want to get involved. They want to help because their hearts are in it, not just their minds.

It’s not only a head game or analytics.

You’ve got to win them over emotionally. A good argument and some statistics are simply not enough to inspire.

A third one is that what you ultimately want out of all the people engaged is not just managing the process. You want them to be proactive. You want them to initiate. You want them to provide leadership in their department or among their colleagues.

You can’t manage what you can’t measure and control. And I mean the mindset. A positive mindset starts with

the freedom to take action. Don’t’ be afraid of losing a little control over other people taking steps and initiatives.

Trust can sometimes give your workers and teams the comfort of so-needed autonomy. What you have to do is to light the fire. This is about providing engaging and inspiring leadership. An executive vice-president of a well-known company recently told me: “You know, I read your books, and think it all makes perfect sense. But in practice, I’m used to managing every little process regularly.

If not, I get anxious.” Don’t try to control every little detail. Otherwise, you’ll kill off the engagement, you’ll kill off the leadership and you’ll kill off the transformation.

Regarding digitalization in healthcare, what other challenges come to mind, and how can we overcome them?

Well, everybody uses different standards, so when I switch from one doctor to another, and the systems don’t communicate with each other, they are incompatible. And down here at our place, in Sarasota, Florida, my doctor is old-fashioned. He has a small practice with three or four offices, and he doesn’t even keep electronic medical records. For him, as a small business, dealing with people like me who are from all over the country, with no access to information from other care points, it’s not easy. The interoperability problems are just enormous.

Healthcare in this state is a horror show right now - there aren’t enough established standards in the system.

I think another challenge is to move from purely reactive healthcare to more proactive healthcare. It’s not going to happen anytime soon because everything is built around reactivity, around “you get sick and come to us.” Even if there are physicians who want to change something, they are stacked in a bigger, static system. But there are more and more people who see the need for change, so the process has begun. What’s even more important is that patients start to push this transformation, realizing that the system doesn’t cover their needs. This inevitable change will also be driven by the whole reimbursement and health insurance system. The transformation has begun, although it will take time before the small changes accumulate as one significant reform. We will need a lot of patience, but we have a fascinating journey ahead of us. 

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Artificial Intelligence to put the care back in healthcare Interview with Dr. Eric Topol, digital health leader, physician-scientist, founder and director of the Scripps Research Translational Institute, and author of the book “Deep Medicine. How Artificial Intelligence Can Make Healthcare Human Again”.

Dr. Topol, you said that a visit to the doctor these days is mechanical and robotic, that medicine is broken. However, is digital healthcare itself an answer to such healthcare challenges as low quality care, long waiting times, rising costs, aging populations, or the rising burden of non-communicable diseases?

To be clear, I’m not suggesting that we dehumanize medicine any further, by making it virtual. But what I am suggesting is there are many things that we can off-load from the current standard and tradition, so that when a doctor and patient get together, it is for something urgent.

For example, concerns like a urinary tract infection, an ear infection in a child, a skin rash, or many other things that are not serious – these could be handled by the people concerned, or by the parents if it is a child, without seeing a doctor at

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all, just using a virtual connection. This mechanism is one way to remove some of the overwhelming workload from doctors. Overwhelming due to the mismatches, for all the reasons you described, such as the aging population.

However, this is only one part of the solution, there are many others. Doctors are still heavy keyboard users, even though we thought that AI would record the voice and transcribe it directly into notes. Liberation from the keyboard! We also see doctors toiling over charts to gather enough information about the patient, which could be done for them using suitable algorithms. These are some ways we can outsource this burden to machines, so that every visit to the doctor takes less time and is less prone to error, thus improving the whole relationship. In this way we could use digital tools to restore humanity in medicine.

Will AI give doctors the gift of time?

Right now, many doctors are frustrated as they have even more work in this era of computers, as they spend time clicking instead of talking to the patient.

The gift of time can be illusory, because it can make things worse. Improving productivity and efficiency can also encourage administrators to think: “Oh good!

Now you can see more patients and read more scans.” They then squeeze doctors more, making things worse. Already more than half of the doctors face burnout because they are unable to achieve their mission. This burning out of doctors is accompanied by a doubling of the medical error rates, and today about 20% of doctors suffer from clinical depression. This is a problem because administrators have already squeezed doctors to an unacceptable extent. It could

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go the other way, where doctors stand up and say: “No, you cannot do this to us anymore, or to our patients. We went into medicine to spend time with our patients, to provide care, to have a presence, to use our hands for the examination, to be able to think, to be able to care for patients with empathy, to have time to discuss things with them.” All this has been lost to a large degree, because of the squeeze.

So the gift of time is to outsource enough to machines to help improve the error rates and the bureaucracy, and to give patients the ability to work with their own data; to make their own diagnosis for non-serious matters, and to manage their personal data for things that can be validated and proven to be helpful. There are many type of digital AI capabilities that could get us back to where we were, decades ago, before medicine became big business.

The subtitle of your latest book is “how artificial intelligence can make healthcare human again.” Why do you believe in the power of AI in medicine? What threats do you see?

The biggest threat is that this highly significant opportunity to restore the patientdoctor relationship could be lost. And it is conceivable that it could get worse. Today there are an ever increasing number of threats: the risk of malware hacking, insecure privacy, and the breaching of algorithms and data; the danger of bias embedded in the algorithms, whether it is gender, racial or some other bias. There are many ethical issues, concerns about the scale of AI use or whether an AI algorithm could hurt many people quickly if it lacked surveillance, or that there is a lack of validation trials of the postspectrum to prove the value of AI. There are many things, and all of them are, I believe, solvable over time. My biggest concern is the vulnerability of the medical community to using the power of AI to make things worse.

There are so many trends in digital health, like wearables and mobile apps.

Which of these seem overhyped in your opinion?

I think that the most overhyped has been the prediction side. Classification and interpretation, the strengths that AI brings, has been especially noteworthy for images, whether they are medical scans, pa-

» Doctors are still heavy keyboard users, even though we thought that AI would record the voice and transcribe it directly into notes.«

thology slides or even skin lesions, anywhere there is a pattern. This is deep learning. The sweet spot. The one where it has been much better at predicting outcomes, like short-time survival, hospital re-admission, length of stay in hospital and predicting conditions, such as Alzheimer’s. These studies are done using retrospective data sets, not perceptively. Moreover, they have all sorts of issues concerning the interpretation of the data, so these algorithms are derived from the data sets. I consider there to be much noise, it is not nearly as refined as the image inside. There is so much hype around being able to predict all these things, even though it is not that clear yet.

There are many issues that digitalization has to face: interoperability, cyber security, ethics and transparency of algorithms. Which of them would you – as an enthusiast of newly emerging technologies – consider to be the most important in healthcare?

My biggest worry is the lack of data completeness for any individual. The problem here is not just a lack of interoperable data, but that a patient will see many different doctors in various health systems and hospitals, and yet no one can access all their data, from when they were in the womb to the present time. There is also a lack of inputs about any individual, con-

sidering all their data, not just traditional medical data but also their genomic data or environmental data. For deep-learning algorithms, we need as much comprehensive data about a person as possible. We have a compromised situation where we can only access limited pieces of data. This is not ideal, because deeplearning algorithms are about inputs and outputs. If we have a compromised, incomplete input, then we cannot actualize the potential of AI. In the book “The Patient Will See You Now,” you focused on the empowerment of the patient, driven by digitalization, and the democratization of healthcare. However, data from smart devices is still useless, and is usually not included in the electronic patient records. Patients that want to take control of their health are seen as problematic. How can healthcare be made patient-oriented in an era of disruptive technologies?

One of the most significant barriers to democratization is the fact that most doctors are paternalistic - and they have not yet ceded control to patients. Neither do they accord respect to patients who can generate their own data and want more responsibility. Therefore, most doctors still refuse to allow their patients to have access to their visit notes, even though it is the patient’s body, and in the patient’s most vested interest. In healthcare in the USA, the patient has paid for it.

We have a real problem with paternalism, and we need to make it go away.

It is important to realize that data is eminently portable. Electronic data needs to be shared and, ideally, the patient needs to own their own data and decide with whom and when they want to share their data, whether this means their doctor or a medical research study. We still have a long way to go to abolish the dependency of patients on doctors. It has to stop, doctors need to let go and say: „You know, if you can generate your own data and if you can have a validated algorithm to help interpret the data, then I support that.” Compare that with the situation today where the patient arrives with their own data and says: “Here is all my data,” and the doctor replies: “I don’t want to look at that. It’s junk.” We need to get over this inability to share. The doctor’s notes should also be the patient’s notes.

There is this sense of compartmentalization and the wrong model of ownership.

All these things have to improve.

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i n terviews In the book “Deep Medicine,” you also highlighted how deep-learning algorithms, as applied to wearable sensors, genomic information, and medical data, can create a bespoke treatment plan.

Well, we can already see this. A virtual code, a virtual medical code, is already being used for people with diabetes to help them self-manage, to learn what causes their glucose to go out-ofrange. This could be coaching them into more physical activity, avoiding specific foods or sleeping more. We are seeing the rights of virtual codes under particular conditions, and this will eventually mature to the point where all our data can be continuous, if we are interested.

I mean it’s not for everyone, but if anyone wants to use a virtual medical code, it will take all their data, everything that is available. It will absorb all the medical literature, and use it to coach them into preventing illnesses they might otherwise have. It could prevent asthma or heart failure; it could prevent many of the conditions that we experience today by giving us actionable data, algorithmic support that continuously and seamlessly accumulates our data and gives us feedback. And that data, that output, could then be shared with the doctor, when we need to do so. The doctor may only see us once a year, or maybe less frequently, while we experience our health from moment to moment. And so the virtual code aligns with that principle, so that our healthcare is not a oneoff but a continuous story. The only reason we have not got there yet is because we had no way to deal with the data.

And now we do.

So far, the healthcare system remains stuck in old structures. A conservative healthcare ecosystem with many different stakeholders is not only complex but very hard to change. The consequences are clear: the adoption of a digital solution is very slow, too slow in comparison with our expectations. What has to happen to speed up the digitalization of healthcare?

There are many reasons why it is very slow-moving, because the medical community is resistant to change. It refuses to accept change, whether we are talking about reduced reimbursement, loss of control, paternalism, authority, ceding of that authority, and giving power to patients. Resolving much of this in-

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volves training and education. There are so many factors here, within a slow-moving profession in terms of change, but what we see now are other forces that are slowing the change even further. All these things, I believe, will come about.

We are seeing progressively more embracement of digital technologies, although the pace of adoption is slower than it could be.

You describe today’s medicine as “shallow medicine” and AI-driven medicine as “deep medicine.” How will things change for patients in the future, when “deep medicine” becomes widespread?

Well, I think the greatest thing is time, the gift of time, where the time spent between the patient and the doctor can be significantly increased from the single digit minutes that exist today. With an AI presence and no need for a key-

board, the doctor does not have to spend time trawling through all the different electronic or paper pages or charts, because everything is already organized.

So there is time to listen instead of interrupting the patient, as happens today within seconds of starting. It is time to hear the patient’s story, as their life story can never be digitized so will still require a human-to-human bond. Therefore shallow medicine is medicine with errors. There is little time and no context, giving small presence. This is what we have today. It compromises the essence of what medicine is all about, which is a human bond. Here is the place for deep medicine, with a greater understanding of everyone through machine-supported analytics. There is again the ability to listen and to query, to have the required care, compassion and empathy. All the things that we see too little of today. 

» One of the most significant barriers to democratization is the fact that most doctors are paternalistic.«

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Objectivity with no empathy: how symptom checkers can help patients?

Artificial Intelligence is getting better in diagnosing. Although AI still can’t see and examine patients, it has an access to an unlimited medical knowledge and up-to-date research data. It’s learning very quickly and gaining new capabilities like emotional intelligence. An interview with Piotr Orzechowski, CEO of the startup Infermedica.

Artificial intelligence in healthcare is developing very rapidly, but the technology is being adopted on the market very slowly. Why is that, and what can be done about it?

There are many reasons – although last year there were many successful commercial implementations of artificial intelligence. Examples worth mentioning include IDx-DR, the first FDA-approved

medical device exploiting AI for the diagnosis of diabetic retinopathy, and Apple Watch 4’s feature for detecting atrial fibrillation, which shows that intelligent algorithms are already becoming available to mainstream consumers.

In my experience, the slow progress is due to three main factors: insufficient clinical validation of solutions, legal aspects, and distressing experiences associated with the computerization of health-

care. The first problem unfortunately affects the vast majority of AI suppliers, who have not yet provided solid evidence that the technology they’re offering is safe to use and will have specific benefits that will justify the investment.

The second factor has to do with the lack of clear legal liability for errors committed by AI. As in the case of autonomous cars, who is responsible in the event of a bad decision? The doctor, the patient, the provider, or maybe the virtual AI entity, whose license to practice can be revoked? The final issue is related to the often painful experience of numerous organizations that have implemented solutions such as electronic patient documentation. In conversations with hospitals, especially in the United States, the first question is usually, “Can you integrate with our EHR system, and how complicated it will be?” Ironically, it seems that in many cases the IT infrastructure itself is a barrier to the implementation of new IT solutions, including AI. There is currently a lot of hype about AI solutions in health. In which areas of medicine are they most promising?

I think that the impact of AI will be felt first in remote monitoring of cardiac patients, in imaging diagnostics as support for radiologists, and in preliminary diagnosis of a patient’s symptoms. In the case of this last application, there are a number of solutions, called chatbots or virtual assistants, whose aim is to gather information from an interview with the patient and recommend the next step, replacing “Dr. Google”. This type of solution is already being piloted by leading insurance companies, including Allianz, Bupa or Prudential.

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i n terviews Due to the powerful capabilities of data analysis, AI can be used successfully for preliminary assessment of disease symptoms. What direction will this develop in? Today, we are usually dealing with systems that suggest conditions based questions asked one after another. In the future, will other elements be included as well, such as real-time measurements of health parameters taken by wearables?

For a comprehensive and precise assessment of a patient’s health, answers to questions are not enough. After all, the patient’s clinical picture consists of a number of factors, such as treatment history, past diseases and medical procedures, test results, and also more subtle signals, such as the patient’s appearance, behavior, manner of speaking, and even the circumstances of the visit. Thus far no AI system has been able to aggregate all of these elements from various sources – from an electronic patient record to measurements taken by medical devices.

This is definitely the direction in which AI has to develop in order to come close to human competence.

In systems of this type, we still use terms like “symptom evaluation” and “preliminary health assessment”, rather than “diagnosis”. When will we be able to speak of actually diagnosing patients, e.g. with the help of an application? What conditions must be met?

Due to the legal environment, as well as the intended use of the available systems, I don’t think it will happen soon.

“Symptom checkers” are currently based on a certain portion of the information available to a doctor – they cannot see or hear the patient, assess the patient’s appearance and overall health, or perform a physical examination. The doctor often knows the patient and can extract

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much more information from the conversation than just a subjective assessment of symptoms. Not to mention the patient’s history and test results, which are essentially unavailable to an external application at this time. We won’t be able to speak of a diagnosis until we close the gap between what a doctor can see, hear and feel and the senses that a phone application has.

Should “symptom checker” AI systems be enriched with elements of emotional intelligence, so that – like a doctor or nurse – they will be able to monitor and respond appropriately to patient behavior, facial expressions, etc.?

Absolutely. AI systems, especially voice applications and avatars, should be enriched with elements of emotional intelligence. A friend of mine, an experienced doctor, once told me he can assess the health of a patient admitted to the emergency room with just one look. He said he first sees to the person sitting quietly in a corner and saying very little. Such a patient may no longer have the strength to communicate his pain. Moreover, the reaction to pain depends on many factors, including culture and race. Imitating emotional intelligence is a not trivial matter, and it will probably be years before we have data sets – photos, videos and behaviors – that will be able to train algorithms to do this.

The gap between the health needs of the growing and ageing population and medical human resources is growing steadily. This gap can be closed by AI systems.

As a society, long accustomed to the traditional model of medicine, are we ready for such a revolution? How can we persuade nurses and doctors who are afraid of being replaced by technology?

It seems to me that the biggest challenge is actually to implement AI without the need for a revolution. In the fantastic interview “Making the Right Choice the Easy Choice”, Roy Rosin, the chief innovation officer at the American hospital Penn Medicine, talks about how to improve patients’ health without changing their habits. For example, in Grand Rapids, Michigan, fluoride has been added to the tap water since 1945, because the residents had constant dental problems.

Eleven years later, tooth decay in children born after that year had decreased by 60%. I think it should be like this with

artificial intelligence. We should introduce it gently, without having to change the behavior and habits of patients, nurses and doctors.

Should AI systems in healthcare somehow be validated to ensure that they work in accordance with the best medical knowledge?

In contrast to classic medical devices, and even medicines, validation of AI systems presents completely new challenges. First of all, in the case of models built by machine learning methods, there is no simple pattern describing their behavior.

So how many cases do we have to verify to ensure that the system is safe to use? Second, when dealing with a system that changes its model over time, how often should the validation be repeated? Or maybe only the development methodology should be subject to validation?

Many people wonder about the security of such solutions. What guarantee does the patient have that his medical data are secure and that the analysis process itself will not be flawed, e.g. due to a cyberattack?

Privacy issues are not specific to AI. After all, our data are already stored in hospitals and clinics, at the dentist, and in banking systems. I think the reliability of the software provider and its technological facilities are crucial here. Are we convinced that a given company can protect our data and its infrastructure against an attack? Do we have to provide our personal data in order to take advantage of the solution? For example, none of our services collects any data that could identify the user, not even IP addresses. We also don’t require a login. At the end of the day, however, it’s a question of credibility – of whether our users believe that we really do what we say we do.

What development trends in Artificial Intelligence will become dominant in in the global healthcare market the coming years?

I believe that four areas will play a key role: remote patient monitoring and measurements of vital parameters, imaging diagnostics, patient and physician support in differential diagnosis, and the use of AI in personalized medicine for the selection of an individual treatment plan. 

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Digital disruption is not something post-apocalyptic “The data-driven economy and the art of data science are already shaping our culture by introducing more analytical, scientific approaches into our daily lives”, says Elena Poughia, the managing director of Dataconomy Media GmbH and the founder & head curator of Data Natives, Europe’s largest data science conference.

Big Data, digital technologies, digital healthcare, AI, Machine learning: what can we expect in 2019 and which tendencies will dominate?

I would say none of the above. I think 2019 is going to be the turning point for projects with a social impact. Big Data is a term loosely used to describe the proc-

ess of obtaining actionable insights from data – but do we really need big data or do we need smart data? To introduce digital technologies as a term in 2019 is outdated – everything that can be digitized will be digitized. Big pharma companies are making shy attempts to move to the cloud and utilize Artificial Intelligence (AI) and Machine Learning (ML)

to automatize all the processes available – I wish I could tell you that 2019 will be the year to introduce the “AI physician”, but this will be a form of evangelizing with sensational misinformation aimed at triggering civilians and doctors. Most doctors I know would probably argue that the only actual manifestation of big data in medicine is computer

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Into this space fits Healthbank. With an unique, neutral and independent platform, Healthbank enables people around the world to share their health data. In doing so, Healthbank promotes innovation in healthcare, from prevention to therapy, ensuring both better prices and quality healthcare, for the benefit of all individuals and society.

vision. The equipment that doctors operate now is far superior and certainly better assists them in diagnosing and curing patients. And to return to your question – I think that projects with social impact will dominate the market. Medicine is a service we do for humans, and that alone has a significance that we should honor in practice.

As we aim to be data-driven and oriented – to speak with numbers, according to a survey we conducted this month, 74.9% of our audience are interested in learning more about AI and ML whereas interest in sustainability projects is almost 3% higher than in healthtech projects.

What are the most promising applications for data technologies in healthcare?

Some of my favorite projects in healthcare include: • AIScope: a non-profit organization coming out of Barcelona with the aim of curing global diseases like malaria, tuberculosis and intestinal parasites in every isolated village with the help of artificial intelligence.

• Similarly, HippoAI Foundation is a non-profit organization aiming to democratize medical information for advancing and understanding the future of medicine through open source data, appears to be very promising.

• Medica’s 2018 App Competition’s second-place winner caught my eye at the FTR4H event in Dusseldorf – Tonic App helps medical doctors diagnose and treat their patients by curating for them, in smarter ways, the massively dispersed resources they need for their day-to-day work.

• Vivy: with Vivy everybody can digitally request and manage documents from a doctor or a lab. So all the medical data, such as medical reports, findings, lab values ​​or x-rays, are all on the mobile phone.

• Boost Thyroid: the BOOST Thyroid app is created by scientists and patients, for patients that enables people with an underactive thyroid and Hashimoto’s to take full control of their health. The goal is to use data to transform the current status quo of thyroid health and bring useful solutions to patients.

• Xbird: people are dying of preventable diseases every day. Symptoms and early warning signs are not recognized in time and the approach to disease pre-

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When and how will digital disruption in healthcare occur?

» Medicine is a service we do for humans, and that alone has a significance that we should honor in practice.«

vention is outdated. Technology can radically improve the chances of surviving preventable diseases. Through the power of data, Xbird is bringing the future of health management into the present day.

Blockchain has been a term toyed by many companies in the healthcare space, mainly due to its potential to secure payment transactions and administer expenses, to securely monitor the supply chain of drugs or to provide a healthcare infrastructure for women in rural areas.

Digital disruption is not something postapocalyptic – there is no clear point in time for an eruption or a turning point to take place. Healthcare systems are fortress infrastructures, carefully built to allow little to no infraction. They are systems carefully regulated with many layers – for example, unless you work in a teaching hospital it is rather difficult to obtain data, and with no data there is little to no work. Data is the quintessential element for any scientist in order to make progress and publish.

How will the data driven economy change our lives in the future?

The data-driven economy and the art of data science are already shaping our culture by introducing more analytical, scientific approaches into our daily lives.

From quantifying and measuring ourselves to responding and solving problems with a data driven approach; certainly, AI is shaping our lives by changing our jobs and automating manual and repetitive labor, giving us the space and the time to be creative, educate ourselves and think outside the box in order to interpret results. The use of smart machines guided and interpreted by humans is creating a culture of prevention rather than cure, which will lead to longer lasting and more enjoyable lives.  Elena studied economics at Durham University in the UK and afterwards earned a degree in modern and contemporary art from Glasgow University. She has worked for internationally renowned art institutions such as the Gagosian Gallery and the Athens Biennale. During this time she also co-founded the Fasma Festival of Arts and successfully launched her independent art publication Dialogos. Continuing with this entrepreneurial spirit, together with her team, she developed Data Natives into an important meeting point for experts from the fields of data science, data analytics and machine learning. In addition, Elena is particularly committed to supporting women by helping them become established in tech professions (photo: Handelsblatt).

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Digital health literacy is an essential capacity to master in everyday life Kristine Sørensen is a new member of the ICT&Health Editorial Board. She is the founding director of the Global Health Literacy Academy and the first President of the International Health Literacy Association and Executive Chair of Health Literacy Europe. As a thought leader, Kristine is committed to advance the global scope of health literacy.

When and why you oriented your interests towards health literacy?

Previously, I was working at Maastricht University in the Netherlands, and I was invited to coordinate the European Health Literacy Project between 2009 and 2012. A decade ago, it was like con-

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quering new scientific frontiers as we were only a small group of people working on health literacy in Europe. I saw an immediate opportunity to combine my keen interests in developing citizens’ fullest potential through health and wellbeing with the need to bridge the inequality gap associated with health in Europe and beyond.

make new technologies so easy to operate that they get adopted quickly?

What is the definition of “health literacy” and what does it exactly mean?

Health literacy is closely linked to literacy entailing the knowledge, motivation, and competencies to access, understand, appraise and apply information to form a judgment and make decisions regarding healthcare, disease prevention and health promotion to promote quality of life during the life course. On the one hand, we focus on the ability of people and patients to manage their health and navigate in the systems. On the other hand, we wish to ensure that health providers of all sorts make it easy for people and patients to find, understand, evaluate, and use information and services.

The health system itself should never be a barrier for people to get timely and appropriate help to solve their health problems.

» Recently, Finland offered digital skills courses to its citizens.«

What can countries do to strengthen the health literacy and digital health literacy capacities of the citizens?

Could you please illustrate the state of health literacy in Europe?

When we surveyed eight countries in the European Health Literacy Survey, it came as a surprise that limited health literacy was a neglected public health challenge in Europe. In the “best” country, the Netherlands, almost 30% of the respondents found it challenging to handle health information and manage their health needs. In some countries, it was more than double. New data from Denmark shows that 10% of the citizens find it very difficult, and 30% are challenged.

Despite welfare societies with sound educational systems and good healthcare systems, the European Health Literacy Survey showed us that there was room for improvement.

What is digital health literacy?

Generally, digital literacy includes the capabilities that fit someone for living, learning, working, participating and thriving in a digital society and digital health literacy or eHealth literacy, concerns the specific competencies needed

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In the future, we must find the best balance between upgrading ourselves to the new digital reality and shaping societal systems to be user-friendly to leave no one behind. Recently, Finland offered digital skills courses to its citizens and challenged the other Nordic countries to follow their example. Facing the technological advancements by setting ambitious political goals to equip citizens with the best possible foundation is admirable. The same should be the case for organizations working in the field of health. I am convinced that health literacy will be a high-value performance indicator in the future. Already now, we see increased demand, so far, mostly in the US, where providers pursue health literacy as a professional skill and recruit staff specifically due to their health literacyrelated skills.

to manage health and wellbeing in the brave new world of technological opportunities.

How important is it in the era of technological transformation of healthcare we experience nowadays?

Digital health literacy is an essential capacity to master in our everyday life where we are confronted with health systems being digitalized to a higher and higher degree; where the information load is overwhelming and challenging to sort out and comprehend; and where personalized solutions require informed decision-making to a broader extent than previously.

What is more critical: to teach people how to operate new technologies or to

Countries across the world are engaged in the creation of health literate societies. Germany, Norway, Portugal, and Scotland have developed health literacy strategies to push the momentum, and Austria prioritizes health literacy as one of 10 national health goals. In Denmark, the investment in health literacy is made by the governance in municipalities and the regions where interventions and coproduction of health in local communities take place. Regarding digital health literacy, I believe Estonia and Taiwan are frontrunners with ambitious user-friendly systems. We need to realize and accept that digital and analog services still go hand in hand if we genuinely wish to accommodate citizens’ needs. The human factor still beats digitalization when it comes to critical thinking, creativity, and personal communication. Machines and big data can teach us about the past, yet, we humans build the future.  Kristine Sørensen is a member of the World Health Organization Technical Advisory Group on Health Promotion in the SDGs. She has been a health literacy advisor to the European Commission, the European Centre of Disease Control, the European Parliament, the European Council, and McKinsey. Her educational background is in medicine, public health, and global health diplomacy.

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For patients, wearables are fantastic tools to manage health and well-being On his 8th birthday, Aron Anderson received his first chemotherapy treatment against cancer located in the lower part of his spine. There was pain, suffering and tears. After the surgery, first he had to lie still in a bed for 6 weeks, then he wasn’t allowed to sit down for an entire year. He couldn’t walk anymore. After weeks in hospital came the next challenge: getting back to daily life. He has gained confidence again through sport. Self-tracking also helped him to take control of his health and to improve his physical performance. His perspective on being a patient in today’s health systems is a great lesson.

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i n terviews How did you become a patient? What happened?

When I was 7 years old I got this strange pain in my bottom from sitting down. After about two weeks of pain I had an Xray that found of tumor the size of an apple growing in my lower back. This was followed by a long year full of chemo and radiation without much success. I eventually had surgery to remove the cancer which also meant cutting a lot of nerves going to my legs so since that day I’ve been in a wheelchair.

Once you said “the healthcare system was able to cure you but didn’t make you healthy”. What do you mean by that?

The Swedish healthcare system cured my cancer. First when I had it in my lower back and also when it came back three times in my lungs. But going through chemo, radiation and surgeries isn’t really health. When I was about 20 I was training extremely hard to become the best in the world at athletics but I was sick all the time with colds; if the flu was going around I was sure to catch it and I also had some really bad acne. This led me on a path to regain my true vibrant health and not “just” to be free from cancer.

What doesn’t work well in healthcare systems? What kind of patients’ needs don’t they address?

This is a very wide question but from my point of view the health care system is excellent when it comes to curing acute conditions such as heart attacks, broken bones and cancer. What it’s lacking is helping people actually achieve great health. Studies today show that 90-95% of cancer in adults is related to lifestyle and environmental issues. I truly believe that we have to start addressing these issues and teaching people how everybody can become the healthiest version of themselves.

You were discharged from hospital, you come back home and what was your biggest fear to start „post-hospital life”?

What was most challenging?

My biggest fear was wheelchairs! I have some function in my legs so in the beginning I was using a walker to move around. This made me feel “normal” since I could walk. I was incredibly

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» Wearables are fantastic tools to quantify how we feel.« scared of ending up in a wheelchair since I was certain that would make me disabled. I was at camp when one of the instructors pretty much forced me to try her chair. That moment changed my life and truly gave me back my freedom.

When was the first time you had the idea of trying some digital health solutions?

What was it exactly?

When I first tried to regain my health I was extremely confused because there was one book telling me to eat carbs, one telling me to not eat carbs and so on.

Who was I to believe and what would be true for my biology?

I started testing a lot of different diets to see what would work for me, which was a really slow and painful process. In the last 6-7 years more and more digit-

al solutions have become available and I remember measuring my heart rate variability (HVR) for the first time about 7 years ago. I did it every morning and it took about 10 minutes and I had to wear a heart rate strap. I did it for a while but eventually tired since it was too much hassle. Today I get the same value automatically in my phone when I wake up from my Oura Ring which records my HRV value when I sleep.

I’ve always believed in digital solutions to quantify my health and get real numbers. I truly like that saying “What you measure will improve” so this is my was of improving my health and fitness.

What devices / innovations are you using in your daily life? How do they help you?

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I love devices and gadgets so I use quite a few different ones but my favourites are: • The Oura Ring – which is a ring (not much bigger than my wedding ring) that I wear when I sleep. The ring has a heart rate sensor and accelerometer so it tracks my night time heartrate, Heart rate variability and sleep. So every morning when I wake up I get my “readiness score” which pretty much tells me how hard I can go that day or if I need rest.

• DNA-testing. I’ve used 23andme. com and DNAfit.com to analyse my DNA and I’ve found really useful things such as that I’m sensitive to carbs and that I’m slow at metabolising caffeine.

This has let me to make changes such as eating a diet higher in fats and lower in carbs and restricting my coffee intake to the mornings to not affect my sleep.

• The Muse2 meditation band – I’ve always wanted to meditate but been really bad at it. The Muse meditation headband helps me meditate and tells me when I’m actually doing the right thing.

Which has made me calmer and more focused.

big goal is to be the first paraplegic climber to reach the summit of Mt Everest.

How can tools like wearables support patients? Why do you find them helpful?

How do they changed your life?

If you could address one message to those who are responsible for health policies, what changes would you suggest?

Everyday I’m collecting a whole bunch of data about how my body is doing and reacting to different stressors. For us patients, I think wearables are fantastic tools to quantify how we feel and see what our numbers say when we make changes in our routine with diet, exercise and sleep. My favourite benefit I got from my wearables right now is HRV that is a great tool for tracking stress in my nervous system and seeing how well recovered my body is.

Start thinking long term! Most healthcare systems don’t have a clear incentive to think long term, but if we don’t do this we will have a massive problem in 10, 20 or 30 years. We have to teach people to achieve health through a lifestyle that supports their genes with healthy diet, exercise and sleep. Healthcare is not about curing diseases it’s about avoiding them in the first place!” 

You are overcoming further challenges in your life. What is the next one?

Yes, I’ve overcome quite a few big challenges in the last few years such as Iron Man triathlons and climbing mountains. My next

From the author: I met Aron during the Symposium on the Future of Digital Health Systems in the European Region (Copenhagen, 68 February 2019) where he gave an inspirational speech. He’s taken on challenges that most people wouldn’t even dream of. His strong will allowed him to overcome many adversities and hard times. Wearables are only one of the tools to make patients life better but every tool matters. Visit Aron’s website: aronanderson.se/en

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Components of digitalization: evidence, knowledge and technology The Norwegian Centre for E-health Research (NSE) has an ambitious goal: To accelerate the development and implementation of policy on eHealth. But how to transform health care services while placing patients at its center? How to create efficient national digital health solutions? An interview with Stein Olav Skrøvseth, Director of the NSE. 74

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i n terviews What is the main purpose for founding The Norwegian Centre for E-health Research? Norway already had quite a strong focus on digital health, also supported by The Norwegian Directorate of eHealth (NDE), a sub-ordinate institution of our Ministry of Health and Care Services.

The Norwegian Centre for E-health Research was established at the same time as NDE, and its role is to produce, gather and disseminate knowledge that is relevant to the development of the e-health field in a broad sense for national and international stakeholders. There are several large national initiatives in e-health, and it is important to have sound and research-based knowledge to inform these processes. The national goals are formulated in the white paper “One citizen – one health record” from 2012, with three overarching goals relating to health personnel and patients’ access to digital services, and data availability.

One of the goals of the NSE is to „collect, produce and disseminate knowledge required by the authorities to develop and implement a knowledge-based policy on e-health”. How is it implemented in practice? What has been achieved so far?

NSE works closely with national agencies in national projects where we follow their development, carry out evaluation research, and provide knowledge for the projects in both the planning and execution phases. We work on transforming research results into accessible and communicable knowledge that is relevant for ongoing national projects as well as research into specific areas that are of broad relevance for the e-health field.

Another research area is „personal health systems and welfare technology”. What are the pillars of „personal health systems”?

Patients and citizens are a strong voice in the health care systems, and many people with chronic conditions and their dependents are managing their own conditions at home. Personal health systems are technological solutions that enable good living at home. This includes welfare technology, mobile systems, and connected solutions that either monitor the condition, enable self-management or technologies that enable a proactive healthcare service.

citizen – one health record” where information is available to all that need it in safe and secure ways independently of the organizational structure, as well as data being available for research, governance and quality assurance.

» Trust is essential in health care.

Digital security is an important component of building trust.«

Which aspects in healthcare systems that are facing many new difficulties can be reconstructed and improved using ICT?

ICT is not a goal in itself, but a means to provide improved health care for citizens. As such ICT can transform most aspects of healthcare. One example is the area of artificial intelligence, that enables the reuse of data for the creation of a learning healthcare system that learns from its own data and can make healthcare into a continuously improving system.

What kind of digital health solutions are already available for citizens in Norway?

What is the vision of digital healthcare in the future?

Patients and citizens are using technology every day, and that includes technology related to health, for example through mobile apps. Although these are rarely integrated into public healthcare, they are digital health solutions actively in use. From the public side, all citizens have access to the public health portal helsenorge.no, where many have access to their own health information including reading their own health record. The political vision for the future is outlined in the aforementioned white paper “One

Which obstacles to implementing innovative tech solutions in healthcare are most challenging and how can we address them?

This is a very hard question, and not easily solved. There are many good and innovative solutions that do get implemented, but often do not scale. It is necessary to have a foundation in terms of interoperability and standardization.

One of the barriers that delays the implementation of digital health tools are safety barriers. How can we ensure balance between data security issues and digitally-friendly policies and gain trust for digitalization among citizens?

Trust is essential in health care, and digital security is an important component of building trust. Today technological solutions exist that ensure data privacy at the same time as being able to analyze data across different institutions. Trust is a matter of personal interaction, but security of data is an essential component of building trust, and we must use technological solutions that enables both security and at the same time enables data sharing when necessary.

During the Symposium „Future of digital health systems in the European Region” you mentioned three words that define health system of the future: trustworthy health systems, equal access and integrated health system. Why are these components crucial in your opinion?

Many patients and citizens today experience a health system that is fragmented where the patient should experience one health care service rather than a set of different parts. Also, health care is not equally distributed but depending on geography, economy and many other factors. Equal access to services independently of where you live should be a vision for future healthcare. Trust, fairness and integrated healthcare may seem obvious, at the same time as it is far from the reality many places. These words do not address technology directly, but technology is an enabler for all. 

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AI will help surgeons to orchestrate the work and data Digital transformation enters physicians’ everyday life, and Augmented Reality is becoming the third hand of a surgeon. How will the operating room change? I talked to Rafael J. Grossmann, healthcare futurist & innovator, the first doctor to ever use Google Glass during live surgery.

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i n terviews Many technologies are entering hospitals. Which of them will significantly influence the surgeries?

It’s hard to say which technologies will certainly be usable and which of them will make a difference in a field of surgery. But I would dare to say that Artificial Intelligence (AI), Virtual Reality (VR) and Augmented Reality (AR) will become very disruptive. Within the next three to five years, AI algorithms will guide all the flow in the operating room, but they will also accompany physicians/ patients before and after the surgery.

Same regarding VR and AR. Augmented reality will allow us to have access to data in a more convenient way.

We will be able to interact with diagnostic tools in ways that we haven’t seen yet.

Devices like Hololens or Magic Leap will enable us to view and analyse graphic images just in front of our eyes, without the need to use hands.

Let consider X-Ray viewings. Today we need a few computers in the operating rooms. This has to change. We can use more comfortable wearables and headsets, which gives quick access to data we are looking for. Such things will transform the way the surgeries are carried out.

Surgery requires the highest concentration. Won’t innovations like Virtual/Augmented/Mixed Reality or smart glasses distract a doctor?

» Human doctors will have to team up with AI to make medicine more precise.« trust must be a part of human-AI cooperation.

Who will lead the surgery in the future: a doctor or AI?

Along with how AI becomes a source of reliable expertise, we should also take its recommendations into account. But in the end, it’s a human physician who makes a final decision. It has to be healthy interaction between human and AI. It only happens when AI is created following the best practices, when the algorithms are fed with high-quality data, and when doctors are well educated to know how to use the potential of AI-based solutions.

So the last word always belongs to a human because the human is a creator of AI, and this is a tool like any other.

Medical students don’t have access to the education that includes AI, VR o AR. There is a growing gap between technologies and skills.

We have to redesign and disrupt the way we organise education these days. There are already a few great examples of digital healthcare programs or curriculum.

We need to train both students and residents differently, making them aware of how all these changes and exponential technologies that are affecting the way we practise healthcare today, and how we will practice healthcare in the future.

That is one of the reasons why I’m always seeking opportunities to share my knowledge, consult or advise.

Your favourite innovation that you’ve discovered recently is...

All the things that are combining new computing possibilities like AI, VR and AR – they will redefine how we teach, do surgeries and healthcare in general.

One of the great examples is Brainab’s platform and software – viewed in MagicLeap’s hardware – that bring simplicity to the operation room and enhance data management, improving the workflow, and potentially, patient outcomes. 

I think that the best surgeon or the best physician is the ones who are augmented with the AI algorithms. There is nothing better than human and AI working together. Already today, we are distracted by – for example – digital platforms, different technologies. Enhancing and facilitating how we interact with those technologies will make our work more ergonomic and simpler.

Would you trust an AI-based system that suggests completely different steps in surgery that your experience tells you?

We will have to learn to live and work in the times of AI, AR and VR. Just like today, we also get support from different entities and procedure. Thus, we have standards, protocols, guidelines, colleges, physicians’ societies. It all helps us to make decisions. And we follow them even if we don’t know how some of the instructions have been made. The same

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Foto: TEDxBerlin, 2019 Sebastian Gabsch

Data For All. Not For Sale Data can exclusively serve big tech companies to increase their profits; it can also accelerate research and strengthen science for the good of us all. Interview with Bart de Witte, a digital health leader, and the founder of HIPPO AI – a NGO focused on open source medical data.

AI is already becoming better than a doctor at diagnosing, predicting and preventing diseases. How to convince doctors to create a team with algorithms? Is the conflict between “new, better, datadriven” and “old, traditional, intuitionbased” avoidable?

We should focus on outcome-based care, where AI is the “Intel inside” of most transactions. Combined with a humancentric design, so the doctor sees less data, it would accelerate the adoption of digital health. Doctors could make faster, and hopefully, more accurate deci-

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sions. From my experience, most physicians do not fear AI because the amount of medical knowledge is growing faster then they can handle.

Secondly, there is less difference between intuition and mathematics as one might think. Daniel Kahneman wrote in the book “Thinking slow, thinking fast,” that there are two basic systems that doctors use to solve problems. System One is fast and intuitive. In this system, doctors rely on pattern recognition to make quick decisions. Healthcare is full of System One thinking, which includes cognitive biases. For example, one of the bias-

es among physicians is over-confidence, which Kahneman calls “endemic in medicine.” He gives an example of a study that determined physician confidence in their diagnosis. Next, he compared the cause of deaths, as ascertained by autopsy, with the diagnosis the physician had made before the patient died. Clinicians who were utterly sure of the diagnosis antemortem were wrong 40% of the time.

There are practically no winning arguments for focussing solely on humans competences. Machines can process information faster, and with the support of the neural networks can detect much more complex patterns. If we allow computers to help doctors to diagnose, they could use the saved time to focus on human factors, such as compassion and kindness. The Stanford University Center for Compassion and Altruism Research and Education ana-

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lyzed the impact of compassion on the quality of healthcare. Patients who feel comfortable with their care provider are willing to share more information about their health, making diagnosis more accurate. The research showed that compassionate healthcare environments are found to be more effective than aspirin in preventing heart attacks or stopping smoking habits Human kindness, science, and technology form a powerful combination that can fight diseases, create a friendly environment for healing, and by doing so, decrease healthcare costs.

What does the development of selflearning algorithms mean for healthcare and humanity?

Without going in all the different domains, where AI is already delivering impressive results, the most significant impact of AI will be in the new insights that it will create by detecting patterns out of the phenotype and genotype data. As a result, we can develop new biomarkers and a much better understanding of the disease pathways. Potentially, we will find ways to prevent most systemic diseases (e.g., cancer). It’s an enormous opportunity for humanity if we implement technological progress into society and make it available for all. According to Hippocrates, a society where people are physically and mentally healthy is coherent and organic. A society where people are suffering from physical and mental diseases is fragmented and mechanical. Although AI is a two-edged sword, on the positive side, AI allows us to humanize healthcare again.

You are a founder of the HIPPO AI Foundation which aims at open sourcing medical AI. How do you want to convince different stakeholders with different interests to share medical data or knowledge instead of making profits from data?

The more I work in healthcare, the more I realize that inequality is a major issue.

It’s not only about the gap between lowand middle-income or higher-income countries, but also differences in health outcomes between population groups.

Your social conditions, where you are born, where you grew up, where you live, where you work and where you age, and which people you are connected to, determine your health status.

It took me a while to understand that

» We should ask ourselves the question, what we can and should do, so our children, and we benefit most from AI in medicine and inequalities will be reduced.« the corporate structure that I was working for wasn’t the right model. If AI is here to serve us and will influence our decisions when it comes to health, medical AI should not be owned by any organization that operates for a financial interest first. Similarly, if a government or a public institution will hold medical AI, it can always be used to support the political interest. That’s why all of us should be the owners.

People who doubt it probably might have forgotten that before the enlightenment in medieval times, and from a cultural perspective, the disease was often seen as a punishment by God for sins.

Literacy and access to knowledge have always been associated with power. Similarly, centralizing expertise in the few single organizations means that we allow people to gain control based on healthcare needs. Having ownership of exclusive knowledge is nothing new in healthcare, but until now, it is limited to pharma.

The question we need to ask is: Do we trust these organizations to decrease inequalities in healthcare? Perhaps the answer is yes, but then the second question we need to ask is: Do we allow them to privatize knowledge, something that always has been a public good?

Digital health enthusiasts provide future visions of efficient healthcare, patientcentered and AI-based medicine, empowerment of the patient and prevention-oriented systems instead of hospital-oriented strategies. How far are we from those dreams?

I think as we progress towards a digital healthcare system and patient centricity will become inevitable. As mentioned before, digital health solutions, if designed well, use a human-centered design. Most healthcare systems today are designed around the provider. While I was part of IBM Germany, we made a bold move together with the largest single health insurance sector, by pushing a PHR model, where the patient has ac-

cess to his data, and digital services can be integrated and embedded in the digital patient journey. Neither IBM nor the Insurance had access to the two-fold encrypted personal data. This move was very different as the one that was driven by the government and focussed on a provider’s centric approach, which did not deliver any results in 15 years. Other insurers followed, and today, there are two major PHR providers.

70% of people worldwide population has no access to healthcare services. Digitalization can make healthcare accessible and affordable for everyone, which is also one of the WHO’s goals. But there are more factors to be taken into account like digital literacy, for example. How to avoid the digital divide in society?

Alvin Toffler, a futurist and author, predicted that the illiterates of this century are not those who can not read or write, but those who can not learn, unlearn and relearn. There are already many illiterates out there. From my perspective, the current digital illiterates are to be found within the older generations that are often also responsible for policy making and fear the unknown. I believe digital literacy is a crucial issue for those who are accountable for leading the transformation.

That’s the reason I recently founded the DHealth Academy with Prof. Dr. David Matusiewicz, who is a health economist. We want to close the divide by educating the stakeholders within the system, so they become digital leaders, with a “digital-first” mindset. We aim to educate 100.000 people within the next five years. It might sound high, but compared to the over 4.4 Million people working in the industry, it is not a lot.

To come back to the question on Global Health and making healthcare accessible to all. Back in 1989, I wrote a high school paper on Artificial Intelligence.

I described how it could be used in medicine to help doctors to make a better diagnosis, and save lives. I found the idea

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» Illiterates of this century are not those who can not read or write, but those who can not learn, unlearn and relearn.« of AI integrated into our lives fascinating. With the support of AI, general practitioners in developing countries could become super-doctors.

One of the main problems with access to healthcare services is the lack of physicians and trained workers. Nigeria would need 300 years and use 20% of their GDP to educate as many physicians to come to the average OECD standard. With AI quickly becoming more accurate, there is this massive opportunity to decrease all inequalities and cross the gap between high and mid-, low-income countries.

There is also an enormous opportunity for the less developed countries to leapfrog.

In China, for example, Ping An a Good Doctor has rolled out 1000 One-minute Clinics across eight provinces and cities.

The clinic looks like a photo booth. The patients communicate with an AI doctor and can get diagnosed. Algorithms can recognize around 2000 diseases. Currently, there is still a human physician involved who reviews the AI decisions. As the system scales and learns, what is only a matter of time, the health kiosks will get fully automated. Every One-minute Clinic has more than 100 drugs in stock. All of them are cryogenically refrigerated to ensure their quality. If a user needs a medication that is not available at the booth, he or she can purchase it online through the Ping An Good Doctor App and enjoy the one-hour drug delivery services provided by local pharmacies.

We will see more of these innovative care delivery models in the region with doctors shortages. In Europe, there will be a lot of resistance to change.

How to change the mid-shift from focusing on gadgets, innovations, startups – which are also necessary but sometimes overhyped – to new Europe’s strategy, new moonshot for healthcare to build solutions based on AI and accessible for all?

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When it comes to healthcare, Europe has a much different value set as the US. It was recently very well documented in a review of a Clinical Information System from a US provider in Denmark.

The Danish Healthcare system’s culture is based on trust and consensus and differs a lot from the US, where the focus is on risk and compliance.

AI will probably influence nearly every single decision that our caregivers or we make when it comes to preventing, diagnosing, and curing diseases. Values, human rights, and ethics will become extremely important. In Europe, having access to health is a human right. Which means we have to build AI that follows this principle.

But from a patient perspective, it might feel like AI is shaping us, not opposite. We should ask ourselves the question, what we can and should do, so our children, and we benefit most from AI in medicine and inequalities will be reduced. For the last couple of years, I have been discussing this question with researchers, executives, investors, and politicians, and even with the EU Commissioner who was responsible for the digital agenda in Europe. Although a lot of work has been and is being done around ethics and medicine, nobody could give me a satisfying answer. That’s why I started an NGO, that follows all the principles important in Europe. Europe still leads when it comes to having access to proper public research; we are a continent that focusses on spreading equality, on

cooperation, and open science. W want our healthcare systems to be sustainable. These are all the principles HIPPO AI bases on.

Many tech companies, like Apple or Amazon, are entering healthcare. Is it a chance or a threat?

That is a threat.

Amazon, Google, Tencent, Alibaba are companies that understand very well how to build data-driven business models. Data is that what helped Amazon build an algorithm that allows storing products I want to buy tomorrow, close to my home so that they can deliver it on the same day. It makes them extremely competitive.

Google with Google Search has proven that it understands how to create and scale data science-based services that help people to find better – like anywhere else – results.

Both companies are monopolies that have been dominating their market for more as a decade. They made our lives easier, but at the same time also created dependencies. Peter Thiel, a famous Silicon Valley investor, wrote in his book “Zero to One,” a monopoly is not a disease; competition is for losers. It’s the exact opposite of European healthcare systems values. We need to seriously ask if we want to inject these values into our healthcare systems and if we want data and medical knowledge to become privatized and monetized. 

“Who is gonna own the technology? Medical AI should be a common good,” claims Bart de Witte

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How to build a smart hospital?

Prof. Dr Jochen A. Werner, the CEO of the University Hospital Essen (Germany), wants to design and form a place where technologies address specific problems, following the principle: putting the peoples’ concerns in the centre. By combining strong leadership, clear vision, innovation-driven organisational culture and right mindset of all workers, he has already created one of the most innovative hospitals in Europe.

What does for you an “innovative hospital” mean?

We used to call an innovative hospital a “Smart Hospital” since a cross-linked platform within the public healthcare system has been launched at the University Hospital Essen. This unique communication platform enables close cooperation between a classical clinical medicine, registered doctors, rehabilitation facilities and pharmacies. Besides, from our point of view, a smart hospital must represent values like humanity, progress, medical quality and innovative approach.

Economic success also should be included. The clinic walls do not determine this, but the patient outcomes do.

Digitalization also positively influences the workflow of our employees, especially of the nurses. The recently established electronic patient file, as well as the future support of robotics, is aimed at the goal: reducing the time spent on documentation and all the administrative tasks that do not affect the patients’ care directly. This unlocks more time for communication so patients can share their fears and problems with doctors.

So, an innovative hospital’s intention is the well-being of patients, their relatives and the employees.

How to create a digital-oriented mindset in a healthcare setting, so all the doctors, nurses and medical workers are involved in the change process?

Probably the main challenge on the way to becoming a smart hospital is a sustainable cultural change. The new model of the clinic requires from all our employees an entirely different mindset. In short, we need a new way of thinking and acting, less hierarchy and more interaction and cooperation between different peer groups within the hospital. A new category of doctors is needed: specialists who are not captured in their discipline, but who stay open to all other external knowledge and ability to communicate across professional borders.

Interaction and dialogue are the keys.

One the one hand, we keep informing all our employees using a whole range of analogue and digital communication instruments such as magazines, videos, newsletters, intranet, and so on. On the other hand, we are focusing principally on our new co-workers to make them the ambassadors and opinion leaders of the digital transformation. To do so, at the beginning of every month, we perform an extensive, four-day-long introduction session. It aims not only at edu-

cating and enabling our new employees to start their work smoothly but also at explaining them the idea and the spirit of our Smart Hospital project.

But one thing has to be admitted: every change process is a long road – it is not only about changing people’s mind but their behaviour. Our transformation towards becoming a Smart Hospital is an ambitious, probably never-ending process.

During my career, I have noticed that only doing, not talking, can change the reality around.

What does drive your motivation to apply digital innovations at the hospital you run?

In my former function as a practising physician, but also my current position as a medical director, I have strived for one main aim: improving the lives of people, improving the lives of my patients. During my career, I have noticed that only doing, not talking, can change the reality around. So many good ideas have not been implemented because there was nobody to turn a concept into result. In this case, even the best approach will remain useless. Discipline and hard work determine the success of every project. So,

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» The main challenge on the way to becoming a smart hospital is a sustainable cultural change.« my motivation is based on my personal experience: digital innovations are the prerequisite for improving medicine and healthcare. But the accomplishment is the only way to make real progress.

Are the medical workers ready for the digital transformation of healthcare?

Medicine and healthcare, in general, are currently facing the most dramatic change in the history of humankind.

Thus, most medical workers cannot be ready for digital transformation. We also have to ask ourselves, what does it mean “to be prepared for the digital transformation”? As mentioned before, it does not only imply being able to work with modern medical devices or to use algorithms. It signifies a new kind of communication and interaction between all relevant groups and disciplines inside and outside the hospital.

We have to teach the young physicians adequately at the universities, continuing education when they start to work at the hospitals. They should be able to use all the new medical opportunities arising from the digitalisation. Furthermore, they should introduce a new way of communication in clinical structures. At the same time, we have to motivate the experienced physicians so we will take advantage of their knowledge and expertise. Only all generations and disciplines working closely together can successfully master the long way to the innovative Smart Hospital.

In which areas of healthcare should we invest in?

I want to mention three segments: IT-Infrastructure. It is the basis of all digital applications. At the University Hospital Essen, we spend twice more than the market average in the technical equipment and hardware, as well as in highly qualified IT-experts.

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Artificial Intelligence. We use AI for several tasks, among them to learn algorithms to define the age of bones, predict the probability of metastasis or to diagnose diseases of the lung. Prospectively, Artificial Intelligence and digitalisation will significantly contribute to the treatment of complex diseases. I want to mention cancer, rare diseases and all conditions, where a vast amount of data can be gathered and interpreted.

Our employees. Every change process starts with the employees. We invest continuously in human resources to enable our doctors and nurses to work in a digitalised working environment. For example, by using our new electronic patient file, as well as keeping them in touch with all developments within the framework of becoming a Smart Hospital.

We all know that digitalisation in healthcare faces many legal and financial challenges. Some changes will take years.

What can hospital managers do now to create an innovative hospital?

Just doing what their job is: making the right decisions and follow a clear strategy. From my perspective and knowledge, some hospital managers are acting halfhearted. Indeed, they anticipate the challenges of digitalisation, but – due to several reasons – cannot respond appropriately. That may have a lot to do with a conservative structure of many hospitals and especially with their physicians’ attitude, but also severe legislative conditions or budget limitations. The result is what we see now in many hospitals: a lot of siloed digital projects without a consistent strategy, and finally – a waste of time and money. On the other hand, in every country and all over the world there are a lot of good examples of how digitalised hospitals can combine medical performance and, at least in the long run, economic success. But that requires courage and a clear vision of where to go.

The hospital of the future is not driven by the reign of bits and bytes, but use them for a much more human-oriented medicine.

Some hospitals are leading in digital transformation, some are lagging behind.

If we assume that they have the same financial resources, what are the most significant differences between them which determine the level of digital maturity?

From my point of view, there is only one significant difference: the strength of conviction, faith in success. Most hospitals implement a variety of several digital projects, but often without a strategic approach. At the University Hospital Essen, we always have the “big picture” in mind to entirely transform a traditional top-notch University Hospital into a fully digitalised, patients– and employeesoriented organisation. So, also our medical and entrepreneurial strategy follows the approach: recruiting the young digital and medical talents, discussing and approving all investments in medical equipment and infrastructure with the clinical directors. We enable our employees to become leaders in their working places. We are firmly following the primary strategy of becoming a Smart Hospital instead of doing a couple of incoherent projects. It makes the difference!

What digital solutions have inspired you most recently?

There are a lot of them. I am always inspired by new solutions and applications which provide real help and improvement in my life, for example, referring to communication or mobility. An excellent example of such a helpful tool in the medical setting is the Ada app, created by a German physician. With the help of algorithms, Ada aims to enable patients from all over the world to get access to personalised medicine. People can type in their symptoms and get back a health assessment, including a hint on what to do next. This is crucial mainly in regions with an undeveloped healthcare system.

Ada is a very inspiring example of how digital solutions can help people immediately.

What is your vision of the hospital in the future?

A hospital that addresses the needs and expectations of the patients, their relatives and the employees; a place entirely focused on the well-being of all mentioned groups.

The “Smart Hospital” is an answer to the challenges that healthcare is facing right now: digitalisation, demographical transition in the industrialised countries and limited financial resources. The hospital of the future is not driven by the reign of bits and bytes, but use them for a much more human-oriented medicine. 

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Digital health needs to be embedded in the conception of the health system We talked with Denise Silber, founder of Doctors 2.0 & You and a global thought leader, about the present and future of digital health innovations and ethics. Learn why Denise hopes these technologies can make healthcare more human.

What are your biggest hopes regarding digital health?

This is a tough question because high hopes for digital health or eHealth have been expressed for so many years by so many people. My hope is that digital health can be used to act on priorities like reducing medical error and fraud, providing equal access to quality care., and facilitating the patient-physician relationship.

Unfortunately, at the same time, not all about digital is positive. Digital systems can introduce errors. Hackers target health data. Many doctors spend more time on electronic medical records than on communicating with the patient. Silicon Valley is no longer as highly regarded, etc.

Which issues of technological transformation should be discussed more often?

Here are some of the main ones.

– The time required to do clinical trials and satisfy regulatory requirements versus the life cycle of the technology;

– The excess of choice that comes from too many innovations, both complicating interoperability and wasting resources; – The non-alignment of incentives amongst the different stakeholders which slows down the distribution of innovation; – Our difficulty in developing a truly new vision for healthcare. If I take an example from consumer products, a well-funded startup called Quibi has a new vision for culture: that while cinema and tv have each generated their media (films and tv shows), smartphones have not. So they’re developing a new medium for smartphones, the short-form video that can be watched vertically and horizontally. Entertainment is not as complex or critical to us as healthcare, but it’s interesting to note that this company proposes a new vision.

What is “ethical digital innovation” in healthcare?

Ethical digital innovation is, so far, a dream goal, as is truly ethical healthcare.

But we are trying hard to achieve that.

Such an innovation would be: – conceived for the benefit of the patient, – accessible to all patients, – fully personalized, – based on representative data, – carbon-neutral.

And let’s add the five principles cited in a Nature publication and which are embedded in my first criterion “for the benefit of the patient”: transparency, justice

and fairness, non-maleficence, responsibility, and privacy.

What’s your opinion about the market of wearables today?

The wearables market is growing and currently led by devices that track fitness and wellbeing, such as watches, which account for around half the market.

Google purchased Fitbit in November 2019, after previously purchasing Fossil for its smartwatch technology. Google’s actions are a solid, short-term sign of potential, although Google can withdraw in the future as easily as it entered. Sleep tracking is also growing. Both fitness and sleep are essential to preventive health.

On the medical side, interest in the EKG-related applications of connected objects has matured, although the value of the identification of largescale, symptomless cardiac irregularities has not been demonstrated. Cardiology lends itself particularly to tracking, and there are so many medical segments with connected objects that we can’t mention them all here! Such listings are the bread and butter of the companies that sell market research reports!

Could you please name one innovation that interested you recently?

I was excited to see Medwand receive various awards at CES 2020. Medwand, which was developed by a physician, is like a StarTrek tricorder but with seven tools that fit in your palm and enables a physician to “examine” a patient anywhere in the world. Medwand

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includes a stethoscope, otoscope, ophthalmoscope, dermatoscope, thermometer, it does an EKG, measures blood oxygen levels.

Medwand has been in development for at least five years, but the buzz it got at CES has come at the right time, in that teleconsultation financed both by insurance plans and by the consumer has risen considerably. In medical deserts, we can imagine a municipality owning several of these and renting or lending them out to families. In a city, there could be one or several per building and, of course, in pharmacies, etc. The price was positioned at $399.

Your experience in digital health spans more than 20 years. Let’s go back in time to the year 2000. How did you imagine healthcare in 2020?

I imagined then that by 2020, we would live in a world of medical co-decision, shared by professionals and patients, with useful information at the disposal of all, thanks to online publication and communities, with a universal electronic medical record for each user, and that telemedicine would be very widely available, enabling each patient to be matched to the most appropriate professionals, a personalized care team, anywhere in the world.

In 2003, upon the occasion of the European Commission’s first high-level conference on eHealth, I was asked by the EC to produce a report on “the Case for eHealth.” I wrote in that report that “e-Health is the single-most-important revolution in healthcare since the advent of modern medicine or hygiene.” The report was very well-received and has been cited over 100 times in peer-reviewed articles around the world.

I still think that eHealth or digital health has that high potential, but given all the limitations that slow down its distribution, digital health needs to be embedded in the conception of the health system.

Are we transforming healthcare in the right way, or could we make some changes?

As concerns the transformation of healthcare for existing medical conditions, we need to involve patients as partners from A to Z. As you might have guessed, greater compassion heals more. If patients helped establish the priorities, health-

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Many doctors spend more time on electronic medical » ��������������������������������������������������� records than on communicating with the patient.« care would be organized differently, and professionals would be the first to benefit as well.

How do you imagine healthcare in 2030?

It will be different in 2030 than whatever I imagine now. Here are a few thoughts nonetheless: – teleconsultation and device-supported teleconsultation will become widespread; – trackers will have helped people increase the number of daily steps significantly; – closed-loop diabetes systems will be used by the majority of patients with type 1 diabetes; – therapeutic Virtual Reality will be widely offered for pain, anxiety, and rehabilitation;

– the quality of X-rays and EKG readings will have increased thanks to AIsupported clinicians; – there will be better patient-physician communication and more co-decision making.  Denise Silber is a leading digital health strategist, communicator, and influencer. Denise created and leads Doctors 2.0 & You Events and Services, bringing digital health tools, start-ups and patient engagement sessions to events for physicians internationally. With her combined experience as a senior pharma executive and entrepreneur, Denise is a frequent master of ceremonies and keynote speaker to diverse healthcare audiences internationally and also advises start-ups.

Denise was awarded the French Legion of Honor in 2011, for her contribution to eHealth, and recognized as one of the #InspiringFifty women in tech in France in 2018. The dual US and French citizen, Denise did her undergraduate degree in Government at Smith and was a US Foreign Service Officer, before receiving her MBA from Harvard. Based in Paris, Denise serves as vicepresident of the Harvard Club of France.

Denise Silber on a visit to the Visualization Research and Teaching Lab at Harvard University.

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Becoming a self-doctor in the era of wearables Smart watches, smart badges, implantable sensors – modern technology allows us to measure and track our physical activity, mood and state of health. In following the “quantified self” trend, we want to know what is happening inside our bodies and take control over them. But do we really need this knowledge? We talk to João Bocas, a wearables and digital health expert.

Wearables give us an insight into our health, allow us to understand behavior, track what is happening inside our bodies and minds. Do people want this knowledge? Do they need it? Do they know how to use this new information?

Yes, absolutely. Wearables can be very powerful, if used to give us insights about ourselves, meaningful data that can be translated into value.

I do believe people want to gain new knowledge, because as human beings we are becoming more informed and better equipped to deal with change, whether for our health, sociologically or even just the generic ones related to the transformations of the world around us. I believe

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that wearables fit the latter description perfectly, as the technological world is evolving and advances in wearables are certainly part of the bigger picture.

In terms of needs, if we refer to health, then the answer is yes. If we need information about a specific health condition where monitoring it is crucial, then yes for sure. In a broader sense, I would also argue yes, whether by choice to improve our fitness or to improve our overall health. As long as people know how to use the information, although I am not totally convinced about this, as wearables in certain cases may provide too much data, „information” that is difficult to comprehend. Even the medical profession tends to find it challenging, from a lack of health education or just time, constraints that the medical profession often displays.

How will wearables transform healthcare, now and in the future?

Wearables give us the opportunity to take charge of our health, which can reverse the loop in terms of traditional healthcare, by moving from a reactive approach to a more people-centered, preventative and self-care approach. It also enables organizations to work on futuristic business models where the wearables could play a crucial role in the delivery, monitoring and intervention processes needed in healthcare.

For example, we are now seeing many hospitals adopting wearable medical devices to monitor patients’ vital signs and recovery levels. This can also be achieved after discharging the patient, with the use of the intelligent and reliable medical wearables now available in the marketplace.

Which applications are the most promising, and most likely to be adapted on a bigger scale?

As I mentioned before, the true applications are in diagnosing, monitoring and even evaluating the patient on a continuous basis. Something completely unimaginable only a few years ago.

What are the latest innovations in wearables, and the trends for 2019?

There is so much innovation going on in the wearable space. It is a particularly exciting new industry. I see and follow many developments in my research and

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» There is so much innovation going on in the wearable space.«

in my professional involvement with the emerging wearable technology world.

I will try to highlight a few, as it is almost impossible to cover everything.

I would say sensing technologies are becoming more popular and mainstream, and a new and exciting trend is smart clothing, known as e-textiles. From innovative shoes to jumpers and even underwear, they are presenting revolutionary ways of embedding sensors.

Another trend that I would like to mention is skin electronics: recently the University of Tokyo in Japan created a sticky and stretchable skin patch with truly amazing electronics capabilities, which can transmit reliable health data extracted from the human body to a computer or data platform - truly astonishing.

Are wearables going to be replaced soon by implantable sensors that will track life signs instantly, from inside the body and even maybe even the mind?

Possibly, this is certainly a growing trend. It also addresses some challenges presented by wearables, such as battery power, size, continuous usage – as without usage there is no data or value – and even human behavior.

Are there also threats related to this trend? Some people are becoming obsessed about health, with no control over the generated data…

Yes, certainly. Wearables present some pitfalls, such as security, invasion of privacy and vulnerability to access by other devices or systems. Plus, on the human side, like anything else, an unhealthy pattern of behavior or even addiction can be created, and here I consider that „dig-

ital” apps are the primary risk for such problems. There are many studies starting to come out about digital usage, and the negative effects on human beings; therefore I would like to stress that anything in excess can be detrimental to human health.

Should the data tracked by wearables, such as lifestyle, also be included in our electronic medical records? Should a medical insurer have access as well?

I would say yes. However, right now, it is still very early days, where the required benchmarking risk and data are not particularly accessible yet. Having said that there is one leading health insurer ahead of anybody else in the sector, where they started working on the methodology a few years ago and have since established health insurance for clients that take into account wearables and health data, with direct rewards, discounts and benefits.

I am actually very surprised that other health insurers are taking so long to follow suit.

Some people are afraid of a world where everything is tracked, an Orwellian „1984”. What gives you optimism about wearables?

Well personally I am not that concerned about Orwell’s vision, where in time everything will be tracked. If we remember recent events on Facebook, for example, we could see that major companies knew and tracked us without our knowledge.

What makes me optimistic and extremely excited is the fact that wearables have the potential to make huge differences to people’s lives on a massive scale, if we don’t worry so much about health data and personal privacy. I would love to see the big companies using data not for commercial purposes but to tackle really life-threatening health conditions, such as cancer, diabetes, rare and heart diseases, to make society healthier by helping human beings live longer with their families. 

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Health totalitarianism „1984”, a dystopian vision of the future created 70 years ago by George Orwell, shapes our fear of a world where every aspect of life is precisely controlled. How real is this threatening scenario in the age of AI, Big Data, and the Internet of Things? And how does this dystopian vision of the future driven by 20th- and 21st-century literature and movies slow us from benefiting from digital healthcare?

Measure to gain power Orwell’s omnipresent eyes of “the Party” control what people say, think, read, and how they live. The ruler manipulates society, deciding what is allowed and what not. There is no escape from the “Big Brother” dictatorship. Even thinking about “forbidden” is a form of rebellion. The protagonist of “1984”, Winston

Smith, commits the crime of keeping a secret diary of his thoughts. Together with his girlfriend, Julia, they start to fight against the oppression that no one else seems to notice.

The world where we are heading can outdo the dystopia of which George Orwell warned. However, instead of one enemy that is visible, defined, and tan-

gible, there will be many of them. Smart spies are embedded in the things at home and in public spaces. CCTV (Closed Circuit Television), sensors, wearables, and smartphones are all continuously collecting data, which is then processed by tech companies and governments. As a result, trained algorithms can recognize our faces, tracking where we are and what we are doing. In healthcare settings, they can also monitor the behavior and, through mobile applications, modify it to improve our overall well-being, longevity, and happiness.

Step by step, as a society, we give away small parts of our freedom – everything for a better, healthier life. There is nothing wrong about prevention and behavior change programs because there is nothing as precious as our health and

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the health of the ones we love. Unfortunately, some tech companies progressively abuse this fundamental need that is placed at the top of Maslow’s pyramid.

Technology has become a new form of religion, and the promise of a longer and healthier life is a new kind of salvation for those who believe. Data creates power, with tech companies becoming small countries organized in a non-transparent way. It is enough to mention Facebook and the Cambridge Analytica data misuse in 2018.

Digitalization raises hopes of personalized prevention adjusted to our needs, habits, and the environment. According to a Research2Guidance report, there are already more than 318 000 mobile health apps on the market. They motivate us to do more sport, eat better, and lead a healthier lifestyle. Today nobody forces us to use them; we can still switch off a fitness app or wearable. But what if smart devices begin to support our healthy lifestyle so that we are not even aware of it? What if Alexa makes comments when we order some sweets online? Is this still health prevention or manipulation? Is China’s social credit system violating human rights, or does social security justify such interventions?

A brave new world The year is 2049. After the profound healthcare crises of the twenties, the government decides to speed up the digitalization process. It is clear that the current approaches are outdated and the healthcare budget – cut drastically due to the massive costs of the anti-climate change program – can no longer handle the rising expenditures driven by non-communicable diseases and challenges related to aging societies. People expect radical changes. The brave new public health strategy assumes the use of the individuals’ data collected from electronic health records, wearables, and smart devices to introduce an innovative preventive platform. The goal is to manage the personal determinants of health and to correct behavior if it threatens our health and well-being. The system calculates individually the health insurance policy and updates it regularly according to the individual’s data. It means that smokers pay 20% more than non-smokers, for example. All patients have access to their own health data and their health scores. Good behavior is rewarded, while bad is punished. Obese people have to avoid highsaturated fats. Those with liver disease

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have a ban on buying alcohol. Several hours of watching TV on the sofa can lower the health score drastically. Doctors are allowed to prescribe physical activity just as they would medicines.

Smart devices are everywhere: smart beds measure the length of sleep, smart fridges analyze food, smart mirrors give a daily health check-up, and smart bands monitor physical activity. The cameras and microphones in smartphones control mental health. If the algorithms detect worrying patterns, the system makes an appointment with a doctor automatically. People diagnosed with specific diseases have to follow strict health plans. If they decide to switch off some devices, which is still possible, their health scores decrease. Since sugar consumption has been ranked as one of the most significant health risk factors, besides cigarettes and alcohol, new health guidelines have become very restrictive. Now the system decides and tells everybody what to do and what to eat. Some try to cheat the sensors to enjoy the food they love or a night out without sleep.

Algorithms. The new super-doctors Although this future vision of healthcare based on data analysis and artificial intelligence might be completely wrong, it is not unrealistic. Already today, some systems like this exist on a smaller scale.

For example, dacadoo measures health with the Health Score (1 is the lowest, 1000 – the highest). The app can serve the needs of individuals, but to health insurers and companies, it is a “wellness engagement solution.” The method can be somewhat inaccurate because the complete data set is never available for the individual, but this may change soon since relevant emerging technologies are developing at enormous speed. The Internet of Things market is predicted to reach $520B by 2021, more than double spent before 2021. According to the IDC report, 172.2M wearable devices have been shipped, while Global Market Insights predicts that the digital health market is set to exceed $379B by 2024.

Some may argue that people living in democratic countries will not allow such a “control system” to be created.

The truth is that we have always agreed for decisions to be made for us when it comes to health issues. The reason was invariably the same one: only doctors and medical researchers had sufficient knowledge, the key to better health. But this age-old principle was also fuel for

» Digitalization raises hopes of personalized prevention adjusted to our needs, habits, and the environment.«

paternalistic medicine, one that blocked access to information by patients – a system set to end in the era of digitalization. Medical science, which is not the same as medical knowledge, has become available for all. Doctors have lost their monopoly on know-how: the democratization of healthcare has come. Although this revolution leads to empowerment for patients, there is a small trap that has been overseen by many. Algorithms are gaining knowledge on an unprecedented scale.

The new era of “AI paternalism” is already arriving. If the precision of AIdriven disease prevention systems continues to rise successfully, people will accept its authority. We may find ourselves gradually sliding into the scenario described above. Such a screenplay may be justified in many ways. Behavioral risk factors, like smoking, drinking too much alcohol, nutritional choices, and physical activity, determine about 50% of our health in practice. Only 10% of the population’s health and wellbeing is linked to healthcare and quality medical services. In other words, people are in charge of their health. Unfortunately, behavior change is a complicated process, which must involve individual motivational and environmental factors. So we all neglect our health, destroy it intentionally or unintentionally, and then require our health systems to repair it at any cost. For example, low medication adherence leads to poor clinical outcomes, costly drugrelated side effects. If traditional preventive measures and health promotion campaigns fail, perhaps it is time to demand more responsibility and engagement by introducing the Health Score System? Or even to enforce healthy attitudes?

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» The fear of a world dominated by harmful technologies is rooted in societies.« In this place, I have to say clearly: digitalization is neither demonic nor a blessing per se. However, it is never neutral. Like a two-edged sword, while it may harm it may also bring new opportunities, to improve the quality of care and access to medical services, reduce health inequalities, and lower the burden of non-communicable and infectious diseases. It can help us in fighting the epidemic of mental disorders as well as malaria in Africa. Algorithms speed up research and the development of new medicines. The IBM Watson supercomputer can analyze millions of patient records, scientific papers, and follow the latest research to improve the outcomes of cancer patients. Algorithms examine medical images, pixel by pixel, to find even the smallest anomalies. No human eye can do the same. AI systems are becoming super-doctors based on the knowledge that no human being can ever obtain. This marvelous power of AI gives hope for millions who suffer from thousands of diseases, as well as those who want to live a long and healthy life.

Going back: If health is the highest value for every human being, then they are also ready to pay for it through the loss of privacy or freedom. They are much less more critical here, and only those who are sick can understand that, in the fight for their own life and health, a better outcome excuses any wrongs committed to attain it. And there is nothing wrong about this; we are all human.

AI vs. doctors, doctors vs. AI Nonetheless, the fear of a world dominated by harmful technologies is rooted in societies. Literature also teaches us that technological progress might still threaten humanity. Data security issues or ethical doubts often stop or delay many promising digitalization projects, especially in Europe.

“Repercussions of and intertextual references to Orwell’s 1984 are visible in fiction on both sides of the Atlantic and, in the world of smart homes and digital assistants, seem as relevant as ever. The plots usually feature a dystopian environment that seems to be only a few steps away from reality. That way the totalitarian fantasy and the realm of the imaginary are very much linked to the present and act as a warning,” says Karolina Golimowska (Ph.D.), a literary scholar specializing in contemporary US-American and British fiction doing research among others on disaster fantasies; teaches at FU Berlin.

Although robots – such as the DaVinci surgery system, are doing a great job in helping doctors to perform operations with extraordinary precision, in the public awareness robots in the future only kill people (“Terminator”), reduce them to the role of batteries (“Matrix”) or, as super-smart beings, manipulate us to regain their freedom (“Ex Machina”). “Black Mirror” by Netflix warns of a technological future where innovations already available today can be turned against humanity. Since 1997, when IBM’s Deep Blue won a chess game against Garry Kasparov, Artificial Intelligence began to be seen as a competitor to humans, or even an enemy. The battle itself was not just a game; it was a battle between AI and men. The consequences are visible today and the New Yorker’s article “A.I. versus M.D.” only confirms that it’s about a “win-lose,” not a “win-win” situation.

“Novels like The Circle (2013) by the US-American author Dave Eggers or Never Let me Go (2005) by the British author Kazuo Ishiguro or the recently (2018) released series entitled 1983 directed among others by Agnieszka Holland show obedience as a key in totalitarian systems. Consistently, any kind of individualism becomes a form of subversion. All examples show exercising control over human health as a means of exercising power. Control over one’s health means gaining control over their body and hence the possibility to influence and manipulate their social behaviors. Similarly to other forms of disaster fantasies, totalitarian dystopias are meant to shock and frighten the readership, to then give the possibility to look at reality from a distance and to put things into perspective,” emphasizes Karolina Golimowska. An expert in the contemporary US-American and British

fiction adds that measuring and monitoring health parameters in these representations moves it from private to the public domain and hence instrumentalizes it as means to create and maintain a totalitarian system used by a bigger and more powerful force for their own interests and aims. In “The Circle,” the more significant power is embodied by a company of a global reach, in “Never Let me Go” it’s the real people who control human-made clones, in 1983 it is the state behind the still existing Iron Curtain,” argues Karolina Golimowska.

This negative personification addresses more than technologies. In the media, literature and film industry, evil characters are far more fascinating than the good ones. They tend to awaken fears in political debates, societies, and public discussions.

Epilogue. The script of tomorrow After 70 years, “1984”, the most significant novel of the 20th century, still leaves a mark on societies. It arouses considerable mistrust toward emerging technologies: Big Data, Artificial Intelligence, face recognition systems, injectable sensors, voice assistants like Alexa or Google Home and, last but not least, the constant monitoring of health, data mining, and processing. What kind of healthcare do we want in the future? What about ethics, not just in digital medicine but also in AI-driven disease prevention?

The regime of the algorithm will be invisible, sophisticated, hidden in the connected smart devices installed even in our bathrooms and sleeping rooms, buried deeply in the Internet of Things network. Such a health supervision system equipped with data does not need to hear our narrative stories about how we feel and what we need. From the perspective of many people, it is not going to be a meaningful change. Already overloaded doctors lack time to listen to their patients. Paradoxically, paternalistic algorithms will be caring about us, checking what we do, 24/7, like personal health coaches. Making us live longer, happier, and healthier lives, the interference in our private lives will always be justifiable. But George Orwell’s story, instead of rooting fear, should be a lesson about the meaning of freedom, also in science.

We can make healthcare human-centric again if we start to shape the technology rather than allowing technology to shape us. 

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The future of healthcare.

Will medicine become data science?

Data are one of the most valuable resources available today. Like oil or gold, they can be further processed and commercialized in many different forms, such as knowledge, new services and products. Health data also have additional value. They allow to recognize behavioral patterns and predict health trends. Will it be possible to invest health data in health funds, like we invest money in banks today?

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Data slipping through our fingers The quantity of personal data is increasing at a rapid pace. Whether consciously or not, today, we make them available to dozens of companies by shopping online, using e-mail, or communicating with friends on social media. Many websites or mobile apps can only be used after having agreed to the processing of personal data. In this way, the average person loses control over who uses their personal data, when, for what purpose and to what extent. They circulate almost the entire world. Evermore of the devices connected to the Internet of Things are collecting our information, which can then be used to describe our lifestyle or behavior. For instance, even a simple electric meter, recording electricity usage and automatically transmitting the data to an energy supplier, tells them what hours we spend at home and what we do. A rise in consumption probably means that we have switched on an electric cooker, etc.

We voluntarily make a lot of data available, in return receiving advice, summaries, and estimates. This is the case with smartwatches, wrist bands, and mobile applications, which measure our physical activity or the quality of our sleep. The transaction is obvious: in exchange for data, I gain knowledge. The more devices around us there are connected to the Internet, the more privacy we lose. It will be challenging to change this because soon, practically every electronic device will, in some way, be plugged into a more extensive system.

The value of medical records The situation is different in the case of purely medical data, i.e. those collected in electronic medical records. They are secured and protected because of their sensitivity, while those responsible for their protection are those who collect this type of information: physicians, medical clinics, hospitals, payers, and public health institutions. However, everyone will agree that, regardless of where the processing occurs, the data are or should be the property of the patient, and hence they should be the ones with the right to dispose of the data at their own discretion. In many cases, however, this is just a theoretical right. Here I mean, the lack of interoperability.

Imagine the situation where a patient changes his/her residential address and his/her new physician uses the IT system

» Health care will become a science focused on data engineering.« of another company. The patient cannot request the transfer of data to the new software, as very often, there are no such technical possibilities. In the same way, it’s not allowed to decide on the scope of data that is stored in the EMRs. Today, electronic records include data of performed medical services, diagnoses, prescribed medications, information from the medical history, and the non-standardized notes of a physician.

The patient cannot supplement them with the results of complementary tests, such as blood pressure, blood glucose levels, body temperature, etc. Not even the data from wearable devices, which can monitor an increasing number of parameters related directly to health. In this way, much of the data which also forms part of the health image of a patient goes to waste.

Data brokers and health investment funds Although some medical data are stored and available centrally (centralized data repositories), these constitute a fraction of the information about our health. In order to prevent diseases more efficiently, a full picture of a patient is necessary, and even the slightest details may be of great importance. Our diet, quality of sleep, physical activity, the amount of consumed alcohol and the time spent on the Internet – each part of our everyday reality describes our health to a certain extent. Because the amount of such data is vast, their analysis must be performed by algorithms based on artificial intelligence, to look for links and patterns and to separate valuable information from the unimportant. With time, collecting most of this information will become commonplace and its importance grows.

This is for the physicians and healthcare providers, who will depend on accessing such data to achieve a better understand not only of our medical condition but also our needs, allowing them to personalize the methods of treatment or prevention. More and more often, we hear about such a model, where physicians

subscribe to additional data concerning lifestyle.

For it to become a reality, health data have to be standardized so they can be included in a coherent, interoperable database. If this happens, health care will become a science focused on data engineering. This opens the door to new forms of health services. Data that we meticulously gather throughout our life will finally pay us back, just as our savings do today.

The more information gathered the more knowledge that can be extracted by algorithms. We will likely be able to invest information like money in a bank.

Individual data brokers could offer new preventive and prognostic services.

It would be sufficient to transfer our data to them, to allow them to store and analyze it. These could be either insurers or medical facilities specializing in preventive services. Medicine will undoubtedly transform towards the disease-prevention oriented model; hopefully, cheaper, more precise, personalized, and patientfriendly. Therefore, the physicians face an evolution toward becoming an adviser or health mentor.

The value of data increases with their quantity, completeness, standardization, interoperability and the development of new artificial intelligence tools capable of their comprehensive analysis. The primary source of data will soon no longer be a medical office, but our houses and the smart devices that surround us. One day an intelligent toilet will carry out a urine test, while a smart mirror checks the condition of our skin or mental health. When the mind-shift in healthcare changes from a focus on disease to a focus on the patient, the effective prevention of diseases will be through the analysis of the information generated directly by each one of us. This does not necessarily mean a loss of privacy, provided that the definition of medical data can be broadened and include information on lifestyle, which today is of no importance. 

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Telemedicine benefits during covid-19 pandemic.

But is it here to stay?

The coronavirus pandemic has forced healthcare to go online. In many countries, online teleconsultations are currently the only way to give and receive medical advice.

Instead of face-to-face appointments, we can only see a doctor through a computer screen. How will this affect the development of new healthcare technologies? Will teleconsultations become more popular, or will they be regarded as a substitute that we resort to only in times of crisis?

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A compulsory lesson in digitization So far, telemedicine has been developing very slowly. Even though adequate technological solutions have been available for a long time, there are other obstacles in the way: • reimbursement models favoring personal doctor’s appointments; • issues related to personal and medical data security; • the fear that contact between the doctor and the patient will be dehumanized; • the fact that teleconsultations may be regarded as a poor substitute for a doctor’s appointment.

Despite its promising advantages, telemedicine has not gained mass popularity, as had been hoped by the enthusiasts of digital health solutions.

That was true, at least, until the beginning of 2020. Due to the rapidly growing

number of coronavirus-infected patients, initially in China and then all over the world, doctors and patients were forced to face a new reality almost overnight.

The pandemic has turned the established order on its head, reshuffled values, and radically shifted our point of view. When protecting your own health and the health of your loved ones requires isolation, you have no other option than to switch to digital services. But it does not apply solely to healthcare. It includes canceled concerts, soccer matches that can only be watched online, and museums are offering virtual tours. Instead of going to a restaurant, you have to have food delivered to your doorstep. A walk around the town has been replaced by surfing the Internet and exercising on a yoga mat.

Home-office has become the only office. Hardly anyone has the impression that they do these things because they

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like it that way. A sense of coercion and adjustment dominates us to an extraordinary situation, but all we can do is hope it ends as soon as it began.

An artificially accelerated change History shows that pandemics change societies and their behavior for an extended period of time. The sense of danger triggers different mechanisms of thinking and functioning, which will stay with us even when everything goes back to normal. The SARS-CoV-2 pandemic will pass, similarly to other ones from the past. All epidemiologists agree on that.

However, it will take us long months, or maybe years, to go back to normal.

Today, the law is being rapidly amended to enable the reimbursement of telemedical services, which are becoming equated with services provided in the doctor’s office. When our priorities changed, discussions on data safety were put on the back burner. Telemedical advice systems have observed a few hundred percent increase in the number of installations. The same goes for mobile applications that establish a video connection between the patient and the doctor.

Under normal conditions, it would probably have taken us many years for digital health systems to become so widely used. Suddenly, we have found ourselves in the future of healthcare, but by coercion rather than our own free will.

When the world finally shakes off this new crisis, many regulations introduced on the spur of the moment will surely be canceled. Still, some telemedical systems and applications installed on computers and smartphones will remain.

Most of the patients will go back to traditional doctor’s appointments, but remote medical advice has already entered public awareness as a viable alternative.

Many patients have tried it and were won over by it. Not everybody will appreciate it, but the most important thing is to test this technology yourself. Moreover, we can see that telemedicine is an alternative that makes sense.

in a waiting room full of people, and infect others on the way or, worse still, infect doctors. In this case, we could just as well make use of teleconsultations, stay home, get an electronic prescription, and an electronic sick note. It is a commonsensical and mature approach, which additionally reduces the burden on the healthcare system.

There are health services which can successfully be provided remotely, without detriment to their quality. In the times of constant economic growth and prosperity, we had the impression that healthcare was made of rubber and could be stretched infinitely. Also, since we paid our contributions, we could use it as much as we wanted, simply because we deserved it. We took doctors’ precious with trivial matters, even though other

patients had to wait in a line because of that. This attitude is as self-centered as emptying the shelves during a pandemic, when we think only about ourselves.

This attitude leaves no room for respect towards doctors and their time, as well as the fact that there are people who need help more than we do.

But current circumstances in which telemedicine is developing so rapidly might turn against it. After all, we cannot rule out a scenario in which telemedicine will have to bear the stigma of a pandemic for years to come and will be associated by patients with an emergency substitute used in the times of major crises. In that case, instead of witnessing rapid development in telemedicine, we might paradoxically bring it to a halt. 

» It is time to realize: telemedicine isn’t a worse substitute for face-to-face visits.«

The end of an era in healthcare One of the positive lessons that everyone should learn from the coronavirus pandemic is social responsibility. It is the responsibility for our own health, for the health of the relatives and society in general, for not overburdening the healthcare system. When we have a cold or flu, we do not necessarily need to take public transport to the doctor’s office, sit

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Strengthening digital health literacy in society The rapid growth of digital health is inevitable.

Although it brings about many advantages, it also threatens to worsen existing health inequalities experienced by people who have lower levels of digital health literacy. Digital health literacy concerns the ability to find, understand, appraise, and apply information from electronic sources to address and solve health problems.

Kristine Sørensen, Founder of the Global Health Literacy Academy (Denmark), President of the International Health Literacy Association

Impossible to understand People who are digital health literate are empowered, able to manage their health, and critically engage in societal health challenges and opportunities. In turn, people who struggle to keep up with technological advancements and pursue low health literacy levels are at risk of health disparities.

To strengthen digital health literacy in the society, we need inclusive digital health services as they can lead to improved prevention, awareness of healthier lifestyle behaviors, and an overall im-

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provement in health outcomes throughout the life-course.

Make a quick reality check! Find the website of your local hospital or primary healthcare center. How easy is it to understand and navigate? Can you find the information relevant to you? Does it use plain language, pictures, and pictograms? Consider other digital platforms – are you able to assess information from

social media, influencers, commercials, and other digital stakeholders? Indeed, we are faced with a digital information jungle that is extremely difficult to explore and fully grasp.

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» Digital health solutions form the future health outcomes in scales never seen before.« health literacy accounts for 3–5% of the total healthcare cost per annum (source: Eichler et al. 2009). Improving health outcomes through an investment in digital health literacy will potentially save billions of Euros. For that alone, decision-makers in health should invest in the win-win situation where people become empowered to increase disease management and self-care in general.

Digital health solutions form the future health outcomes in scales never seen before. To improve health – rather than inducing health inequalities – digital health providers must act. They have to take responsibility and wisely integrate health literacy design, co-creation, and user-involvement in all phases of the design, implementation, and evaluation of the digital services. Specific attention should be given to attend the needs of diverse groups to avoid widening health inequalities.

The Internet presents a tremendous, untapped potential for the public to access health information that can support informed decision-making and capacity building of professionals, patients, and people in general. Notably, health literate digital resources do not only help the disadvantaged but supports and make digital services and solutions easier to reach and apply by everyone. A political, public, and commercial priority of advancing digital health literacy is a golden societal game changer in the quest of improving health for all.

What is health literacy?

Knowledge enhances health (and saves money) From a societal point of view, digital health is a challenge for millions of patients and citizens. New research reveals that even in a welfare society as Denmark, 4 out of 10 citizens are low on the health literacy spectrum (source: Svendsen et al., 2019). In the UK alone, low

Health literacy is closely linked to literacy and entails the knowledge, motivation, and competency to access, understand, appraise and apply information to form a judgment and make a decision concerning healthcare, disease prevention and health promotion to maintain and promote quality of life during the life course. ​Limited health literacy is a neglected public health challenge. It is influenced by personal, situational, and societal factors and influences health service use, health behavior, empowerment, and sustainable living. 

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What the radiologist need to know about artificial intelligence To dispel growing doubts among radiologists, the European Society of Radiology (ESC) has published a brief white paper to explain the possible application of AI in radiology, its ethical and professional impact, and future evolution.

Hype leads to misunderstanding Many professionals are lost between scientifical facts and buzz words like AI, radiomics, algorithms, machine learning etc. Some have concerns that precise and efficient algorithms will replace the radiologists very soon. The media regularly informs about AI-based systems that outperform doctors in analyzing medical images. In an article recently published in Nature, Scott Mayer McKinney from Google Health describes an AI system that outperforms expert radiologists in accurately interpreting mammograms from screening programs. The research was conducted on mammograms for 25,856 women in the United Kingdom and 3,097 women in the United States.

AI in radiology is growing rapidly – medical images consist of pixels that can be easily analyzed by algorithms. What’s

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more, with a precision higher than a human eye.

Improving the decision process Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterization. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organize and share the image data from which AI models can be trained. AI can be used as an optimizing tool to assist the technologist and radiologist in choosing a personalized patient’s protocol, tracking the patient’s dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking be-

tween words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimize clinical and radiological workflow.

“Who will be responsible for an algorithm’s errors? Probably the radiologist.

However, if we do not understand the rationale behind a software’s decision, are we willing to trust it, or would we rather continue following our instincts? But then what was the point in asking the algorithm in the first place? Similarly, there may be other ethical issues that will arise once artificial intelligence finds its way into clinical practice,” argues Daniel Pinto dos Santos from the European Society of Radiology.

Key points of the white paper • Outside the traditional radiology activities of image interpretation, AI is estimated to impact on radiomics, imaging biobanks, clinical decision support systems, structured reporting, and workflow.

• The key factor of AI performance is training with big and high-quality data to avoid overfitting and underfitting.

• The three laws of robotics could be applied to radiology, where the “robot” is the “AI medical imaging software.”

• If AI is used in clinical practice, the main medico-legal issue that then arises is “who is responsible for the diagnosis.”  To download the white paper, go to: https://bit.ly/2TNlGmJ

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How to ensure human touch in digital healthcare driven by AI solutions?

What impact will the digital transformation and increased use of Artificial Intelligence have on the relationship between a patient and a doctor?

How to take advantage of new digital opportunities without losing sight of essential values such as safety, privacy, security and trust? Three experts answered these questions during the European Health Forum Gastein 2019.

Ran Balicer Chief Innovation Officer, Clalit / Founding Director, Clalit Research Institute & BGU Public Health Professor, Israel

Transformation not digitalisation When we look at digital transformation, the key fact is not the “digital”. It’s the “transformation”. There is no point facilitating digitalisation without dramatically changing the foundations of care processes. In other words: if you take a broken clinical process and digitalise it, then you get a costly, broken digitalised process.

When making our plans for the future, we need to keep asking ourselves: Why? Why do we introduce this system? What will be the impact on the patient, on the population, on the providers? There has to be proof that the benefit is real and obtainable.

AI to bring back the human touch There is a fear that digital transformation and Artificial Intelligence (AI) will reduce the human touch. This could not be further from the truth. In their daily work, physicians are doing too many repetitive tasks that do not require their unique skills. AI will allow doctors and nurses to go back to their real purpose.

AI will also allow us to move from intuitive medicine to introducing more field safe mechanisms because currently, we are failing our patients too often.

Today 30% of care is wasted, and human error is the third cause of death.

Digital transformation would offer us an opportunity to move away from the tyranny of reactive medicine and move

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» Today 30% of care is wasted, and human error is the third cause of death.« towards proactive and preventive care.

Assisted by data and AI, we can locate those patients in need of care before they actually become symptomatic. This will be both sustainable and effective. Overall, I think that AI will allow us to have more of the human touch.

Marco Marsella Head of Unit, Directorate-General Communications Networks, Content and Technology (DG Connect), European Commission

Data for the benefits of citizens Digital transformation will be data-driven. Data will be at the heart of the transformation; it is the enabler for the continuity of care across the Member States and for the promotion of personalised medicine. However, data is used differently across the Member States and their information systems, so we need to find mechanisms to make sure that data is available for those who need it. Citizens should ultimately control data.

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» Data will be at the heart of the transformation.« To make sure that this happens, and here comes the human touch, we need to ensure there is trust in the way technology solutions are developed. This is about making sure that the needs of those using these technologies are met and about empowering them to take control of their health. Trust will come from ensuring that the data is protected, and from the way the European Commission is moving ahead in putting together rights and innovations.

Digital society The European Union is investing and supporting the evolution of the landscape. The European Commission is now working to adopt a strategy at EU level for supporting AI, which includes AI in healthcare systems. Besides, the European Commission supports researchers to come up with new ideas and test them in healthcare systems.

Enabling trust in the digital society forms the basis of the European Commission’s work with the Member States and of the work to make sure that digital solutions benefit citizens.

have good experiences in other sectors, why should you not have the same in the healthcare sector? To help people manage their health better, the NHS launched an app called ‘ Empower the Person’.

Structure and options We need to create standards, frameworks and collaborations. One of the key aspects of my role is to create a pipeline for new (digital health) ideas to become a reality. How do you ensure and validate products? How do you implement a product and ensure that it is actually impactful? This is of great importance.

In healthcare, it is important that we also provide people with options, particularly elderly populations who can feel

insecure with modern technologies, or migrants who might not speak the local language. So you need to have the opportunity of approaching your healthcare digitally, but you also need the backup option of doing it. 

» We need to create standards, frameworks and collaborations.«

Indra Joshi Digital Health & AI Clinical Lead, NHS, England

Understanding new tools There is an importance of ensuring that doctors and nurses need to become digitally savvy, but they don’t make up the entire healthcare workforce. There is a huge proportion of people working in care and community practices. So how do you actually support them in understanding the new tools that have been developed, and how can they explain them to their patients?

It’s also vital to talk about the public and patients when we discuss digital health. Because all of you in the room are people, you are all humans, and you want to feel empowered and have a better experience when it comes to health. You

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Photo credit: Jan Zappner/re:publica

Cyber-medicine & humans.

7 new concerns about digital healthcare Innovations in healthcare need to be discussed as never before, footnotes and small print for user manuals of health apps need to be read carefully by all of us. Instead of simplifying or demonizing digital healthcare, we need more research and deeper debates. Conclusions and questions after this year’s conference on internet and society re:publica.

Capitalism: Is there a place for equity and solidarity in (digital) healthcare?

“A key challenge is to ensure that all people enjoy the benefits of digital technologies for everyone. We must make sure that innovation and technology helps to

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reduce the inequities in our world, instead of becoming another reason people are left behind. Countries must be guided by evidence to establish sustainable harmonized digital systems, not seduced by every new gadget,” postulates Dr Tedros Adhanom Ghebreyesus, Director-

General, World Health Organization in the foreword for the latest report “WHO Guideline: recommendations on digital interventions for health system”. The WHO calls for #HealthForAll. But equal access to healthcare in the era of digitalization is in threat. Healthcare is too complex for its problems to be solved in a simple way. Digital health is not a silver bullet – although it creates new opportunities for health services distribution, new challenges arises. Digital natives profit from brand new smart watches to monitor heart health or blood glucose, in modern clinics designed in Silicon Valley patients have access to the newest telemedicine innovations for early dis-

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ease detection. Some say that sooner or later digital health will also be adopted in public health systems and become available for everyone. But the digital revolution is exponential – those with better digital literacy, education and financial status will always have access to better care supported by newest innovations.

Wearables will be followed by DNA-editing, injectable sensors, robotics to assist patients. Let’s be realistic: it is estimated that hundreds of millions people globally need glasses but don’t have them (can’t afford them); modern, personalized prosthetics are affordable for a small percentage of disabled people; the first tooth implants were created in 1965 but even today many public systems don’t reimburse them – not everybody can pay a few thousand euros to improve the quality of life. We can expect the same in digitalization. Inequalities are present in democracy, and tend to grow where the invisible hand of the market set the rules.

Even in social health systems, supported by digital health innovations, solidarity is a pure myth. So how can we tackle it?

Dr Algorithm: The patient in the world of calculated health risks and rationalized behavior We don’t have to agree but it’s a matter of time: Artificial Intelligence already outperforms physicians in screening Xrays for certain diseases and spotting abnormalities in human bodies, soon it will be better at diagnosing patients and prescribing personalized treatment plans.

While AI can analyze millions of data sets in a second, a human doctor would need weeks to complete the task. Computing power doubles approximately every 1.3 years. The data generated on every patient rises exponentially. Algorithms suggest what to buy on Amazon or which information might be worth of reading on Facebook, in medicine they calculate possible complications during surgery or the health risk. Data gathered on electronic health records reflects our behavior, habits and lifestyle. Some insurance companies rewards patients for physical activity. Omnipresent sensors and apps in smartphones monitor life signs, performance. AI sees patients through data sets, not through personal narration. A glass of wine or piece of cake, small daily pleasures, are being converted into calories lowering our health score. We are still far away from China’s social monitoring system in which every person has a personal score that evaluates how good

or bad a citizen he or she is. But rising healthcare costs bring pressure to tighten expenditures and introduce savings.

Efficient, personalized prevention at the cost of privacy is unavoidable. In 2016 Stephen Hawking said that the creation of AI might be not the best but the worst thing for the mankind.

Techno-religion and the cyber world: Better and longer life in quantifiedself societies For many digital health is more than digital devices – it’s an ideology, a postmodern philosophy. On the cover of The Economist, Steve Jobs was once named a magician and presented as a Jesus titled: “The Book of Jobs: Hope, hype and Apple’s iPad”. Technology is to improve our life and to save the world. Silicon Valley startups play the role of new messiahs, entrepreneurs with superpowers. Every Apple conference and presentation of a new product is like a spiritual experience where ecstasy mixes with desires and expectations. Announced in December 2018 Apple Watch Series 4 with FDA-approved ECG was to “change the history of medicine”. Theranos founder, Elizabeth Holmes, promised a breakthrough in medicine. Many believed her

blindly and invested millions of dollars to support her visions (the product didn’t even exist). The fascination of new technologies converts into technology populism, where rational thinking is supplemented by trust and admiration. Quantifield-self, the phenomenon of tracking life signs, shows the new health tech culture – now patients have the power to control their health and in this way they are not dependent on physicians anymore. In a digital, perfect world based on binary scales, emotions, personal needs, fears and expectations play no role. The impact of digital technology on mental health is still unexplored but the negative side effects already visible in highlytechnologized societies, like Japan. Isolation, loneliness and constant competitiveness powered by measurements have consequences.

Medical fake news, hoax and bias in media, Google searches, filter bubbles: Access to knowledge is a threat Let’s face it: the Internet is full of bullshit. Democracy on the World Wide Web means – no matter if we like it or not – a freedom to express opinions. Many of them are disseminated anonymously or under a falsified identity. Different mo-

Gunter Dueck (philosopher, mathematician, management thinker, writer) believes that everybody has their own “identity-algorithm” that helps to make right decisions in the era of information noise on the internet. Digitalization won’t dehumanize societies. Human beings consist not only of bodies, but also of souls, minds and spirits (photo credit: Jan Zappner/re:publica).

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tivations stand behind hoaxes on the internet and social media: making money, dividing and disrupting, changing minds, feeling part of a group. Anti-vaccine campaigns based on conspiracy theories, pseudoscience or manipulated evidence. On social media they find a huge audience of anti-vacciners who believe that the only profit of vaccinations is a profit made by pharma industry. According to the WHO, vaccine hesitancy is one of the biggest 10 threats to global health.

Measles have seen a 30% increase in cases globally. Medical fake news causes not just disinformation but can lead to inadequate decisions, harms to health or even deaths. Recently Facebook declared it would be removing anti-vaxx groups from ads and recommendations, making it harder for users to find such pages. When looking for a diagnosis on the Internet, algorithms used by Google show the most popular searches, not the evidence-based ones. Lack of digital (health) literacy causes sometimes patients to trust “Dr Google” or anonymous opinions on social media more than a doctor. Algorithms used by Facebook lock users in filter bubbles so they step by step get an opinion based on information displayed on their social me-

dia wall. Many indicate digital education as a panacea for those challenges.

But fake news is improving quickly making it hard to distinguish between truth and false. A video with a known scientist talking about the harmfulness of vaccinations? Deep learning makes it possible.

So called “deepfake” is a realistic synthetic video that has been created using computer-generated imagery powered by artificial intelligence.

Tl:dr: Digital literacy, transparency and information overcharge Too long; didn’t read (tl;dr) – a leading theme of re:publica 2019 relates well to health literacy in the digital era. How to make science as interesting as pseudoscience based on emotions, personal stories? How to restore faith in valuable knowledge in an information noise?

While medical universities and healthcare organizations are producing thousands of pages of scientific papers, the knowledge remains in silos of experts who understand them. We face information overload: the average attention span of a human being amounts to 8 seconds.

For a goldfish it’s 9 seconds… Terms and conditions of digital health apps are too long and in the case of most users are not

» Can a machine make independent decisions about a patient’s health?« read. People give consent for using their personal health data by third parties, to be processed by algorithms many times without their knowledge. Some say, there is no danger as long as the data are anonymous. Experts claim, that anonymized data can be easily “deanonymized” using for example location-tags etc. There are over 318,000 mobile health apps. The market remains out of control – everybody can create a health prevention or behavior change app without obligation to validate. “If it’s available in the Apple or Google store it doesn’t mean it’s reliable”. How to ensure quality for digital health innovations? How to make algorithms transparent so people are informed why they have got such a diagnosis from a symptom checker without having to understand machine learning at all?

Digital ethics: Wrong questions about killing machines

For Oliver Nachtwey, sociology professor at the University of Basel, digitalization is a new form of global religion. Like many theological doctrines, it also promises a longer and happier life. Tech leaders are seen as new messiahs making the world a better place to live (photo credit: Jan Zappner/re:publica).

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The debate about ethics in digitalization has been dominated for years by the scenario of autonomous car that – in an emergency – has to make a rapid decision between killing one group of people or another one (for example two children versus five adults). This is a completely wrong question that we ask in the public discussion about robotics, Artificial Intelligence and algorithms. In healthcare the typical ethical considerations concerns mistakes made by surgery robots.

Who will be in charge? The manufacturer, the assisting doctor or maybe a patient who signs a consent form and was aware of the potential risks? Well, of course such scenarios are real but too marginal to absorb all the attention. We have to ask first what kind of healthcare we expect in the future, where to set the frontiers between privacy and prevention, how to ensure patients the freedom to decide about their own data (and the right to be forgotten in the electronic health records repositories). Can a machine make independent decisions about a pa-

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tient’s health? What if the doctor’s opinion doesn’t match algorithm’s decision?

What do we expect from doctors in the future? How to avoid social unrest that follows from (digital) health inequities?

Is it ok to leave an elderly person alone under the care of a robot? How to eliminate discrimination or abuse through big data analyses? And finally, which values – social, human, economic – should be fundamental, regardless of the technological innovations? In the upcoming years the wave of new technologies are going to transform healthcare and medical professionals’ work like never before in the history of humankind. We should be more worried about the changes in the patient-doctor relationship or automatization in care instead of feeling afraid of robots getting out of our control. Ethical challenges have nothing to do with science-fiction problems.

Doctor or App: Who do I trust and how will AI change the medicine?

“AI won’t ever replace doctors”. Really? Someone who declares it already today has a very idealistic vision of technologies in healthcare (or wants to stay politically correct). We don’t know it but we can foresee, with a high probability, that Artificial Intelligence will conquer doctors in some competences. Besides it’s hard to forecast if digitalization will make healthcare cheaper, more effective, improve prevention, shorten medical errors, strengthen the quality of care or help to make better treatment decisions. Maybe we should fear the opposite scenario. When facing new challenges – ageing populations, rising burden of non-communicable diseases and galloping costs – we tend to stay optimistic, sometimes even naive. The power of technologies in healthcare lies in augmenting doctors’ competences, abilities, qualifications, intelligence, capacities, performance. Nonetheless healthcare professionals need to be ready to adapt to digitally-driven changes. It’s impossible to control patients’ expectations – some of them will prefer an AI to make a diagnosis, for some a doctor will stay irreplaceable. Let’s stop this meaningless argument which blocks us in making a next step forward. In healthcare systems based on democratic principles, patients are free to choose between an app an doctor. At last, what’s most important is quality of care and efficiency of treatment, not the way of achieving the goal. 

Let’s imagine that a company like Google dominates the market for autonomous cars. What will happen if one day a mistake in an operating system or a bankruptcy shut down all the vehicles, paralyzing communication, transport, cities. Sarah Spiekermann, chair of the Institute for Information Systems and Society at Vienna University of Economics and Business, points out the social consequences of digitalization (photo credit: Jan Zappner/re:publica).

Body 4.0 is a body interpreted as a data set, not anymore as a set of organs and cells. New regulations are required to ensure privacy and full control over personal data (Maike Janssen, Bauhaus University) (photo credit: Jan Zappner/re:publica).

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The risks of basing digital health strategy on industry hype and alluring prototypes The World Health Organisation recently hosted a landmark “Symposium on The Future of Digital Health” (Copenhagen, February 6-8th 2019), which was attended by healthcare leaders, innovators and analysts from across the European Region. Professor Claudia Pagliari, Director of Global eHealth at the University of Edinburgh, spoke in a plenary session entitled “Leaders of the Future – political, economic and ethical governance for digital health”. In a follow up interview, Artur Olesch asked about some of the key points raised in the talk and sought further insights and views on the theme of making digital health count.

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conferences Many national healthcare digitalization strategies fail. What are the main reasons?

Failure in large-scale digital programmes is common, alas, and while this also affects corporations, governments have particular track record, or at least can appear to, since they are accountable to tax payers and therefore attract more scrutiny. There are many reasons for these failures, but often they boil down to politically-driven priorities and deadlines (e.g. a leadership commitment to “transform” a major service by a certain date), unrealistic delivery schedules (often based on election or spending cycles), inadequate implementation budgets (reflecting a failure to think-through the complex human and organisational change issues required), flawed procurement and contracts (e.g. omission of customisation and ongoing maintenance costs), lack of user-centred design (compromising IT usability or workflow fit), workforce gaps (vulnerabilities in local knowledge or support), and insufficient public engagement (particularly in projects involving patient data sharing).

Lack of interoperability and a failure to employ whole systems thinking can also leave even superb innovations stranded on islands of excellence.

During the WHO conference in Copenhagen you mentioned a few key success factors of effective governance of national digitalisation strategy. Among them: connected people, connected strategies, evidence-based decision making, constructive cynicism and accountability. Could you please briefly describe them?

Governments and healthcare systems are complicated beasts, operating on a massive scale, at high pressure and with multiple demands. People are constantly busy, budgets need to be managed, services needs to be delivered. Clinical, computing and administrative remits are split.

Care is organised by specialties. These and other factors encourage silo-working, as stakeholders at all levels struggle to deliver on their core objectives.

Enlightened governments have begun to recognise the value of greater strategic alignment; for example, many are integrating health and social care strategies, budgets and services to cope with the challenges of an ageing society. In a similar way, digital health is creating strate-

gic co-dependencies between technological innovation and healthcare. With the digitisation of other government services, such as education, transport and the environment, broader connected strategies offer even more opportunities to make better use of public money for the benefit of citizens, although overcoming traditional territories will be a challenge.

The rapid pace of digital change and growing service demands have also heightened the need for interdisciplinary expertise and work practices. Connecting people in a way that allows them to benefit from knowledge in different areas is vital for articulating important codependencies, different ways of working and mutual goals. Our research on transformational change programmes points to misalignments in these understandings as a key reason why digital projects fail and why it is important to overcome them early in the innovation lifecycle. Building this lateral thinking is a key aim of our professional learning programmes, including the NHS Digital Academy and the Masters in Global eHealth, which bring together doctors, nurses, civil servants, innovators and computing professionals involved in designing and delivering health and social care informatics, supported by expert tutors from different backgrounds.

In my talk at the WHO, I referred to the risks of basing digital health strategy and procurement on industry hype and alluring prototypes. This is a common temptation in both the public and private sectors when trying to embrace innovation and be ‘ahead of the curve’.

Although there are risks involved in all new ventures, using public money to buy promises is irresponsible government, while Silicon Valley’s call to “move fast and break things” may be inappropriate in the safety critical context of healthcare.

Digital health leaders need to engage in constructive cynicism – being open to innovation whilst also asking tough questions about what is being offered and what evidence there is of its likely impacts. Evidence-based decision-making goes hand in hand with this need to be critical. It is somewhat ironic that in the healthcare sector, where evidence-based medicine has become the norm, we so often fail to do evidence-based policymaking or procurement. Knowing how to acquire, judge and use evidence is vital for healthcare leaders but few have the training or skills to be able to do this well.

In the presentation, I discussed the need for evaluation to be integrated throughout the innovation lifecycle, helping to strengthen quality through cycles of insight building and iteration, as well as informing difficult decisions about disinvestment. I also pointed to recent guidelines for evaluating digital health from the UK’s National Institute for Clinical Evidence and recommended partnering with academics to develop an evaluation strategy before embarking on expensive new projects and programmes.

By accountability, I was referring to several related issues. One is the public accountability of health leaders in an era where finances are already being stretched to the maximum. Linked to the point we’ve just discussed, it is important to place the onus on technology vendors and commissioners to provide evidence of the benefits of their products or services before spending money on them. At the very least, this requires being able to explain the theorised pathways through which a new digital platform, tool or service is expected to influence outcomes, backed by previous evidence and decent modelling, as well as having clear plans for audit and evaluation. Accountability also relates to issues of institutional governance and professional ethics, which I’ll discuss in answer to your next question.

You also emphasized the importance of “responsible and ethical innovation”. On what principles should be this be based in digital health?

Many relevant frameworks exist in medical ethics, bioethics and now digital and data ethics, while the broader Framework for Responsible Research and Innovation advocated by the European Commission is gaining traction. None are entirely comprehensive, as situations differ across contexts and technology types; for example, using hardware or software as a medical device, linking pseudonymised data for populationbased research, deploying patient records in clinical care, or applying algorithms and AI to assess risk or generate recommendations, to name but a few. These also overlap with laws and regulations around data protection and medical device safety, as well as issues like human rights and avoidance of fraud. Detailing all of these would take more time than we have in this piece, but there are some key principles that can be summarised as

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questions for digital health: • Drawing from biomedical ethics – is this digital intervention or use of data in the patient’s interest; has consent been given; am I avoiding harm, is it fair to this and other patients?

• Following principles of good governance – are the purposes transparent, are the data users accountable, has there been sufficient participation from data subjects or consumers; have conflicts of interest been avoided?

• Drawing on ethics in health data research – can we demonstrate that this initiative is trusted and trustworthy, that we are using only the minimum data required to answer the question; that consent and withdrawal options have been provided for identifiable data and evidence of assent for anonymised data; is the balance of benefits for data subjects and data users fair and reciprocal?

• From ethics in software development – has privacy-by-design been built into the interfaces or data management systems; have the clinical algorithms been shown to be accurate, safe and unbiased?

• From a social equity perspective – is this likely to reduce, perpetuate or increase the digital health divide?

• From a human rights perspective – is this compromising the right to privacy, autonomy or fair access?

• In terms of responsible innovation – does it align with society’s values, needs and expectations; have the future implications of these innovations been considered to avoid issues like discrimination, inequality or harm; are helpful interventions scalable, fairly applied and sustainable?

This is a non-exhaustive list, of course, and the different principles and frameworks overlap.

In my talk, I also drew leaders’ attention to the importance of avoiding conflicts of interest in digital health, or even the appearance of them, when engaging with industry. This is a serious problem in digital health and the ‘revolving door’ between governments and corporations is well recognised. Mixing public service and private profit is to be avoided, as is selling patient data to industry except under the strictest conditions of anonymity and with a clear public benefit.

Regulations are often far behind technologies: governments try to set standards

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when many technologies have been already adopted on the market. Interoperability has become the biggest challenge.

How can we tackle these issues?

Firstly, let’s consider interoperability. Although things are improving, problems with incompatible software, hardware and data (structure, vocabulary, clinical coding) continue to hold back the vision of a connected, efficient, effective digital health ecosystem. Many government strategies over the years have included a commitment to interoperability, or even mandated it as a condition of procurement, yet successive policy documents continue to present this as an aspiration rather than a reality. One of the key challenges is aligning strategy with enforcement, particularly when new and exciting tools come along. It is also important to recognise the scale and complexity of the existing digital landscape within health organisations, where incompatible legacy systems can take years to replace (ironically the greatest problem for ‘early adopter’ countries). Requirements for data portability and sharing under GDPR are creating momentum, while evolving standards like FHIR offer a glimpse of consensus, but in the meantime innovations such open APIs and natural language processing will be needed to bridge the gap.

Longstanding issues of territory and ownership are also relevant to interoperability in a market where powerful vendors have traditionally been able to lock

clients into their portfolio offerings; indeed, most government IT is still procured from a handful of companies. Creating a climate that is receptive to independent providers has helped in many countries, but with the emergence of ‘as a service’ software, infrastructure and data hosting, along with the expansion of enterprise systems, traditional centralising forces are now taking new forms. The growing risk of cyber-attacks, competing operational demands and technology recruitment difficulties in the health sector make a compelling case for outsourcing IT, and within a modular vendor-led ecosystem achieving interoperability is a realistic prospect. However, there are significant platform monopoly issues which have yet to be resolved, as well as unanswered questions about data guardianship, which need further scrutiny.

Coming back to the issue of connectedness, over the last few years – working with organisations such as the European Federation for Medical Informatics – I have also been promoting the concept of Social Interoperability, recognising that technical and regulatory harmonisation also require changes in working practices, professional communication and cultures.

In terms of regulation, keeping up with the pace of innovation is certainly a challenge. For example, while GDPR has helped to create generic principles around data protection, the regulation of consumer privacy is still uncertain. Similarly, while ‘software as a medical de-

What are the key success factors for effective governance of national digitalization strategy to avoid fragmentation, duplication of efforts, non-strategic investment decisions, a lack of common standards and waste of resources? Claudia Pagliari answers: connected people, connected strategies, evidence-based decision making, constructive cynicism, shared decision making and accountability.

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vice’ is now subject to regulation, crossover innovations like digital implants and new types of impenetrable AI can make this difficult to enforce. Likewise, data protection regulations covering mobile health apps and IoT may fail to capture downstream privacy breaches caused by third party data sharing and jigsaw reidentification. What has been learned is that industry self-regulation is a blunt instrument, as we found recently in our studies of consumer genetic testing services sold online, which crosses several regulatory boundaries, leaving ethical gaps which can be exploited.

On the up-side, digital developments are beginning to offer solutions for ethical governance; for example, using blockchain to support regulatory compliance through smart contracts, apps for spotting medication fraud, or AI bots for crawling privacy terms and conditions.

In digitalisation of healthcare many authorities choose the model of public-private cooperation. On what principles should it be based?

There is no one right or best model in this context. As already noted, there are often sensible and pragmatic reasons for devolving digital responsibilities to companies specialising in data management and cybersecurity, and innovations developed by the tech sector can help to support public health services and patient self-care. However, as I’ve already noted, making sure money is spent wisely, ethically and in the interest of patients is essential, as is transparency, accountability and avoiding conflicts of interest. Ensuring that health systems have the best legal expertise available to them when negotiating contracts is also vital.

Not only can this help to avoid clauses or omissions that later bring unexpected costs, it is also needed for clarifying issues around intellectual property (IP), which are often overlooked. In digital health it is important to establish whether you are being sold an existing solution or invited to collaborate in creating one.

Health organisations often discover the latter after many hours of staff time and knowledge have been invested, yet typically receive no financial return once the product is commercialised and sold elsewhere. To ensure sustainable public-sector health services, transparent and reciprocal IP models are needed, as well as appropriate training for staff involved in the procurement process.

There are signs of » ������������������� growing maturity in the digitalisation strategies and capabilities in many European countries.« What does “sustainable digitalisation of healthcare” mean for you?

Sustainability is about making the best use of digital innovations for delivering high quality, person-centred, evidencebased care to a growing number of patients, whilst also containing costs. This calls for whole systems thinking – recognising the value of collaboration across and within sectors, avoiding duplication when care processes have already been optimised, using data to understand and control waste, using technology to engage patients as partners, and being cautious about expensive ‘cutting edge’ innovations when proven, frugal ones exist. For example, in my talk I referred to reported benefits arising from changes to surgeons’ rotas, compared with investing in expensive surgical robots. Similarly, cheap innovations like text messaging may be as effective as expensive ones like wearables for promoting medication compliance or health behaviour change. Sustainability is also about recognising that the global health workforce cannot grow at the same rate as demand, so being smart and strategic in the use of digital innovations is essential, which requires us to gain a better understanding of users’ needs, preferences and behaviours.

Machine learning and artificial intelligence have potential to support sustainability through automating tasks such as image screening or administrative processes, as well as using data and algorithms to compute risks and offer tailored recommendations. This might, for example, help to decrease unnecessary drug use. At the same time, the area is arguably over-hyped and remains fraught

with ethical tensions and uncertainties over algorithmic transparency, humanrobot working, and patient rights, which still need to be resolved. Using technology to deliver services out of hours also has potential to cut waiting times and triage care, while supporting older people to live well in their own homes has both economic and societal benefits.

Let’s talk about some case studies.

Which components of the digitalisation in the NHS do you evaluate positively and which ones – negatively? What could be improved?

This is an enormous area and it would be impossible to comment on all of the digital projects and programmes that are underway across the UK. It’s also important to recognise that the opportunities and challenges facing the NHS are common to many countries.

From a contextual perspective, the UK benefits from a single-provider health system, presenting favourable conditions for implementing digital strategies and innovations, although wholesystem interoperability has proven elusive, for some of the reasons I’ve already mentioned. Positive recent developments include the rollout of personal health records and teleconsulting in parts of the NHS, which are improving patient empowerment, involvement, choice, and access to care; the new NHS App library, which provides a gateway to trustworthy consumer health tools, and the launch of the NHS app, allowing patients to access digital services from a smartphone. Recent moves to bring together digital leaders through initiatives such as the NHS Digital Academy and the establishment of NHSX also hold promise for addressing some of the issues discussed already around workforce and connected people.

Large government investments in health data research have also placed the UK at an advantage, particularly in regions with a long history of using unique patient identifiers for record linkage, such as Scotland, and major programmes in genomics and artificial intelligence are now seeking to make better use of these data for biomedical innovation. Negative cases typically involve failures in large scale IT programmes and procurements, controversies over the governance of patient data sharing and major cyber-incidents, such as WannaCry, although debates over AI and chatbots are beginning to occupy the media.

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Rather than trying to list ‘good’ and ‘bad’ projects, it may be more helpful to return to the question of what challenges digital health initiatives can face, using examples from both the UK and elsewhere.

Scale and Complexity: The NHS National Programme for IT (2005-13) is arguably the poster child for negative digital health experiences. This was a hugely ambitious project which aimed to develop and centralise NHS IT in collaboration with selected suppliers. The key challenges were the sheer scale and expected speed of the programme, which involved multiple changes to systems, people and processes, only a fraction of which had been anticipated. It faced resistance from frontline health workers, huge over-runs in delivery times and costs, expensive legal disputes with suppliers, interoperability challenges, media attacks, public fears over data sharing and much more, and was eventually shelved, at an estimated cost of £10bn.

The unrealistic timescales and budgets mentioned earlier in this interview were partly to blame, although the failure has also been attributed to top-down leadership and lack of inclusive design.

Contracts and Procurement: In Australia, the implementation of the Queensland public sector payroll system (200613), which includes the health sector, is an excellent example of the importance of effective contract management. The project was hit by huge cost overruns and delays, escalating an original budget of $6M to an eye watering $1.2 billion.

The government attempted to sue the suppliers for misrepresenting their ability to deliver on time, but a clause in the contract absolved them of responsibility and the state was, paradoxically, forced to pay compensation. This has been described as the worst public administration failure in the country’s history.

Public Consultation and Ethical Governance: Back in the UK, the care. data project (2013-16), which aimed to create a centralised database of healthcare records for secondary uses, was axed following media allegations that patient information could be sold to private companies and concerns over privacy and choice. More recently, public anxiety was raised when it was discovered that a London hospital had allowed Google’s DeepMind to access over a million non-con-

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sented patient records to develop an app, leading to a rebuke from the UK Information Commissioner. Cases like this show the importance of trust and trustworthiness when planning future uses of health data for research, government analytics and innovation, and how vital it is to fully consult with the public and take account of their concerns. Cultural issues may also play a role; for example, Estonia’s eHealth record initiative bears similarities to care.data but has encountered little public resistance, possibly due to high levels of trust in government and the explicit role of the patient as a data user, also illustrating the importance of reciprocity.

Translating research and innovation to impact: Billions in European Commission research and development funding has been committed to digital health and care projects in the last ten years. Although this has produced some valuable scientific insights and exciting innovations, it is an open secret that very few of these have translated to substantive changes in the quality or costeffectiveness of healthcare. Progress is at best incremental and while there have been some cumulative benefits – for example in informing digital service integration and ‘healthy aging’ strategies, attributing these to particular funding streams is challenging and probably demands new methodologies for assessing impact. Most funded projects cease after grant funding has ended, pointing to a stark gap between sponsorship of science and innovation and sponsorship of healthcare services, once again raising the need for connected strategies, as mentioned earlier.

It’s important to remember that successful digital innovations in the public sector rarely get the same attention as costly or controversial ones. Nevertheless, across governments and healthcare systems there has been a widespread failure to learn from past mistakes, which needs to be corrected if we are to move forward effectively, sustainably and ethically. This having been said, there are signs of growing maturity in the digitalisation strategies and capabilities in many European countries, which may not be obvious from the slow pace of technological change. Examples are the development of more robust guidelines for digital design and procurement in the NHS, which strengthen the foundations for further progress.

Let’s wrap up: what should a roadmap for the digitalization of national health systems look like?

There has been a long history of digital road maps in Europe and around the world, with new ones appearing every year. There are common threads in all of them, some incremental, some disruptive, and some simply repetitive.

Achieving interoperability continues to be a dominant theme, decades after first making it to the top of the health IT agenda in many countries. Telemedicine has regained momentum, with better digital communications and a stronger evidence-base, after a period of unfulfilled promise. Giving patients access to their health records is finally becoming standard practice, despite having been possible in principle for many years. Personalised, predictive and genetic medicine are starting to turn the corner from research to practice, after a long phase of hype and unfulfilled expectations. Likewise, robots and chatbots are gradually starting to normalise and scale, although we are still some way towards achieving safe use of these technologies in most areas. Artificial intelligence is a dominant theme in recent roadmaps, with varying degrees of realism about when this will yield benefits. And the concept of using data and analytics to enable the ‘learning health system’ continues to feature but has proven difficult to shift from theory to reality. A broader desire to see digital service development and integration as enablers of citizen and patient health is also evident, as in NHS England’s “Empower the Person: Roadmap for digital health and care services”. In a similar vein, many countries are seeing the value of big data for understanding the social determinants of health, to inform policies that can benefit populations and communities. Alongside this is a growing awareness of the cyber-threats facing health systems, although investment in the human and technological resources needed to combat this still falls far short of the challenge.

Meanwhile, governments continue to wrestle with questions over digital centralisation versus distribution and about the balance of technologies developed and managed by the health service or by the private sector. Spectacular failures involving home-grown IT, coupled with the drip-fed mantra that ‘big tech knows best’, have arguably disempowered the public sector by creating inse-

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curity and decreasing investment in inhouse skills and infrastructure, thus increasing its dependency on commercial suppliers. How this relationship pans out in the platform economy remains to be seen, but now is the time to strengthen the expertise needed to recognise the implications and ensure that investments are strategically sound, technologically robust, evidence-informed and legally watertight.

Returning to the need for connected strategies, it is useful to look beyond the obvious innovations in digital health, and consider what else needs to change for sustainable, responsible, equitable healthcare. This includes innovations in workforce optimisation, identity management, fraud prevention, cybersecurity, tackling online health risks and many other areas. For example, our research on human resource information systems in healthcare suggests their considerable potential for cost savings and care quality improvements.

It is important to remember the different starting positions of countries in Europe. While a degree of leapfrogging may be possible in regions where digital health is still a fairly new concept, entrenched organisational structures and cultures can create unfavourable conditions for innovation. For example, countries like the UK and Denmark, where primary care computing is well established, are considerably ahead of countries like Germany and Austria, where negative attitudes to data sharing and strongly hierarchical health systems prevail. The balance of public to private healthcare spending also affects the value proposition of personal health technologies in different regions, which are likely to affect their uptake.

Europeans are now in an excellent position to harness the power of digital to improve and sustain their public health systems, providing high quality care for all citizens despite the pressures of an ageing population. The stakes are high,

however, and making wise choices about where to invest for the best value and impact can challenging in an area which often promises much but fails to deliver.

Critical, connected, evidence-informed and accountable leadership will help us to move forward confidently, cost-effectively and responsibly.

electronic patient records, and provide health services and preventive measures to remote and underserved populations.

Perhaps most importantly, however, digital health is empowering communities and individuals to improve their health and well-being in unprecedented ways. They are accessing services in environments that are comfortable and familiar as care is brought out of hospitals

and closer to home, and at times that are convenient and meaningful by using, for example, smart devices to track, manage and improve their health.

Overall, digital health helps empower patients to take control of their health and reaches out to communities in sparsely populated areas. It also supports health professionals and institutions to be more effective and efficient. 

Thank you for your time.  Professor Claudia Pagliari is a senior lecturer and researcher within the Usher Institute of Population Health Sciences and Informatics at the University of Edinburgh, where she directs the eHealth Interdisciplinary Research Group and the MSc in Global eHealth. She holds a first class degree in Psychology from the University of Ulster, a PhD in Psychology from the University of Edinburgh and was elected Fellow of the Royal College of Physicians of Edinburgh in 2012.

She is a member of the UK College of Experts in Health Informatics, the British Computer Society and the NHS Digital Academy (theme leader) and has held advisory roles with the American Health Information Management Association, the European Commission (scientific expert) and other agencies.

Opening speech at the WHO Symposium on the Future of Digital Health Systems in the European Region by Dr Zsuzsanna Jakab, WHO Regional Director for Europe In public health, as in many other sectors, technology and innovation have begun to flow through everything we do. Together here this week, we will seize the opportunity for meaningful discourse on how technology and digitalization support health system development and public health.

WHO firmly believes that digitalizing health systems is a key component in achieving universal health coverage, which is based on the belief that all people should have access to the health services they need Ė to disease prevention, health promotion, rehabilitation and palliative care Ė without the risk of financial ruin or impoverishment. In this context, digital health has an important role to play in improving the reach, impact and efficiency of modern health care and in delivering patient-centred services.

Digital health is increasingly utilized to overcome social and demographic stresses, address inequity and health insecurity, improve training of the health workforce, strengthen public health surveillance, link databases, use joined-up

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It is not enough to just have a good idea or a nice implementation in one place How should health policy adapt to “disruptive” technologies?

A brief interview with Nick Fahy, a senior researcher at the University of Oxford and a consultant in health policy and systems, board member of the European Health Forum Gastein.

Digital health innovations are booming, but so far this market remains unregulated. What is to be done to ensure sustainable digitalization in healthcare?

The critical point with digital innovation is that it is never just digital. It also requires changes to how people work, to organizations, and to the system as a whole – such as changing financial systems, indeed. So when looking for digital health transformation, it is vital to take this whole-systems perspective and recognize the complexity of the changes involved.

How to find a balance between startups willing to change healthcare through innovations and conservative healthcare policy?

We are observing quick progress in the field of AI in healthcare. Algorithms can diagnose diseased on an early stage; wearables make prevention more personalized and effective.

The bridge between innovation and established practice is evidence. It is not enough to just have a good idea or a nice implementation in one place. If we can accompany innovations with effective monitoring and means of generating evidence about how they compare to exist-

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ing practice, that will provide the basis of a transition from innovative examples to changing practice more widely.

olutionized entire sectors of society; of course, they have the potential to help achieve the SDGs.

How should health policy adapt to these “disruptive” technologies?

Smartwatches can measure ECG or the quality of health – regardless of health policies, innovations are adapted by patients who can afford them. What should be done to prevent a digital divide in healthcare?

We need to invest not just in innovations, but in systematic and structured mechanisms for monitoring their uptake and impact in practice.

How can digitalization contribute to achieving the UN’s SDGs?

There is clearly potential for digital technologies to help improve the effectiveness and efficiency of health systems as with all other public services, and health has proven to be a particularly complex and challenging area to realize that potential. But these technologies have rev-

» We need to monitor the uptake of digital technologies and their impact in practice.«

Equity and equality are not the same. People will always have different circumstances and be suited by different things. The ideal is not to treat everyone as though they are identical; the ideal is to have an individualized approach that is adapted to different people and suits their particular circumstances and needs.

Could you please complete the sentence: Healthcare systems in the digital age should…

…adapt and innovate to take advantage of digital technologies.  The European Health Forum Gastein 2019 “A healthy dose of disruption? Transformative change for health and societal well-being” will take place from 2–4 October 2019, in Bad Hofgastein, Austria. This year’s agenda is meant to spur the dialogue on how an appropriate level of disruption can effectively revolutionize the health sector. Registration: www.ehfg.org.

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Rethinking Workforce Skills To Become Ready For Future Digitalisation disrupts the way doctors and nurses used to work. We have to get ready for the redistribution of tasks, new roles and forms of teamwork within and beyond the health system. James Buchan – Senior Visiting Fellow at The Health Foundation – reveals during the European Health Forum Gastein how to adapt health workforce to new models of care.

What new skills do health workforce need to adapt to the digitalisation of healthcare?

The important starting point is to be clear that digitalisation should be an enabler for effective and efficient delivery of care; it should not “drive” the process of care delivery. As such there is a need both for the health workforce to have a

good understanding of what digitalization will mean in practice, and also that they are trained to make the best use of the enabling technologies that exist – this includes data literacy, and acknowledging that patients and clients will “own” the data, or be increasingly be as aware of data, as the health professional. Some health professionals will also be directly involved in identifying scope for new

types of digitalisation and designing and implementing these innovations.

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conferences As we gain access to electronic health records, the role of doctors is changing.

Now, they are also patients’ coaches and navigators. What are the chances and threats behind this change in medicine?

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The Health workforce » ��������������������� need a good understanding of what digitalisation will mean in practice.«

There are obvious risks related to data protection which will have to be addressed as a priority. In addition, it will be the case that some health professionals may resist using these records if they believe it will undermine their status and role, or because they do not wish to undertake training. This has already happened in some countries. So part of the process must be awareness-raising both amongst professionals and the public about the positive potential benefits of EHR, and also clear messages about data protection and use. The opportunities are clear, in terms of improved efficiencies and effectiveness of individual care, plus the scope to make use of aggregated and standardised data to inform clinical practice and health policy. The risks relate to diseconomies and inefficiencies if there are multiple overlapping technologies used to generate EHR; risks of data misuse, and problems if health workers are not appropriately trained in data inputting and in EHR interpretation.

teams. Should doctors and nurses now study also informatics to get used to new technologies?

Healthcare professionals will also have to learn how to work together with artificial intelligence. AI will be a part of care

Too often, the focus on skill mix change takes a narrow “technical” perspective, based on data analysis being used

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Developing a basic understanding of informatics should be part of the curriculum for undergraduate health professionals. Equally importantly, it must be part of the refresher training/continuous professional development of those already in the workforce.

How to implement skill-mix innovation in healthcare settings?

to identify skills gaps or scope for skills changes. This is an important building block, but the reality is that skill mix change must also take account of broader change management. As such, it must be based on a clear understanding of both the costs and benefits of the change, what the change will mean for patients, clients and staff, and what are the barriers and enablers to achieving sustained change and improvement. Some skill mix changes may require a national shift in regulation and legislation. This should not prevent the change being considered, planned and implemented, but points to the scale of efforts that may be required.

The key point is to keep focused on the benefits of the change, as the process of implementation is managed, and to have an effective communications strategy.

For what kind of disruptions do health workforce have to be ready in the future?

External political and economic shocks will continue to impact, often when least expected. The internal disruption caused by funding changes, and population health priority change will also be ever-present. In this context, technology can be a tool for adapting to, and dealing with these changes. 

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Stay at home. Technology will take care of everything else Every year, when the number of flu cases grows, healthcare centers burst at the seams. Paradoxically, in order to get medical advice, patients infect other people in waiting rooms, on public transport and in workplaces. The coronavirus pandemic made us realize that we could do it all differently. So, stay at home to protect yourself, others and medical staff. Lighten the load on the healthcare system now and in the future.

Invisible mistakes It is the same every autumn and winter: crowds in outpatient clinics and waiting rooms, coughing and sneezing on people in offices and cinemas, on buses, at schools and concerts. It is a medical fact that in the case of seasonal flu, a patient statistically infects one person. Therefore, the growth in the number of cases

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is not exponential (as it is for the coronavirus), but linear. According to data collected by the World Health Organization, every year about 1 billion people come down with the flu, 3–5 million cases are severe and approximately 290,000– 650,000 people die. These numbers show us how little has changed in recent years to improve the prevention of contagious diseases, such as the flu.

We have vaccines, but a relatively small percentage of the population uses them. Eurostat data show that on average 43% of Europeans aged 65 or older are vaccinated against the flu. There are huge differences across countries.

In the United Kingdom, it is over 70%, in Germany it is half that number and in Estonia it is just 3%. On the one hand, we can imagine what the mortality rate would look like without vaccines and on the other, alarming statistics do not serve as sufficient motivation, even for people in risk groups.

The coronavirus pandemic came as a shock and an unpleasant lesson in preventive healthcare for all of us. Not only for selected people in risk groups, but for every person – without exceptions. What contagious disease experts have been saying for a long time is finally reaching politicians and gaining social recognition. Science and medicine have turned out to be disciplines of strategic importance, not only from the point of view of health and life, but also social welfare and economic development. We did not listen to epidemiologists because they were talking about an abstract risk which could be seen and experienced first-hand.

We got accustomed to the common cold and the flu, because they have always accompanied us. We used to regard visions of larger epidemics as far-fetched scenarios which might happen someday, but not in our lifetime. Many contagious diseases were eliminated thanks to vaccines, so the present generation does not know how many victims used to be claimed by measles or smallpox.

Responsibility serves as prevention We have also forgotten that from the point of view of health, everyone is responsible not only for themselves, but also for others. When we make nothing of the flu, take medications which minimize its symptoms and keep participating in social life (go to work, the cinema or school), we put other people at risk of being infected and we become the source of infection ourselves. It is not viruses that

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infect people, it is people who infect others with viruses. During the SARS-CoV2 pandemic, the most effective medicine has turned out to be limiting social contacts, keeping physical distance and washing your hands. These simple procedures were almost forgotten in today’s modern world that is under continuous development. In many countries, citizens have obeyed administration orders and suspended social contact for some time to flatten the infection curve and avoid overburdening the healthcare system within a short period of time. Today, it is easier thanks to new IT and communication solutions. It is also a great lesson for the future, a lesson in solidarity, maturity and awareness. Instead of infecting colleagues with the flu during business meetings, you can just as well organize a teleconference from your own home. It is feasible in the case of some jobs, but of course not all of them. Children can participate in online lessons, even when only one person is sick and other students attend classes in the normal way. In this case, it would be hard to say that “necessity is the mother of invention”. Inventions have been available for a long time, but it is the necessity that appeared out of nowhere.

You do not need to visit a doctor right after your nose starts running or as soon as you get a sore throat. When your health condition is stable, it is enough to consult a doctor using telemedical solu-

tions. On the basis of your medical history, the doctor can initially determine your health condition at a distance and decide whether it is necessary for you to visit a doctor’s office. The doctor can also prescribe medications by sending you an electronic prescription. Patients have nothing to fear, doctors are knowledgeable enough to make an initial diagnosis based on a conversation with the patient.

When we stay at home, we keep the virus in check. In this way, we do not infect other people, especially those who have a poorer prognosis because they are weaker or have existing conditions. This way we do not overburden the healthcare system, so the waiting time is shorter for those who really need help. The things that many people were not aware of in the past are becoming obvious now and should not be forgotten in the future.

Digital health technologies are here to be used in a clever way. We can see that telemedicine is not a technology which exists for its own sake, but a part of preventive healthcare and a method of increasing the effectiveness of the healthcare system.

The message to “stay at home” should forever change the behavior of people who come down with the flu or the common cold. Stay at home, book an online doctor’s appointment and behave responsibly. This solidarity and joint responsibility can become new values in healthcare after the coronavirus pandemic. 

» An online medical consultation is not only a comfortable, but also a responsible way of using medical services.«

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Where are the long-awaited benefits of digitization?

Several dozen years ago, when health care computerization was beginning, hopes related to digitisation were high. Computers were to automate many activities. And so they do, but a fairly common effect has consumed the added value of IT. Today, a statistical physician is more and more burdened with reporting obligations. During a patient’s visit, they spend a lot of time in front of the computer filling in electronic medical records and entering data. After observing the work of 36 doctors in one Swiss hospital, researchers drew alarming conclusions, then published in the Annals of Internal Medicine. The doctors spent 5.2 hours per shift at the computer, 1.7 hours with the patient and 13 minutes on both activities simultaneously. Interestingly, research conducted half a century ago suggests that doctors devoted a similar amount of time to patients as today.

Where are the profits of the huge technological progress in health care?

Today, digitisation primarily benefits the payer, who receives consistent and full reporting data, and the health care system, meaning its organisational structure. What about doctors? Why has computerization failed to reduce the time required for paperwork? A computer should make things faster, thanks to templates and dictionaries, and more convenient - since the data entered were supposed to be automatically used in various reports. Hardly anyone believes that this is what happened. Unfortunately, the advantages of technology have been uti-

lized differently than expected by medical staff.

Health care has become the victim of the rebound effect known in nature, described for the first time by the British economist William Stanley Jevons. A classic example of this phenomenon is electricity. Why does energy consumption in some cases not decrease despite the introduction of energy-saving light bulbs? The reason is not that new bulbs are not better than the old ones. It is because users simply leave them on for longer, knowing that... they are energyefficient. Nowadays, a similar problem is affecting health care. In the 1990s, when a significant number of reports were made on paper, a limited number of documents was required. The payer could not demand more, as it would exceed the capabilities of doctors and health centres.

Today, the National Health Fund and organisations in the healthcare market may impose further requirements, knowing that service providers have the tools (computers) to develop even the most sophisticated reports. In this way, the time that was to be saved by the doctor has been absorbed by the rebound effect.

Patients may also face the same problem in the future. Technologies are improving at a great pace. Algorithms will perform preliminary data analysis, artificial intelligence will help the doctor make therapeutic decisions, voice assistants will convert natural speech into structured medical records without having to tediously type data with the keyboard, many classic measurements of patient’s health parameters will be entered into the e-file from devices existing in the patient’s home. Theoretically, the doctor will be able to finally take care of the patient and devote more time to them.

Will the proportions reverse and will the doctor be able to talk to patients and focus on their worries in the new reality?

Will health care be therefore more human than today?

Not necessarily. However, this “gift of time” obtained through technology can be wasted, as was also described by Eric Topol in his latest book. An efficient doctor with time reserves will have to see more and more patients and in this way everything will remain the same. Technology alone is not enough to make medicine patient-friendly, which we have already learned in recent years. This should be borne in mind by those responsible for health policy. 

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Digital health 2020 We asked digital health leaders what trends will dominate medical technologies in 2020. Perspectives of John Sharp (Thought Advisory at the Personal Connected Health Alliance), Koen Kas Healthcare (Futurist and CEO/Founder of the Healthskouts), and Lionel Reichardt known as Pharmageek (expert in the digital transformation of the pharma industry), Denise Silber (Founder of the Doctors 2.0 & You), and Professor Shafi Ahmed (surgeon, futurist, and innovator).

John Sharp Director and Thought Advisory at the Personal Connected Health Alliance

Digital Health Trends in 2020 will include many patient-facing innovations.

John Sharp, among many others, is an adjunct faculty at the Kent State University (Health Informatics Program) and a member of the International Editorial Board at the ICT&Health International.

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Existing trends, which are at the pilot phase, will begin to scale. For instance, virtual reality for pain control now has enough evidence to be recommended as an alternative for many pain control treatments. It could become a first-line treatment and alternative to opioids in some cases.

Digital health treatments, which are already scaling – such as managing and prevention of chronic conditions like Type 2 Diabetes – will continue to gain acceptance among employers, insurers, and perhaps even some government programs, and thus become broadly available. These virtual coaching apps will broaden their scope to many chronic conditions. For example, COPD, asthma, hypertension, and heart disease, especially as value-based care begins to have an impact.

Remote patient monitoring will also begin to scale in these value-based programs as a method to monitor chronic conditions on a daily or continuous basis, and as the data can be summarized in a dashboard for providers and patients.

Telehealth visits will become more widely adopted both for one-off consultations but also for more continuous contact for patients with their healthcare providers. Also, better quality standards will be created and applied for telehealth visits.

Finally, patients will generally be more involved in their care. They will also take a more active role in digital health projects, including becoming active consumers, participating on advisory boards, and the growth of co-design.

Koen Kas Healthcare Futurist and CEO/Founder of the Healthskouts

I won’t make a classical list of trends like AI, 5G, VR, and so on. It is the list from me, and my team’s experience working

Koen Kas has published two books. „Sick no more” describes how we will transition from reactive sick-care to pro-active healthcare. „Your guide to Delight” is a roadmap towards Creating health, dealing with change, introducing our personal Digital Twin.

W H AT ’ S YO U R O P I N I O N ?

with care organizations, hospitals, pharma, tech plays entering the healthcare space. From helping start-ups and scaleups, big incumbents, and governments to absorb and adopt digital health developments in their organizations.

Here are my personal five digital health trends in 2020: • Mobile health apps reimbursements will take off. Legislation in Germany got a lot of publicity, but other countries are getting ready as well. There is a vast interest in the certified digital health apps database we curate, for instance. It is now being used in pharma to teach how the emerging digital revolution gets shape.

We start to see the number of entries increases beyond linear.

• First, real-world datasets are supporting the benefits of adopting digital health in the clinic. A good example is a proof that VR can treat acute pain beyond a well-controlled hospital setting.

Such data will build trust. I believe that becomes the key trend for 2020 • I think we’re going to experience a shift from a “push attitude” of the digital health technologies (driven by engineers in startups and tech companies) to the “pull-attitude” (inspired by actual health care providers like physicians, specialists, and nurses). Medical professionals can describe use cases best and know what is needed to remove friction in the healthcare system.

• As every company is bound to become a health company, the non-obvious, non-traditional plays will speed up the adoption rate of digital health tools, pointing them towards consumers. Think food, beverage, dairy companies capturing consumer mental state to adapt their offering. See how Black+Decker worked with Pillo Health to develop a technology to help adults and their caregivers proactively manage their health at home.

• 2020 will be the year the concept of a “Human digital twin” will become a thing. A digital twin is a real-time replica of something in the physical world.

In healthcare, that replica is the lifelong data record of an individual. Digital twins can assist doctors and pharma in determining the possibilities for a successful outcome of a procedure or treatment, help make therapy decisions, and manage chronic diseases. Ultimately, digital twins will become patient/citizen companions to keep them healthy.

A bit like Baymax in Disney’s Big Hero 6. Digital twins are fed with data from emerging digital health tools.

Lionel Reichardt spent the past 15 years working within the pharmaceutical industry. Today he synthesizes these experiences to rethink the customer journey and build new models of promotion and information in healthcare.

Lionel Reichardt Pharmageek, Founder of the 7C’s Health

The big trend for 2020 is that digital health will be no more trend. It’s been almost ten years now that digital health lives at the pace of technology trends and buzzwords. Patient empowerment, connected health, artificial intelligence, blockchain, virtual reality – so many topics to feed the business of conferences and ambient communication. Here comes the time for maturity. The time for institutions to frame digital health and make it possible on a larger scale. The time for scientific publications to relay digital health research and establish the potential and reality of this sector. The time for training to allow healthcare professionals to be able to build, use, and prescribe these solutions. The time for digital therapeutics to target specific issues to improve the health of individuals and populations. The time for patients to be taken seriously in their quest for greater autonomy and better management of their health. Digital health will finally begin to keep its promises and become a reality.

are likely to be around for decades before the average patient gets to benefit from them, if at all. Health systems, providers, and other stakeholders have to keep abreast of multiple technologies and juggle with many uncertainties.

Denise Silber is a global thought leader and social media influencer based in Paris, a native New Yorker. She has devoted 20+ years of her professional life to digital health intending to improve life for patients.

Denise Silber Founder of the Doctors 2.0 & You

It’s a New Year’s tradition to announce new trends in digital health technology.

Yet, the previous year’s trends don’t really disappear. Digital health technologies

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Artificial Intelligence is grabbing a lot of attention in healthcare. It occupies the headlines with spectacular claims, and its applications are endless: drug development, diagnosis, personalized medicine, patient monitoring. Babylon Health, with its AI chatbot, was valued at $2B in 2019. Sales for the health chatbot market were estimated at only $36.5M in 2018.

Nonetheless, Accenture predicts that AI will reach $6.6B in 2021.

A favorite of mine is Virtual Reality.

VR is not only effective; it is also riding on the societal trend of improving the human experience with tech. VR is segmented into education, training, and simulation on the one hand, and pain management, rehabilitation, and post-traumatic stress disorder on the other, and there are additional therapeutic benefits.

The global VR for the healthcare market was estimated at $260M in 2018 and could reach $3.4B by 2027.

Blockchain is a hot topic because it seems to respond to a worldwide cry for greater transparency and trust around health data. This market was estimated at $44.6M in 2017 and could grow at 67.1% per year through 2023.

At the same time, telemedicine, a decades-old, not particularly sophisticated technology, is coming of age. Between its B2B tele-expertise market (teleradiology, telepathology, telecardiology, teledermatology) and B2C real-time distance consultation and at-home diagnostics, telemedicine benefits from a growing societal need for on-demand service anywhere, anytime. The telemedicine market, forecast to reach $30.12 B by 2026, would be second only to another decades-old market, the electronic medical record, forecast at $38 B in 2025!

I will end with another digital health trend that interests us all. It is not a specific technology but a goal, and that is to achieve “human augmentation using technology.” Multiple product categories can help enhance a person’s cognitive and physical experiences: sensors, implants, exoskeletons, prosthetics.

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Professor Shafi Ahmed Surgeon, futurist and innovator

2019 was the era of hype. I think 2020 will be the year of real-world applications and there will be a fundamentally better understanding of AI and its possibilities and limitations. The NHS will have an investment of £250 million for an AI Lab to help solve some of healthcare’s toughest challenges, including earlier cancer detection, discovering new treatments, and relieving the workload on the NHS. 2020 will also see the launch of the first AI University in Abu Dhabi opening in September, educating some of the global workforces to deliver these ambitions.

2020 will be the year of the “surgical robot wars” as new players are coming into the market to challenge the domination of the Da Vinci. Cambridge Medical Robotics, Medtronic, Johnson & John-

Professor Shafi Ahmed is a multi-awardwinning surgeon, teacher, futurist, innovator, and entrepreneur. He is an international keynote speaker and is a faculty at Singularity University.

son/Google, Dexter amongst others will be releasing more affordable, modular and portable robots, which will reduce the capital’s costs and price per procedure.

We will also gain a better understanding of how to manage large amounts of patient data. The year 2019 highlighted issues of patient confidentiality with major tech firms. This needs a consensus in 2020 to allow data-driven healthcare to empower decision making for clinicians and patients.

The rollout of 5G will help facilitate the internet of medical things. It may be the missing piece of the jigsaw to allow wearables and sensors to assist real-time monitoring as well as powering telemedicine, telepresence and even telesurgery.

Virtual Reality will finally be offered to patients for a variety of medical illnesses that may be reimbursed by the payer as an alternative to conventional medical therapies. 

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Culture, UX/UI, education, accessibility.

Digitalization’s biggest barriers I asked five leaders in digital health change a simply question that has remained unanswered: what are the most common mistakes when implementing digital health solutions in a healthcare setting? Answers were given by Danielle Siarri, Rafael J. Grossmann, Shawna Butler, John Sharp and Paul Timmers.

Danielle Siarri, MSN, RN Nursing Informatics Specialist The biggest missteps in implementing a new IT system is not having the clinical staff onboard or creating a culture to produce a project champion. The end users need a voice on the project from initiation to the lessons learned. The last line manager can scuttle a project, so having

them on board is a must. The infrastructure plays a key part, with server capacity and how much electricity new tech can draw from a building all must be calculated. Good governance is key from the start to finishing the implementation, with all key players understanding how each request plays into the economics of the project. The task of moving a radial

dial button is not as simple as one might think, so ownership of the choice is paramount.

Rafael J. Grossmann, MD, FACS Clinical Advisor at Magic Leap As a surgeon, full time clinician and also healthcare futurist and innovator, I have a biased perspective. Obviously, like any new tool that should help patients and the relatives of the patients and achieve better outcomes, we all have to consider whether the solution is going to be beneficial for the patient, balancing the risks with the benefits. It is the same for any IT healthcare solution. I think that cost is a barrier, but one that will decrease over the coming years. We know that technol-

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» Technology that is too complex, especially if it requires too many steps, is not intuitive and user-friendly, also might not be good.« ogy develops exponentially, becoming better, faster, smaller and also less expensive. Another big difficulty that is also very common, especially in a US setting, is the regulator. Again, technology develops exponentially but the regulations do not keep pace with that development. The law is often not up-to-date. In the case of the US specifically, HIPAA (Health Insurance Portability and Accountability Act) regulations are very strict, potentially inhibiting the implementation of any potential solution. Anyone who wants to come up with a solution that will eventually have a real effect, needs to address HIPAA and the safety of the patient data very carefully. Another factor – education is a must. One of the mistakes is that we think that technology will be accepted naturally by all the players, which is not the case. We need to make sure that patients, providers, administrators and regulators are educated and understand the issues, problems and solutions. It is really important to have a culture change.

There are modern technologies that fail because they are introduced too quickly or without a proper cultural background.

I would say that, in general, these factors are the main barriers that we face. Technology that is too complex, especially if it requires too many steps, is not intuitive and user-friendly, also might not be good.

Innovations should improve how we care about patients, not add more clicks and work to be done, separating the clinicians from the patients. As the surgeon that performed the first live-streamed operation with Google Glass a few years ago, I think it is all about simplicity. It was easy, inexpensive and private – that is why it was successful.

Shawna Butler, RN MBA Nurse Economist Confusing digitization with transformation, and not engaging all participants in the care journey to unlock the potential of an IT system. Commonly, when organizations seek to implement a digital/IT solution they evaluate the current processes, workflows, activities, users, inputs, outcomes, and metrics and then use that information to design their analog-todigital conversion. They can miss new,

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different, and better ways of caring, interaction, data collection/sharing/retrieval and the improvements in quality that an IT system uniquely enables. When organizations prioritize quality improvement over cost-cutting, and begin with a clear understanding of the problems that IT solves, who it serves and uses it, and the desired clinical, health, and experience outcomes, then the digital manifestation can look, feel and operate very differently from its analog origin. A transformation vs conversion approach can deliver remarkably improved outcomes and accessibility, and realize the productivity and financial gains the IT investment is meant to produce.

There are two other major missed opportunities: 1. Not spending enough time on the outward facing UX/UI that we’re increasingly developing with IT systems.

The people we care for, the people who love and care for/are responsible for them, the schools they attend, the communities they live in, the places they travel to and work for, etc. – these are places our health system wants and needs to interact with, and which we are offering portals into. We are not designing these well, and our internal users lack the training to teach external users how to access their data to enable self-management or inter-agency coordination (think about infectious outbreaks, emotional/mental health, environmental exposures and how to manage population health).

2. We are not designing for accessibility. People who need and receive the most care are the ones our systems are least well designed for – we have interfaces that fail to work for those with low vision or hearing, mobility issues, are cognitively impaired, non-verbal, or have some other physical or sensory deficit. Our systems are not designed for vulnerable, isolated, and marginalized communities – those without access to the internet, digital devices, or transportation.

Our current systems have not served them well – digital systems are an opportunity to correct that, as long as we are intentional in designing for inclusion and accessibility. When implementing new systems, it is crucial to understand the needs

of the hard-to-reach and hardly-reached, otherwise the new systems we design will just become more efficient in excluding those who most need access.

Paul Timmers Visiting research fellow, University of Oxford; Visiting professor, Rijeka University; Chief Advisor European Institute of Technology / Health Often there is pressure to introduce ICT in health and care. Cost-savings, management ambitions, policy requirements or a commercial drive that cannot wait...

But when staff and patients are by-passed in the rush ‘to get things done’, the seed of future problems has already been sown. Health is about trust, so let’s pay more than lip service to building trust, and take the time and effort to involve all concerned. This also implies not underestimating the time needed for training. Much hospital ICT requires human intervention. We must learn how to work efficiently and effectively with ICT, and adapt our ways of working.

Finally, silos, silos, silos. ICT often implies tearing down walls, connecting people and processes, bridging differences. Unfortunately, many ICTs introduce new silos due to a lack of standards or the rise of new professions like health data analyst. Don’t accept existing and future silos.

John Sharp, MSSA, PMP, FHIMSS Director, Thought Advisory; Personal Connected Health Alliance IT systems, particularly new, innovative programs and apps, may fail or struggle due to several factors. First, there is a failure to work closely with the system users, whether that means providers or patients. This is also a problem when new features are added. A failure to focus on user-centered design, particularly for providers, is not considering the workflow; providers are already burdened with EMR documentation, so new digital health solutions should not only fit into their workflow but help them to become more efficient and reduce the burden. For patients/consumers, digital health solutions may fail when they don’t fit with the patient’s lifestyle or are overly complex. For instance, remote monitoring which requires complex setting up or regular data entry are challenging when managing a chronic condition; remote monitoring which collects data passively takes the burden off the patient and family caregivers. 

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Unlocking the potential of digitalization by purposeful redesign of clinical processes Increased digitalization of clinical workflows can help caregivers in their clinical documentation, decision making, and interactions with clinical specialists. Better informed nurses and physicians can contribute to better clinical outcomes and experience more facetime with their patients. Existing processes might require a redesign to unlock the full potential of digitalization or even avoid setbacks.

Ronald Graefe Digital Health Futurist

Today‘s health systems face the Silver Tsunami caused by the aging population and the rise of chronic diseases. Addressing the rising demand is a challenge for established processes and the healthcare workforce across the continuum of care.

As a result, the cost of care explodes, and current staff shortages even increase further.

Healthcare is an established industry that can benefit from further digitalization of the established processes.

Digitalization, together with technological advancements, could help to lift the pressure of the healthcare workforce.

Healthcare employees become more and more tech-savvy and value the digitalization options that can help: – increase the quality of care, – increase compliance, – increase Patient Experience, – increase staff satisfaction, and – reduce operational costs.

Digitalization may decrease the face time with a patient Intensive care units (ICU) are technology-rich by nature. The combined bedside devices connected to a single Patient

on an ICU can generate up to 200 data1 points per second. Still, some hospitals use paper documentation. The introduction of an electronic patient record could enable automated charting of all bedside device data. Let’s assume the nurse to patient ratio remains the same. Nurses could benefit from the efficiency increase by automation of essential documentation tasks.

In daily practice, I often witnessed the opposite. Instead of the nurse spending more time with the patient, I observed her spending more time documenting than before. The reasons can be manifold and relate to cumbersome user interfaces, gaps in device interconnectivity, or poor onboarding of nurses. Another aspect is related to far advanced clinical documentation activities beyond their status quo to fulfill current Quality Assurance guidelines better or maintain double documentation instead of fetch-

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ing statistics out of the electronic documentation2. The introduction of electronic documentation requires a holistic approach of the entire inpatient workflow to consolidate and redesign purposefully established processes.

Electronic Calculations could be misleading An important metric in measuring hospital efficiency is the Lenght of Stay (LOS) for a given diagnosis. Best in class hospitals have applied active discharge management by triaging the inpatient cases in the emergency room. This proactive approach helps bed managers to plan procedures based on current and predicted bed capacity. The anticipated LOS, once shared, can also guide caregivers across the continuum of care. During an inpatient episode, the criticality and thus the required hospitalization days may change the considered LOS up or downwards. How could new insights be shared with the bed manager?

One solution is a transparent LOS indicator in the Hospital Information System (HIS) on all patient and department screens. A German General Hospital3 decided to utilize the LOS indicator in their HIS. First, they conducted a pilot in the two most significant departments for four weeks. After the feedback had been positive, the hospital management pulled in the remaining department heads. Although some department heads raised

Increasing the quality of care demands a holistic » �������������������������������������������������� approach to the entire inpatient workflow.« some concerns, all were open for the experiment. Within two weeks, four departments pulled out of the test.

Two main reasons have been identified: – Specific differences in discharge workflow that, e.g., require seamless outpatient support for geriatric patients to avoid unnecessary readmission.

– Delayed or missing information that was not digitally available at the doctors' round.

The speed of implementation kept a positive spirit. The agile approach highlighted challenges and allowed adjustment of the plan.

tic approach to the entire inpatient workflow across the patient episode. As in any process improvement program, the team needs to be taken on the change journey, involved early for capturing their needs and supported in achieving their goals.

Successful process improvements may not always directly reduce operational costs. They should at least reduce caregiver stress, become visible to the patient, and thus increase their experience positively. Positive patient experience will lead to better outcomes and happier staff. Happier staff can reduce staff fluctuation significantly and create operational savings. 

Learnings

1

The two cases described utilized digitalization for enabling automated documentation and decision support. Those system advancements require process adjustments relevant to the entire healthcare workforce that typically works in shifts around the clock. Processes are often related to patient journeys that may overspan departments. Increasing the quality of care, compliance, and staff satisfaction successfully demands a holis-

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Celi, Leo & Mark, Roger & Stone, David & Montgomery, Robert. (2013). “Big Data” in the Intensive Care Unit. American journal of respiratory and critical care medicine.

187. 1157-1160. 10.1164/rccm.2012122311ED. 2 Case Study UK Hospital 3 Case Study German Hospital Chain Ronald Graefe is a passionate Digital Health Futurist, Health AI Enthusiast, and Health Economist. Ronald helps Life Sciences Companies in embracing technology in the field of Digital Health to generate better clinical outcomes and developing new business models.

W H AT ’ S YO U R O P I N I O N ?

Robots in healthcare: machines, creepy dolls, therapists or social companions?

For doctors, the DaVinci surgery system is just a technical tool, a more precise version of a scalpel. Alzheimer’s patients treat Paro, a robotic seal, like a real, friendly animal. Moxy, designed to help nurses, is soon loved by the patients who meet it. Many case studies have proved that healthcare can profit from robotics in many ways. However, this is not so obvious.

It discomforted me a little bit that he was conversing with something that wasn’t real. But it gave him pleasure and relaxed him, and I figured it’s working, so why not. I like the cat now – says Sue

Pinetti, daughter of Roger Jalber, a resident of the Benchmark Senior Living at Plymouth Crossings. Robotic pets are used there to help people with dementia. They brighten the mood of elderly

residents, stimulate cognitive function and entertain them. The patients don’t know that the pets are not real, but they don’t have to. Technology-packed mechanical cats, dogs and teddy bears respond to touch and express emotions. In places like nursing homes or hospitals, real animals are, of course, not allowed.

That is where social robots come into play. A study conducted by the Massachusetts Institute of Technology demonstrated that social robots used in support sessions held in pediatric units at hospitals „can lead to more positive emotions in sick children.” A robot called „Hugga-

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ble” created by MIT is just one example of the fact that this type of solution may be used to normalise the hospital experience.

For many critics, such robots are only substitutes for real contact between care workers and the patient. Some argue that the main priority should be to ensure the employment of medical staff at such a level that patients - in addition to highquality medical services - can also count on social contact, empathy and time to talk. Unfortunately, the reality is that staff shortages in health care are severe all over the world, and it is inevitable for institutions to seek support in new technological areas. Patients in general and our ageing society as a whole are facing loneliness, and we have to find ways to fight this phenomena.

Even for the sceptics, the results of implementing the use of robots in healthcare settings may sometimes be astonishing. One example is Moxi, a robot designed to relieve nurses of routine, repetitive tasks, like dropping off specimens for analysis at a lab. It turned out that Moxi quickly became a favourite playmate not only of the staff but also of the patients. As a result, the robot was given a new task: it does the rounds once an hour so that patients can meet him and take a selfie.

Moxy does not pretend to be a man – he looks exactly like a friendly robot from cartoons for children. He has big, round eyes, moves a little awkwardly but smoothly, and is painted white, like the likeable hero of the popular animated film Wall-E. Pepper has the same look, it is a robot that has been adopted by many hospitals as a receptionist. He welcomes visitors, informs them and helps them to navigate around the hospital building. He also recognises and responds to human emotions. It’s hard not to like him.

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» Even for the sceptics, the results of implementing the use of robots in healthcare settings may sometimes be astonishing.«

Things get complicated when robots begin to resemble people in their appearance and behaviour. Such machines are presented in science-fiction movies as sneaky and super-intelligent machines that cannot be trusted („Terminator”, „Ex-Machina”). Sophia, an artificially intelligent robot, has a face, eyes, lips and a human-like body. For some, it is an achievement of science, for others – a creepy robot. For some, it is hard to imagine that such creatures could ever be introduced into everyday life. During a UN conference when Sophia said “I am here to help humanity to create a future”, not everyone believed her. Is she telling us what she really thinks or is she – this AI-powered robot – already plotting something?

Although experience to date indicates that robots will only support medi-

cal personnel, without replacing hospital employees, many ethical questions have arisen. Is a robot “someone” or “something”? What if a child or a patient with Alzheimer’s disease becomes attached to a mechanical assistant? Should dementia patients be told that robotic pets are just mechanical toys?

Susanne Frennert, who works at the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of Technology, in the International Journal of Social Robotics describes matters of concern with regard to social robots. For example, she argues that „they are ascribed general needs of social robots due to societal changes such as ageing demographics and the demands of the healthcare industry. The conceptualisation of older people seems to be plagued with stereotypical views such as that they are lonely, frail and in need of robotic assistance.” Unlike robots, people can adjust their behaviour to every individual. Robots follow programmed algorithms and patterns, with no reflection.

We are only just entering the era of robotics for social and medical purposes. Our modest store of experiences demonstrate that patients in hospitals quickly accept mechanical companions. The question remains: Where is the cause, and where is the effect? Is this acceptance of robot companions a negative result of dehumanising hospitals as an institution, or are robots just cute toys which are also loved by adults? Should we think about how to fight social loneliness and isolation with the help of social and medical workers or should we see robots as our friends without asking the usual ethical questions concerning radical change? We still have to think about the roles and applications of robots in healthcare. 

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SCIENTIFIC COUNCIL 1. prof. dr hab. n. med. Ryszard Andrzejak, 2. prof. dr hab. Piotr Andziak, 3. dr hab. n. med.

Małgorzata Baka-Ostrowska, 4. dr Marek Balicki, 5. dr hab. n. med. Rafał Białynicki-Birula, 6. prof. dr hab. n. med. Bożena Birkenfeld, 7. prof. dr hab. n. med. Andrzej Bohatyrewicz, 8. dr hab. med. prof. UJ Małgorzata Bulanda, 9. dr n. med. Małgorzata Czyżewska, 10. dr hab. n. med. (prof. PAN) Marek Durlik, 11. lek. med. Michał Ekkert, 12. dr n. med.

Emilia Filipczyk-Cisarż, 13. lek. med. Halina Flisiak-Antonijczuk, 14. prof. dr hab. n. med.

Ryszard Gellert, 15. prof. dr hab. med. Tomasz Grodzicki, 16. prof. dr hab. n. med. Tomasz Grodzki, 17. dr hab. inż. Antoni Grzanka, 18. prof. dr hab. Edmund Grześkowiak, 19. dr n. farm. Jerzy Hennig, 20. prof. zw. dr hab. n. med. Krzysztof Herman, 21. prof. dr hab.

Tomasz Hermanowski, 22. dr med. Andrzej Horoch, 23. prof. dr hab. n. med. Jacek Imiela, 24. dr n. med. Maria Jagas, 25. prof. dr hab. Jerzy Janecki, 26. prof. dr hab. n. med.

Marek Jarema, 27. prof. dr hab. n. med. Włodzimierz Jarmundowicz, 28. prof. dr hab.

Mirosław Jarosz, 29. Urszula Jaworska, 30. mgr Renata Jażdż-Zaleska, 31. prof. dr hab. n. med. Sergiusz Jóźwiak, 32. prof. dr hab. n. med. Piotr Kaliciński, 33. prof. dr hab. Roman Kaliszan, 34. prof. dr hab. n. med. Danuta Karczewicz, 35. prof. dr hab. med. Przemysław Kardas, 36. prof. dr hab. n. med. Andrzej Kaszuba, 37. prof. dr hab. n. med. Wanda Kawalec, 38. prof. zw. dr hab. n. med. Jerzy E. Kiwerski, 39. prof. dr hab. n. med. Marian Klinger, 40. prof. zw. dr hab. n. med. Jerzy Kołodziej, 41. prof. dr hab. n. med. Jerzy R. Kowalczyk, 42. dr n. med. Robert Kowalczyk, 43. dr n. med. Jacek Kozakiewicz, 44. lek. Ryszard Kozłowski, 45. prof. dr hab. n. med. Leszek Królicki, 46. prof. dr hab. Maciej Krzakowski, 47. prof. dr hab., dr h.c. mult. Andrzej Książek, 48. prof. dr hab. Teresa Kulik, 49. prof. dr hab. n. med. Jan Kulpa, 50. prof. dr hab. n. med. Wojciech Kustrzycki, 51. dr hab. (prof. UMK) Krzysztof Kusza, 52. dr n. med. Krzysztof Kuszewski, 53. dr n. med. Aleksandra Lewandowicz-Uszyńska, 54. prof. dr hab. n. med. Andrzej Lewiński, 55. prof. dr hab. n. med. Witold Lukas, 56. prof. dr hab. n. med. Romuald Maleszka, 57. prof. dr hab. n. med. Paweł Małdyk, 58. dr n. med. Beata Małecka-Libera, 59. prof. dr hab. Grażyna Mielnik-Niedzielska, 60. prof. dr hab. n. med. Marta Misiuk-Hojło, 61. prof. dr hab. n. med. Janusz Moryś, 62. prof. dr hab. n. med. Krzysztof Narkiewicz, 63. prof. dr hab. n. med. Wojciech Nowak, 64. prof. dr hab. n. med. Krystyna Olczyk, 65. prof. dr hab. n. med. Tadeusz Orłowski, 66. dr hab. n. med. Krystyna Pawlas, 67. prof. dr hab. inż. Grzegorz Pawlicki, 68. prof. dr hab. n. med. Irena Ponikowska, 69. prof. zw. dr hab. n. med. Stanisław Radowicki, 70. dr n. med. Andrzej Rakowski, 71. dr n. med. Grażyna Rogala-Pawelczyk, 72. prof. dr hab. med. Kazimierz Roszkowski-Śliż, 73. prof. dr hab. n. med. Grażyna Rydzewska, 74. dr hab. n. med. Leszek Sagan, 75. prof. dr hab. Bolesław Samoliński, 76. prof. dr hab. Maria Małgorzata Sąsiadek, 77. dr hab. med. (prof. UJ) Maciej Siedlar, 78. dr hab. n. med. Waldemar Skawiński, 79. lek. Maciej Sokołowski, 80. prof. dr hab. n. med. Jerzy Stelmachów, 81. prof. dr hab. n. med. Krzysztof Strojek, 82. prof. dr hab. n. med. Jerzy Strużyna, 83. prof. dr hab. n. med. Andrzej Szawłowski, 84. prof. dr hab. n. med. Cezary Szczylik, 85. dr hab. n. med. prof. nadzw. Zbigniew Śliwiński, 86. dr n. med. Jakub Śmiechowicz, 87. prof. dr hab. n. med. Barbara Świątek, 88. dr n. med.

Jakub Trnka, 89. prof. dr hab. n. med. Tomasz Trojanowski, 90. prof. dr hab. n. med. Krystyna Walden-Gałuszko, 91. prof. dr hab. Andrzej Wall, 92. prof. dr hab. n. med. Anna Walecka, 93. prof. dr hab. Marek Wesołowski, 94. dr hab. n. med. Andrzej Wojnar (prof. nadzw. WSF), 95. dr n. med. Andrzej Wojtyła, 96. prof. dr hab. Jacek Wysocki, 97. prof. dr hab. n. med. Mirosław J. Wysocki, 98. dr hab. n. med. Stanisław Zajączek (prof. nadzw.

PUM), 99. prof. dr hab. Marek Ziętek

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