FOREWORD FOREWORD The soundtrack to the last five years of healthcare innovation has adopted a familiar, repetitive rhythm: “the future is bright”; “healthcare is evolving”; “Medical technology industry and AI are vitally important.” As healthcare systems around the world try to treat more patients while dealing with limitations in budgets, the promise of AI to cost-effectively satisfy the demand for timely, accessible, quality healthcare is exciting. Medtech and AI may change the landscape for patients, healthcare professionals, hospitals and the NHS. They have the potential to support earlier detection and prevention of some of the most common and challenging diseases, improving outcomes. They could offer quicker, more accurate diagnoses and guide subsequent treatment decisions, so they are optimised for each individual. This contributes to the promise of Precision health. And they could improve efficiencies and operational workflows within hospitals, breaking down walls by creating one truly integrated system Many would agree that the why of AI is potentially compelling, but that doesn’t answer one crucial question: how can it be delivered? That is why GE Healthcare has become a founding partner in NCIMI, an academic, clinical and industry collaboration. We understand the frustration of being sold a Big Future without the practical tools needed to deliver it. By taking ideas and algorithms that have been developed in a research context and moving them into clinical practice, the consortium aims to deliver manageable and tangible healthcare transformation in diagnostics, therapeutics and monitoring. After all, reason and theory form the foundation of every project, but robust methods and real-world application will deliver the dream – of clinicians supported by efficient and accurate AI-powered software to deliver individualized healthcare
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The GE Healthcare - NCIMI partnership Who are NCIMI? NCIMI is a consortium established by the University of Oxford that Includes a network of NHS trusts (groups of hospitals) covering the full spectrum of NHS delivery; commercial partner companies ranging from small start-ups to industry leaders like GE Healthcare; patient groups and charities. By bringing together a wide range of expertise into one sustainable ecosystem, NCIMI aims to address unmet needs in AI. The consortium provides the infrastructure needed to give industry access to patient data sets that inform multi-modality clinical imaging projects and software development. This enables emerging projects to be disseminated and up-scaled across participating NHS trusts. This is the first time such a multi-stakeholder approach has been adopted for clinical imaging and AI. Integrating different viewpoints to generate patient trust and added value from development to implementation and NHS adoption will give the consortium the power and insight to make AI in healthcare a reality.
NCIMI: an academic, clinical and commercial collaboration 15 NHS Trusts Oxford University Hospitals NHS Trust, Leeds Teaching Hospitals NHS Trust, King’s Fertility, Newcastle, Royal Brompton Hospital, Royal Papworth Hospital, Royal United Hospital (Bath), Royal Cornwall Hospital, Liverpool (Aintree NHS Trust), NHS Greater Glasgow & Clyde, University Hospitals Bristol NHS Foundation Trust, , Royal Berkshire NHS Foundation Trust, Milton Keynes NHS Trust, Buckinghamshire Healthcare NHS Trust and Frimley Health Foundation Trust 10 Industry Partners GE Healthcare, Plexalis, Caristo, Ultromics, Brainomix, Mirada, Optellum, Perspectum Diagnostics, Behavioural Architects and Alliance Medical
What is GE Healthcare’s role in the consortium? As the biggest industry partner in the consortium, GE Healthcare is well placed to take ideas from brain to bedside by sharing our talent, software, and equipment. When talking about AI, data management is the cornerstone of any initiative. GE Healthcare’s Centricity Clinical Archive is a key element of the partnership. It is the foundational technology for data collection, storage and management of protected data from the participating NHS trusts and will be under the governance of the Big Data Institute at the University of Oxford. This unique Vendor Neutral Archive (VNA) acts as a real data hub, and can transform siloed data from many departments and facilities into powerful, information sharing engines by housing medical images and files of clinical relevance from different sources, unifying specialties for a complete representation of a patient’s journey, and making data available to any system in a comprehensive manner. This will enable consortium innovators to obtain and manage images and documents from hospitals across the network, supporting software development and informing critical treatment decisions, while saving time and lowering costs. Privacy and security are also key features of the Centricity Clinical Archive, only allowing properly pseudo-anonymised and consented data to flow into the consortium’s data store. It forms an essential foundation for many of the ongoing projects with NCIMI and has already been deployed in hundreds of healthcare facilities around the world.
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But our partnership extends far beyond software. GE Healthcare is dedicating time and resource to developing new and evolving algorithms, image analysis, and training tools that deliver novel clinical science support and applications, particularly in the areas of Positron Emission Tomography/Computerised Tomography (PET/CT), Magnetic Resonance Imaging (MRI) and X-Ray. We are also able to provide market access for distribution of any eventual NCIMI innovations into the market, helping start-ups and small- to medium-sized enterprises (SMEs) scale their own innovations and take them to the global market. By combining our innovation experience with NCIMI’s academic, clinical, patient and ethical expertise, we hope to develop software across a wide range of clinical applications, with a high level of quality, usability and functionality that has been validated across a variety of robust data sets.
What makes our partnership successful? The partnership between GE Healthcare and NCIMI has been designed to deliver innovations for use in the real world. We’re developing algorithms to serve care teams and patients across care pathways, including oncology, neurology, cardiology, as well as algorithms used in acute care such as critical care suite for chest X-ray. Collaborations like this are essential because no single organisation can overcome the practical challenges of delivering healthcare transformation alone. AI solutions can only thrive via an ecosystem where technology companies have access to clinical data, therefore the old closed innovation model that worked for the development of a medical device, for example, is no longer applicable. AI solutions require clinical data from day one. Furthermore, it doesn’t matter how good the idea is, if a system is too complex or only works in scientific or academic conditions, it won’t be adopted. The process from ideation to validation and delivery must be seamless, with a strong framework that allows for smooth collaboration between stakeholders. That’s why NCIMI selected GE Healthcare as one of their industry partners of choice. Having established a strong relationship over recent years through academic collaborations, the foundations for a partnership based on trust, mutual respect and commitment were already in place.
“GE Healthcare is enthused, proactive, adaptable, pragmatic” Claire Bloomfield. Chief Executive Officer, NCIMI.
Trust NCIMI have said they’re confident GE Healthcare can deliver – our work has already been shown to be insightful, intuitive and easy to use. They know they can rely on us for care delivery, research and commitment to allocating the resources needed. And they know that, despite our size, we act nimbly and quickly; an essential quality to meet the competitive demands of the grant application and funding process.
“GE Healthcare have been impressively responsive. It’s difficult for large organisations to move at speed, yet they are quick to react to emerging challenges and opportunities – that’s vital for the success of NCIMI!” Dr Fergus Gleeson. Professor of Radiology, Oxford University. Clinical Director, NCIMI.
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Mutual respect GE Healthcare and NCIMI operate through clarity and honesty to ensure we’ve aligned our missions to support earlier detection and prevention as well as more accurate diagnosis and subsequent treatment. We each bring something different to the partnership. The academic and clinical partners in NCIMI have a great research track record but want to increase the commercial focus. Researchers are particularly focused on individual products and diseases. Conversely, GE Healthcare bring the ‘big picture’ perspective of a larger industry organisation, always looking for synergies and international applications. Together, we can leverage both mindsets to make healthcare transformation a reality.
Commitment Instead of working together on a project-by-project basis, GE Healthcare is investing in a longstanding relationship as part of NCIMI. This has allowed us to build an exciting pipeline we can drive forward together. “There is a real sense of being in this together… It really is a partnership model of working and not many big companies can work like that.” Claire Bloomfield. Chief Executive Officer, NCIMI.
Ongoing GE Healthcare-NCIMI projects The proof of the partnership will be in the outcomes we deliver. GE Healthcare and NCIMI are currently pursuing the development of AI technologies to deploy in the NHS, utilising pre-existing work from both organisations to optimise their use and broaden their application so that the benefits can be realised across the entire healthcare ecosystem, from individual patients and clinicians to hospitals and the NHS. It’s not simply about identifying the need and creating an algorithm in isolation. Thanks to a wide network of NHS hospitals involved with NCIMI, all our algorithms will be tested and validated in a range of real-world settings to ensure they’re robust enough to remain accurate amid differing institutions and working practices.
GE Healthcare exemplar projects at NCIMI • PET/CT reconstruction (see case study on page 5) • Why: PET scans are used to stage oncology patients and assess cancers. However, demand is high and the rapid and accurate quantification of lesions can be difficult • How: Develop an algorithm that optimises signal recovery, thus providing superior image quality, as well as predicting how long you need to scan a patient for, and what the image will be like • Critical Care Suite (see case study on page 6) • Why: Chest x-rays are the highest volume exam and commonly acquired frequently on the sickest patients. A critical finding requires immediate attention from the physician to stabilize the patient – and long radiologist turnaround times, delaying diagnosis, may put the patient in further danger. • How: Develop and test GE Healthcare’s algorithms that accurately locate and identify critical findings, such as a pneumothorax or collapsed lung, and is embedded within the X-ray device to flag cases within seconds of acquisition as needing immediate radiologist review. • PET/CT AI-assisted reader • Why: Radiologists in Oncology want to be able to read PET/CT images more quickly and confidently • How: Develop an algorithm detects and classifies cancer on patients’ PET/CT images August 2019
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Case study 1: Improving Oncology PET/CT clinical workflow and productivity with AI-enabled reconstruction and processing methods What? A software tool that takes the GE Healthcare software, Q-Clear, to the next level. The aim is not only to increase the quality and accuracy of imagery and diagnoses; but also to predict the amount of time and radiation dose required to get the optimal image.
Why? PET scans are used to stage oncology patients and assess cancers. It is a huge growth area with a focus on Precision Health, aiming to provide a tailored scan for each individual, rather than a generic approach for everyone. Two key challenges for clinicians are: 1. Non-specific grainy marks around the scanned image called ‘noise’. These reduce the image quality, making the scan harder to read and therefore making the precise and accurate quantification of lesions difficult, particularly the small ones 2. Scan duration and radiation usage are not optimised for each patient, so they take more time and expose patients to more radiation than may be required By developing an algorithm that predicts how long you need to scan a patient for, and what the image will be like, we could help to: • Obtain better images while scanning for less time, thus increasing the speed whilst maintaining accuracy of diagnoses • Reduce radiation dose, reassuring cancer patients that their exposure is as low as possible • Personalise the scan to each patient, taking into account variables such as body size • Increase the hospital’s capacity by enabling the potential for more patient scans per day In the future, these benefits could also apply to other disciplines, such as neuroimaging and cardiology.
How? In this multi-year partnership GE Healthcare and NCIMI: are committing extensive experience and resources to this project • Momentum: GE Healthcare have already developed a powerful algorithm called Q-Clear that ‘denoises’ the image its use in hospitals is growing internationally, resulting in ever-more data being gathered. Thus setting the stage for new algorithms development • Presence: As one of Europe’s primary PET providers , GE Healthcare already have a strong PET footprint, which would allow for wide distribution if the algorithm is approved. The project also includes hospitals that have GE Healthcare PET scanners • Investment: GE Healthcare is providing the scientific talent and modality access to accelerate the mission as well as covering 50% of the cost of the first 3 years. Both parties will continue to commit resources over time • Range: The consortium has links with a number of scanning departments within the partner Trusts that can contribute the raw data needed to make the algorithm robust
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Case study 2: Critical care - chest X-ray What? A suite of algorithms that rapidly identify critical findings, such as a pneumothorax, or collapsed lung, on chest X-rays within seconds of image acquisition. Additionally, AI algorithms to detect quality errors are also embedded with the X-ray device to give real time feedback to the technologist of potential images that may need to be repeated or reprocessed.
Why? AI algorithms in chest X-Rays are an area of significant interest due to high demand and a wide variation in their quality. In Critical Care, it can then take between 2–8 hours before a Radiologist is available to read a chest Xray. In addition, X-Rays from this department are often difficult to read due to the level of illness patients in that department experience. The ability to triage chest X-rays with red and green lights to help radiologists prioritise cases would be hugely beneficial to hospitals. However, the stakes are high. The algorithm needs to be extremely sensitive, otherwise abnormal X-Rays may be missed, which could delay critical treatment. This will be the first of a suite of Critical Care AI algorithms focused on chest X-Rays. In addition, it is the first imaging device to deploy a clinical detection AI algorithm within it.
How? GE Healthcare has developed an algorithm called the X-ray Critical Care Suite, which detects pneumothorax on chest X-Rays, and quality issues such as rotated images, incorrect protocol used, and patient mispositioning. Other algorithms are also in development. Together, they will form the foundations of the software. This is an extremely ambitious project to take on due to the quality of X-rays in critical care, but GE Healthcare and NCIMI have ensured the necessary conditions are in place to create a reliable and robust suite of algorithms: • Diverse data input: With 11 million patients, we have a reasonable population with a good number in critical care, providing a broad, real-world sample • In-hospital expertise: A critical care doctor who works in an emergency department and in general medicine has already been identified to lead the testing. Additional resources and talent are constantly being committed to the project • Ongoing testing: The algorithms will be tested as they’re developed, allowing us to validate them quickly and maximise their accuracy
“This will be a good learning curve in terms of the difference between what is possible in an academic institution vs. clinical practice.” Dr Fergus Gleeson. Professor of Radiology, Oxford University. Clinical Director, NCIMI. Now in the advanced stages of development, we anticipate that this suite of algorithms will be deployed in NHS hospitals in the near future, demonstrating the value of GE Healthcare’s partnership with NCIMI.
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Ensuring robust data governance through every aspect of our work It’s not just project-by-project practicalities that need to be streamlined in order to successfully deliver innovation into healthcare. Broader challenges around data governance also need to be managed to realise the full potential of AI. Robust data governance is paramount to the successful adoption of AI. Questions such as ‘who owns my data?’, ‘what do they know?’, ‘who has access to it?’, ‘is it accurate?’ and ‘could it be misused?’ are currently at the forefront of the public and professional conscience. GE Healthcare and NCIMI are working to ensure robust data governance flows through every aspect of our work, so our data is both consistent and reliable. By establishing a clear framework around data quality, transparency/ownership and security.
“Our governance framework is essential to ensure everyone, from data controllers and custodians to clinicians and the public, knows who the data is going to and what it will be used for.” Professor Jim Davies. Executive Data Scientist, NCIMI
AI has the potential to benefit patients and hospitals. The consortium plays an active role in shaping the debate around how all stakeholders in the ecosystem can benefit through economic, clinical, societal and academic benefits. We work closely with patient organisations and charities to maintain clear and honest dialogue. We also work closely with key stakeholders within the NHS to contribute to and inform national policies regarding data ownership between private companies and NHS hospitals so that it doesn’t form a barrier to progress.
Investing in the future GE Healthcare and NCIMI’s commitment to the partnership Very little is certain in such a rapidly-evolving world. New opportunities present themselves; novel technologies are developed; and unique challenges arise. That’s why we’re investing in our relationship as an integral partner with NCIMI. Together, we are well positioned to help address the developing needs in healthcare imaging through innovation and AI integration. AI innovation will thrive as part of a wider ecosystem, utilising the skills and expertise of many stakeholders to ensure an idea can become a reality. So, no matter where the future takes us, we’ll have the academic, clinical and commercial insight needed to lead the way in healthcare transformation. “NCIMI has the potential to serve as an exemplar of how academics, SMEs, major industrial players and clinical service lines can collaborate to shape the future of intelligent healthcare.” John F Kalafut PhD. Director Digital Innovation and Solutions, GE Healthcare
Our partnership with NCIMI is real-world proof that we don’t just talk the talk in AI. At GE Healthcare, we are so much more than a provider of technologies - we help deliver manageable and tangible healthcare transformation in diagnostics, therapeutics and monitoring through flexible, committed partnerships. By sharing our robust methods and real-world applications with current and potential customers, we can develop new and exciting partnerships that aren’t just inspired by the why of AI integration, they’re powered by the how.
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Imagination at work Data subject to change. Š 2019 General Electric Company. GE, the GE Monogram, imagination at work, and LOGIQ are trademarks of General Electric Company. Reproduction in any form is forbidden without prior written permission from GE. Nothing in this material should be used to diagnose or treat any disease or condition. Readers must consult a healthcare professional. JB71063GBa
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