The Impact of AI, ML, and Robots in European Healthcare Workforce

This article discusses the potential of artificial intelligence (AI), machine learning (ML), and the internet-of-things (IoT) in revolutionizing the European healthcare sector. The AI in Healthcare market is projected to grow substantially during the period from 2023 to 2028, prompted by increasing demand for improved healthcare services. The technology has the potential to enhance care outcomes, patient experience, and healthcare access while reducing workforce burnout. To harness AI's highest potential, European healthcare organizations and health systems must collaborate to introduce and scale AI in healthcare. They should assess their capabilities, develop regional or national AI strategies, set standards, redesign workforce planning and clinical education processes, provide incentives for collaboration, and address regulation and funding issues. By doing so, Europe can remain a leader in AI-driven healthcare, while improving access, affordability, quality, and safety for its citizens.

James Gillespie

Faculty, Saint Mary’s College

Brianna Geary

Junior Product Manager, Publicis Sapient

Artificial intelligence (AI), machine learning (ML), internet-of-things (IoT), and robots in healthcare refers to the use of AI technologies such as image analysis, natural language processing and predictive analysis to improve access, affordability, quality, and safety.

These initiatives will cause positive disruption of existing processes, as well as the creation of entirely new processes, products, and services for providers and patients. The private, public, and NGO sectors in Europe all have increased interest in this likely positive impact. Specifically, this is reflected in the increased private equity and venture capital funding flowing to AI-related healthcare technology startups.

The AI in Healthcare market is expected to grow more than fivefold between 2023 to 2028, with a compound annual growth rate (CAGR) of 47.6% during the forecast period. The growth is driven by the increasing demand for improvised healthcare services due to the current imbalance between healthcare workforce and patients. This market for services will grow at a higher CAGR during the forecast period as compared to the software segment. Deep learning in the machine learning segment is projected to hold the majority of the market share in 2028, and the market for patients is expected to grow at the highest CAGR during the forecast period.

AI in healthcare is currently in its early stages, but there are already a growing number of use cases throughout Europe. These include software applications that allow patients to self-manage care, online symptom checkers, virtual agents to accomplish tasks in hospitals, and predictive analytics for early diagnoses. The impact of AI on healthcare in Europe and globally will be significant, but its full potential remains to be explored.

AI can offer effective tools for automating chores and supporting and educating clinicians, epidemiologists, and policymakers on the most effective ways to promote health at the population and individual levels

Staffing Shortages

AI and robots can be used to address what is perhaps the most pressing long-term issue of European healthcare providers: Insufficient human capital. There are critical staffing shortages facing the European healthcare industry, especially nursing. The significant number of clinicians exiting the workforce due to retirement, burnout, and pandemicrelated stressors, combined with the increasing nurse turnover rates, indicate that there is a need for innovative solutions to address these challenges. These challenges will continue to increase as primary care physicians and nurses are expected to leave the industry in large numbers in the coming years.

On the non-clinical side, there are also hiring difficulties. Health IT hiring presents new challenges, including matching skillsets and cost, as competition in the market increases. The need for health IT professionals has grown with the move into digital health and the digitization of healthcare, leading to a high demand for talent. Hospital cyberattacks have also increased the demand for cybersecurity talent. Moreover, many healthcare organizations are struggling under a vast pile of unfilled job postings.

Help from AI and Robots

AI and autonomous robot technology can be used to help healthcare staffing, including assisting with efficiency, productivity, and patient satisfaction. The specific benefits of AI, IoT, ML, and robots in healthcare include:

• Substantially lessen time spent on the rote, administrative tasks that can consume almost ¾ of a typical healthcare worker’s bandwidth. By automating rote tasks, AI can free up time for physicians, nurses, and other healthcare workers to focus on patient care and other more complex and specialized tasks, helping to reduce burnout.

• Improve diagnosis and treatment by implementing clinical decision-making software that can analyze large amounts of data.

• Automate nurse staffing and scheduling, decreasing manual work for nurse managers and increasing retention.

• Speed up job recruitment by automating over 90% of the hiring process for nursing vacancies.

• Leverage internet-of-things enabled badges to help collect data enterprisewide, gain real-time insights on equipment utilization, clinical workflows, and patient/ staff interactions to reduce operational bottlenecks, increase efficiency, and improve patient experience.

• Run advanced workflow platforms to provide patients with proactive status updates, estimated wait times, and family text messaging. This proactive patient communications can provide peace of mind and keep patients and loved ones wellinformed throughout the care journey.

• Automated nurse call systems can reduce response times to patient needs, allowing healthcare staff to have more time to focus on patient care.

• Implement staff duress safety solutions that provide peace of mind to staff members, enabling them to request assistance to their exact location should it be needed. This reiterates that staff work in a safe environment where they are valued.

• Help implement automated asset tracking that allows clinical staff to locate within seconds and retrieve crucial equipment via IoT-enabled tags.

• Leverage real-time location technologies and digital wayfinding to improve the patient experience in 2023. Digital wayfinding can reduce stressors for patients and visitors by providing turn-by-turn directions to their destination. These solutions can also benefit healthcare facilities by reducing missed appointments and delayed patient care, making them a cost-effective and scalable option for improving the patient experience.

• Automate recruitment of unfilled or changed shifts to the right workforce based on skills competency, price point, and workload. It can direct prospective hires to new positions and interview times with only a few items of screening information. This helps to reduce administrative tasks and eases the burden on HR staff, allowing them to focus on more nuanced connections with applicants during the interview process.

Key Changes Needed

To encourage the introduction and scaling of AI in European health systems, there are several changes that need to happen. These include:

1. Working together to deliver quality

AI in healthcare: There needs to be more collaboration between healthcare professionals and AI developers to ensure that AI solutions are of high quality and fit seamlessly into the workflow of decision-makers. This means involving healthcare staff early in the design phase, focusing on user-centric design, emphasizing explainable, causal, and ethical AI, and building clinical evidence of quality and effectiveness.

2. Human-machine/robot collaboration: Institutions will have to develop teams with expertise in partnering with, procuring, and implementing AI products and services.

3. Overhauling education, learning, and skill-building: To increase digital literacy, as well as understanding the fundamentals of AI, ML, and genomics, healthcare systems should provide opportunities for continuous learning and technology training.

4.Improving data governance, interoperability, quality and security: This will require the private, public, and non-profit sectors to support efforts to generate, collect, manage, govern, and analyze large volumes of high quality, anonymized data.

5. Managing cultural and organizational transformation: Effective leadership is key to introducing AI in healthcare. If the potential for patients and providers is to be maximized, the challenge is as much managerial as scientific or technological.

6. Developing new talent and creating new roles/positions. To achieve the successful introduction and utilization of AI/ML, new mission critical roles will need to be created such as data engineers, data scientists, statisticians, and technologists.

7. Scaling implementation and innovation: When it comes to the application of AI/ML to healthcare in Europe, scale does matter. This applies to biomedical research, digital health, translational research, and other fields. As part of this, large entities can collaborate on creating centers of excellence that facilitate regional and public-private partnerships to facilitate scaling AI in European healthcare.

8. Adoptions of new regulations, public policies and risk management: The European Health Organisation, the European Medicines Agency, the UK’s National Health Service, and other national and international regulatory agencies will need to clarify access to and utilization of data generated by AI, including issues related to ownership, privacy, and security.

9. Providing capital: To achieve scale, there will need to be utilization of both creative and traditional funding models to provide capital for startups and insure reimbursement of

AI applications. This includes encouraging the creation of centers of excellence and accelerators/incubators focused specifically on AI and healthcare.

10. Develop and promote responsible AI in healthcare, with a focus on ensuring that AI is implemented in a way that is ethical, transparent, and inclusive. This includes initiatives to support education and training programs for healthcare professionals, as well as efforts to establish guidelines and standards for the development and deployment of AI in healthcare. While some argue that AI can eliminate biases and improve efficiency, others have raised concerns about potential biases and discrimination. Since Ai can create substantial social stress, workers will need more insulation/ protection from algorithmic decision-making. The EU is working on legislation to regulate the use of AI in the workplace, but experts caution that workers need to be aware of the risks and advocate for their rights.

11. Address concerns about potential job displacement in the healthcare workforce. While it is unlikely that AI and ML will completely replace physicians and nurses, it is possible that certain tasks and responsibilities may be automated, leading to the need for job redeployment and retraining. For example, AI and ML may be used to perform repetitive and time-consuming tasks, such as analyzing medical images, enabling physicians and nurses to focus on more complex and specialized tasks, such as patient care and counseling. To address these concerns, there are ongoing efforts to develop strategies for job redeployment and retraining. This includes initiatives to support the development of new roles and responsibilities for healthcare workers, as well as education and training programs to help healthcare professionals acquire the skills needed to work alongside AI and ML technologies.

Conclusion

The use of artificial intelligence (AI) has the potential to transform healthcare delivery, including improvement in care outcomes, patient experience, and access to healthcare services. As demand for healthcare services continues to grow due to the aging European population, changing patient expectations, and lifestyle choices, AI can increase the effectiveness and efficiency of healthcare delivery for patients. Additionally, AI can help reduce burnout among health workers (e.g., physicians, nurses, physician assistants) by allowing them to spend more time working directly with patients. Ultimately, the goal is to ensure that the use of AI and ML in healthcare results in better patient outcomes and improved efficiency, while also supporting a sustainable and dynamic healthcare workforce.

In sum, healthcare organizations and health systems in Europe need to work together to introduce and scale AI in healthcare. Healthcare organizations need to assess their capabilities and define their ambition for AI, as health systems develop a regional or national AI strategy for healthcare, set standards, redesign workforce planning and clinical-education processes, provide incentives for collaboration, address regulation and funding issues, and ensure that funding and reimbursement mechanisms reflect the seriousness of innovation in healthcare. By working together, healthcare organizations and health systems can improve the quality and effectiveness of healthcare in Europe with the help of AI.

It is imperative for European healthcare organizations and health systems to assess their capabilities and willingness to commit resources for AI in healthcare. As part of this, there is certainly an opportunity to help grow an AI ecosystem via collaboration and joint solutions for patient populations. Health systems can develop a regional or national AI strategy for healthcare, set standards for digitization and data quality, redesign workforce planning and clinical-education processes, provide incentives for collaboration, and address regulation, liability, and funding issues. Overall, Europe is playing a growing role in the fast-moving market of AI in healthcare. In addition, its member countries have the potential to remain leaders in driving the future AI to benefit health systems, healthcare workers, caregivers, communities, and patients. AL, ML, and robots present a once-in-ageneration opportunity to improve access, affordability, quality, and safety in European healthcare.

James Gillespie is a faculty member in business administration and data analytics at Saint Mary’s College, Notre Dame Indiana. His education includes Northwestern University Kellogg School of Management, PhD, MS; Harvard University School of Law, JD; Princeton University School of Public Policy, MPA; and Massachusetts Institute of Technology, BS.