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Medical science has improved rapidly, raising life expectancy around the world, but as longevity increases, healthcare systems face growing demand for their services, rising costs and a workforce that is struggling to meet the needs of its patients. Demand is driven by a combination of unstoppable forces: population aging, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation being but a few.
Of these, the implications from an aging population stand out. By , one in four people in Europe and North America will be over the age of 65—this means the health systems will have to deal with more patients with complex needs. Managing such patients is expensive and requires systems to shift from an episodic care-based philosophy to one that is much more proactive and focused on long-term care management.
Healthcare spending is simply not keeping up. Without major structural and transformational change, healthcare systems will struggle to remain sustainable. Health systems also need a larger workforce, but although the global economy could create 40 million new health-sector jobs by , there is still a projected shortfall of 9. We need not only to attract, train and retain more healthcare professionals, but we also need to ensure their time is used where it adds most value—caring for patients.
Building on automation, artificial intelligence AI has the potential to revolutionize healthcare and help address some of the challenges set out above.
Our working definition of AI in healthcare in this work is deliberately broad; it includes a functional continuum from the application of rules-based systems through to cutting-edge methodologies that include classic machine learning, representation learning, and deep learning.
AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients and in so doing, raise staff morale and improve retention. It can even get life-saving treatments to market faster.
At the same time, questions have been raised about the impact AI could have on patients, practitioners, and health systems, and about its potential risks; there are ethical debates around how AI and the data that underpins it should be used. It aims to cast light on the priorities and trade-offs for different parts of the healthcare system in Europe and beyond.
Last, to highlight where AI is already having an impact in healthcare, the report also looks at detailed examples of existing AI solutions in six core areas where AI has a direct impact on the patient and three areas of the healthcare value chain that could benefit from further scaling of AI Exhibit 1.
The report does not attempt to cover all facets of this complex issue, in particular the ethics of AI or managing AI-related risks, but does reflect the efforts on this important topic led by EIT Health and other EU institutions.
Equally, while it acknowledges the potential disruptive impact of personalization on both healthcare delivery and healthcare innovation in the future e. Last, AI is in its infancy and its long-term implications are uncertain.
Future applications of AI in healthcare delivery, in the approach to innovation and in how each of us thinks about our health, may be transformative. We can imagine a future in which population-level data from wearables and implants change our understanding of human biology and of how medicines work, enabling personalized and real-time treatment for all. This report focuses on what is real today and what will enable innovation and adoption tomorrow, rather than exploring the long-term future of personalized medicine.
Faced with the uncertainty of the eventual scope of application of emerging technologies, some short-term opportunities are clear, as are steps that will enable health providers and systems to bring benefits from innovation in AI to the populations they serve more rapidly. What do we mean by AI in healthcare?
In this report we include applications that affect care delivery, including both how existing tasks are performed and how they are disrupted by changing healthcare needs or the processes required to address them.
We also include applications that enhance and improve healthcare delivery, from day-to-day operational improvement in healthcare organizations to population-health management and the world of healthcare innovation. As such, it illustrates a spectrum of AI solutions, where encoding clinical guidelines or existing clinical protocols through a rules-based system often provides a starting point, which then can be augmented by models that learn from data.
AI is now top-of-mind for healthcare decision makers, governments, investors and innovators, and the European Union itself. An increasing number of governments have set out aspirations for AI in healthcare, in countries as diverse as Finland, Germany, the United Kingdom, Israel, China, and the United States and many are investing heavily in AI-related research.
Geographically, the dynamics of AI growth are shifting. The United States still dominates the list of firms with highest VC funding in healthcare AI to date, and has the most completed AI-related healthcare research studies and trials.
Europe, meanwhile, benefits from the vast troves of health data collected in national health systems and has significant strengths in terms of the number of research studies, established clusters of innovation and pan-European collaborations, a pan-European approach to core aspects of AI e.
Yet, at the same time, valuable data sets are not linked, with critical data-governance, access, and security issues still needing to be clarified, delaying further adoption.
European investment and research in AI are strong when grouped together but fragmented at the country or regional level. A surprising 44 percent of the healthcare professionals we surveyed—and these were professionals chosen based on their engagement with healthcare innovation—had never been involved in the development or deployment of an AI solution in their organization. While there are widespread questions on what is real in AI in healthcare today, this report looked at 23 applications in use today and provides case studies of 14 applications already in use.
These illustrate the full range of areas where AI can have impact: from apps that help patients manage their care themselves, to online symptom checkers and e-triage AI tools, to virtual agents that can carry out tasks in hospitals, to a bionic pancreas to help patients with diabetes. The scale of many solutions remains small, but their increasing adoption at the health-system level indicates the pace of change is accelerating.
In most cases, the question is less whether AI can have impact, and more how to increase the potential for impact and, crucially, how to do so while improving the user experience and increasing user adoption. We are in the very early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalization. Nevertheless, interviewees and survey respondents conclude that over time we could expect to see three phases of scaling AI in healthcare, looking at solutions already available and the pipeline of ideas.
First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimizing healthcare operations and increasing adoption.
In this first phase, we would also include AI applications based on imaging, which are already in use in specialties such as radiology, pathology, and ophthalmology. In the second phase, we expect more AI solutions that support the shift from hospital-based to home-based care, such as remote monitoring, AI-powered alerting systems, or virtual assistants, as patients take increasing ownership of their care.
This phase could also include a broader use of NLP solutions in the hospital and home setting, and more use of AI in a broader number of specialties, such as oncology, cardiology, or neurology, where advances are already being made.
This will require AI to be embedded more extensively in clinical workflows, through the intensive engagement of professional bodies and providers. It will also require well designed and integrated solutions to use existing technologies effectively in new contexts.
This scaling up of AI deployment would be fuelled by a combination of technological advancements e. In the third phase, we would expect to see more AI solutions in clinical practice based on evidence from clinical trials, with increasing focus on improved and scaled clinical decision-support CDS tools in a sector that has learned lessons from earlier attempts to introduce such tools into clinical practice and has adapted its mind-set, culture and skills.
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Zaremba M. Agerberg M. Stockholm: Regeringskansliet; Debate article. Dagens Nyheter. Sample size in qualitative interview studies: guided by information power. Download references. The authors would like to thank all the participating physicians, registered nurses and assistant nurses who participated in the interviews.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. You can also search for this author in PubMed Google Scholar. All analysed the data. PN drafted the manuscript, but it was reviewed and critically revised for important intellectual content by all authors. All authors read and gave final approval of the version of the manuscript submitted for publication.
Correspondence to Per Nilsen. All the participants gave their written and oral consent to participate in the interviews. The study was performed according to World Medical Association Declaration of Helsinki ethical principles for medical research involving human subjects.
To maintain the principle of non-maleficence, the participants were guaranteed confidentiality, which was taken into account when reporting the findings through abstracted findings presented at the group level. In the interviews, the researchers were aware of power issues, in that an interview is not a conversation between two equal individuals. The interview time was taken into careful consideration. The participants were given opportunity to reflect on what they said in the interviews, and time was also available for the participants to ask questions.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Nilsen, P. Characteristics of successful changes in health care organizations: an interview study with physicians, registered nurses and assistant nurses.
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Search all BMC articles Search. Download PDF. Research article Open Access Published: 27 February Characteristics of successful changes in health care organizations: an interview study with physicians, registered nurses and assistant nurses Per Nilsen 1 , Ida Seing 2 , Carin Ericsson 1 , 3 , Sarah A. Abstract Background Health care organizations are constantly changing as a result of technological advancements, ageing populations, changing disease patterns, new discoveries for the treatment of diseases and political reforms and policy initiatives.
Methods The study was based on semi-structured interviews with 30 health care professionals: 11 physicians, 12 registered nurses and seven assistant nurses employed in the Swedish health care system. Results The analysis yielded three categories concerning characteristics of successful changes: having the opportunity to influence the change; being prepared for the change; valuing the change. Conclusions Organizational changes in health care are more likely to succeed when health care professionals have the opportunity to influence the change, feel prepared for the change and recognize the value of the change, including perceiving the benefit of the change for patients.
Background The only constant in health care organizations, as the saying goes, is change. Methods Study setting, design and participants Study data come from interviews with Swedish health care professionals physicians, registered nurses, assistant nurses. Table 1 Participant characteristics Full size table.
Having the opportunity to influence the change The health care professionals emphasized the importance of having the opportunity to influence organizational changes that are implemented.
Discussion Change is pervasive in modern health care. Conclusions In conclusion, organizational changes in health care are more likely to succeed when health care professionals have the opportunity to influence the change, feel prepared for the change and recognize the value of the change, including perceiving the benefit of the change for patients.
Availability of data and materials All interview data analysed during the current study are available from the corresponding author on reasonable request.
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Article Google Scholar Stensmyren H. Google Scholar Young GJ. Google Scholar Noordegraaf M. Article Google Scholar Wilensky H. Article Google Scholar Zaremba M. Google Scholar Halldin J. Acknowledgements The authors would like to thank all the participating physicians, registered nurses and assistant nurses who participated in the interviews.
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