Accelerating Artificial Intelligence in health and care: results from a state of the nation survey - AUTUMN 2018 - KSS AHSN
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Accelerating Artificial Intelligence in health and care: results from a state of the nation survey AUTUMN 2018
Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 3 CONTENTS 4: Foreword 6: Introduction 8: Executive summary 10: What do we mean by AI in health and care? 14: Results of the national survey about AI technologies in health and care 22: Real world analysis: feasibility and implementation 38: Summary and next steps 44: Appendix 1: Case studies 48: Appendix 2: Further reading 52: Appendix 3: Glossary 59: Acknowledgements 62: About The AHSN Network AI Initiative
4 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 5 FOREWORD New technologies that harness envisions what can be achieved As it stands, the NHS is primed to We have already made some industry, academia, innovators By working together we will the power of data, like artificial when the vast potential of AI is use AI to improve its efficiency, important steps forward in and commissioners we will be able to explore all potential intelligence (AI), present huge unlocked. The report is based deliver better outcomes and this area. These include the continuously iterate the principles avenues of opportunity, and risk, opportunities to transform on a survey conducted by NHS prevent ill health. However, introduction of a new national and guidance contained in the and to make sure that none are healthcare, improve the quality England and the AHSN Network we must be realistic about the data opt-out and by the Bill, Code. Together, we will work to missed. We hope that, based on of people’s lives, and to make the AI Initiative, and it underlines the challenges. First and foremost, currently before Parliament, to ensure that the NHS gets the this reassurance and reflecting on job of working within the health potential for AI to contribute to the public must have confidence put the National Data Guardian maximum possible benefits from the information presented in this and care system more rewarding. improved care: 94% of the UK’s AI that AI (and the health data which on a statutory footing. At the end these partnerships, both for enlightening report, you are left We are determined to harness thought-leaders cite AI as being fuels the development of new of this report we point to, for the existing use cases of AI and those feeling as optimistic as we are this potential. extremely important or very algorithms) is being used safely, first time, a Code of Conduct for that appear in the future. And, of about the ability of technology, important for diagnostics; 89% legally and ethically, and that Digital Health Innovations and course, these developments all and AI in particular, to transform While these opportunities are support this view for operational the benefits of the partnerships Intelligence Algorithms, which is take place in the context of our health and care. available to every country, the and administrative goals; and, between AI companies and the designed to provide a national review of the current regulatory UK is well-placed to take a global 79% have this opinion in regard to NHS are being shared fairly. As set of ‘rules of engagement’ for framework and analysis of the advantage in this field. By virtue the benefits for health promotion a consequence, realising the any NHS organisation entering future needs of the health and of our universal single-payer and preventative health. The potential of AI in health and into a partnership with an AI care workforce. system, the complete longitudinal report cites many exciting care requires changes to data developer. datasets the NHS holds on every examples of pilot schemes and infrastructure, organisational A collaborative approach is citizen’s health and care, and more developed programmes structures, commercial Inevitably, there is still more to do important; no single partner in our world-leading AI and tech that are already delivering better arrangements, and models to seize the opportunities ahead. this endeavour has a monopoly industries, our goal should be to healthcare for British patients. of consent. By working collaboratively with on wisdom about what will work. bring the transformative power of AI to every corner of the NHS. For that reason, we are delighted to introduce this ‘state of the nation’ report, which looks to the future of health and care and Matt Hancock Lord O’Shaughnessy Secretary of State Parliamentary Under Secretary of State Department of Health and Social Care Department of Health and Social Care Photo attribution: Chris McAndrew [CC BY 3.0 (https:// creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
6 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 7 INTRODUCTION The report is split into In recent years there have been a number of policy reports published intelligent technology is being realised. This survey went live to four main sections: on the potential for artificial the nation at the start of 2018 and intelligence in healthcare. In this we have captured throughout this What do we mean by AI in health report we are not attempting to report the initial findings from the 1 and care? This section describes recreate that content but rather 131 responses. how AI is broadly defined and shows to address some of the concerns how, as AI evolves, it is becoming an In order to present a rich picture raised and outline some of the increasingly complex landscape. of the nation’s ecosystem and emerging policy in this arena within bring to life the complex and the UK. multifaceted aspects of the Results of the 2018 national survey We developed a survey in industry, the report also highlights collaboration with industry, a number of case studies that set 2 of AI technology in health and care, and the defining characteristics of academia and policy makers in an the scene for the work needed to the first 131 solutions that were attempt to capture the reality of scale up evidence-based solutions submitted. what technology is actually being that are safe, effective and offer developed within the UK health value going forward. and care sector, and to understand Real world analysis of feasibility what complexity of artificial 3 and implementation based on evidence from over 100 leaders and pioneers working in the field. This highlights the top barriers and enablers for catalysing an ethical, evidence-based market for AI- enabled solutions in health and care, and defines the issues that will set the agenda for the sector over the coming months and years. A summary of proposed next steps. 4 This includes the key themes for policy makers to develop a ‘Code of Conduct’ for an AI-enabled digital health and care market going forward, and the regulatory challenges that need to be addressed.
8 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 9 EXECUTIVE SUMMARY Over the last few years numerous The survey The results shows that AI has • ground AI solutions in real Showcase Code of conduct reports have been written about huge potential to transform ‘problems’ as expressed The survey was developed with whole the health and care by the users of the health To show what can be achieved as To address these challenges, a the opportunities and benefits the input of the AHSN Network system. Unlocking value in data/ system; AI is embraced across health and number of workstreams have artificial intelligence (AI) can AI Initiative Core Advisory Group analytics was the top category care, this report showcases some already been initiated across offer for healthcare. These have (individual members are listed • engage healthcare (75%) addressed by solutions of the emerging examples of the UK health and care sector. ranged from the Reform report1 in Acknowledgements) and sent professionals and create an submitted for the survey, more complex AI methodologies These include the Topol review illustrating the areas where AI out nationally via the AHSN ethical framework to enhance followed by condition recognition currently being used and which on workforce; a set of principles could help the NHS become Network and a number of AI and and preserve trust and (60%) and organisational hold significant potential to and guidelines summarised in more efficient to the report by innovation networks including transparency; processes (50%). deliver impact at scale in NHS, a Code of Conduct for digital Future Advocacy2 which reviews the AI community run by NHS • build capacity and capability; social care and, importantly, health innovations incorporating the ethical, social and political Horizons. The survey results are Whilst impressive, the survey • ensure the regulatory in preventative health. As the intelligent algorithms; and implications of AI in health and self-reported and were compiled shows that many solutions are framework is fit for purpose; complexity and capabilities of a number of initiatives to and analysed by a team at Kent primarily in their infancy and have these projects increase, it is vital understand and unlock the value medical research. • explore innovative new Surrey Sussex Academic Health a long way to go before the true that the policy and organisational of data to provide maximum funding and commercial While acknowledging these Science Network, supported by potential of AI for health and care contexts, processes and benefit to citizens and UK plc. models; and reports exist, we felt there was Health Education England Kent can be realised. As one survey regulation evolve to keep pace. a need to understand what is Surrey Sussex. This report is a respondent commented ‘AI is • focus on building a collaboration between the AHSN still evolving... it won’t solve all sound data infrastructure actually happening on the ground Network, NHS England, NHS the problems healthcare faces at and high quality data and what is being developed. We Digital and the Department for the moment’ and we must avoid sets, underpinned by also wanted to ask people within interoperability and sharing Health and Social Care. the trap of ‘overhyping potential, the health and care system who standards. Survey respondents included unrealistic claims and poorly use artificial intelligence (which CEOs, senior managers and others thought out products.’ is summarily defined as a series Furthermore, momentum is working across the AI ecosystem The survey revealed that realising of advanced technologies that starting to build for unlocking in England. They represented both the truly huge potential of AI open innovation through ‘Focus on building enable machines to effectively large organisations with 250 staff carry out complex tasks that to transform health and care establishing open data or more (32%) as well as micro services will require overcoming would require intelligence if ecosystems across health and organisations with less than 10 completed by a human) what stage of deployment their work staff (28%) across private, public and charitable sectors as well as several key barriers, and working together across the care. a sound data AI ecosystem to: has reached. academia. infrastructure and high quality data sets, underpinned by interoperability and Harwich, S. and Laycock, K. (2018). Thinking on its own: AI in the NHS. Reform. Available at: http://www.reform.uk/wp-content/ sharing standards’ 1 uploads/2018/01/AI-in-Healthcare-report_.pdf. 2 Fenech, Matthew, Strukelj, Nika and Olly Buston (2018). Future Advocacy and Wellcome Trust. ‘Ethical, social and political challenges of artificial intelligence in health’. Available at: https://wellcome.ac.uk/sites/default/files/ai-in-health-ethical-social-political-challenges.pdf.
10 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 11 WHAT DO There is no single, universally better understand clients’ Thanks to advances in AI and agreed definition of AI, nor indeed current and potential future Big Data research, narrow AI of ‘intelligence’. Broadly speaking, financial needs. technologies have the potential intelligence can be defined for wide application in health and WE MEAN BY as ‘problem-solving’, and ‘an • Ambient (Intelligence) - social care, bringing benefits to intelligent system’ as one which the application of several individuals, families, communities, takes the best possible action in technologies (including and society as a whole. While a given situation. Artificial or Augmented early examples from our survey Intelligence, but also sensor AI IN HEALTH illustrate that much of this work The ‘A’ of AI generally refers to networks, user interfaces, is at an early stage, current one of the following: home automation systems, technologies support a more • Artificial (Intelligence) – makes etc) to create proactive ‘smart’ general shift away from reactive environments. AND CARE? it possible for ‘machines’ to care models to models that are learn from new experiences, more personalised and proactive. AI is generally classified into the adjust outputs and perform following types: But this is not without its human-like tasks. It can be • Narrow AI typically focuses on challenges in health and social thought of as the simulation a narrow task, or works within a care and more widely – ensuring of human intelligence and narrow set of parameters such these technologies are fit for could include voice and visual as reading radiology scans, or purpose, ensuring outputs are recognition systems. transparent and explainable, and AI describes a set of advanced technologies that enable machines to • Augmented (Intelligence) optimising hospital workflows; ensuring people are trained in the carry out highly complex tasks effectively – tasks that would require - outputs that complement • Strong or general AI is a use of these new technologies. human intelligence, hypothetical concept which intelligence if a person were to perform them. emphasising AI’s can refer to an AI that can learn supplementary role. Examples to perform several different include tools that support types of task, or to a sentient radiologists in reviewing large machine with consciousness numbers of scans, or that and mind. support financial advisors to
12 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 13 Example of how simple components form modules which then form a complex AI application Complexity There is a significant amount of effort being devoted in the research space to map machine learning and AI, but it has been challenging to Clinical scale in AI categorise them according to their ‘intelligence’. decision Thus far, attempts at categorisation have been support Complex limited to looking at their generic ability to solve system application new problems, and at the speed with which they adapt to these problems. A more straightforward way of understanding AI is to classify AI systems by their complexity. A ‘Complexity Scale for AI’ can be seen in the boxed Inference User engine interface Modules section and compared with methods and case studies revealed in our survey. A glossary of AI-related terms used within the Complexity Scale for AI can be found at Appendix 3 on page 50. Components Algorithm derived of modules Link to EPR for Interface widgets Context-sensitive by ML from current case & resource files help historical dataset High complexity Middle complexity Low complexity AI applications AI modules or AI reasoning methods Devised by Jeremy Wyatt, Director and Professor of Digital Healthcare, Wessex Institute of Health Research, components Clinical Advisor on New Technologies, Royal College of Physicians, Fellow of American College of Medical Informatics & UK Faculty of Clinical Informatics) • Autonomous vehicle • Natural language • Deep learning module • Machine translation tool to SNOMED code • Ensemble methods (e.g. Random Forest Models) processing module • Care companion robot • Neural networks • Image processing • Chat bot • Object segmentation algorithm module • Surgical or pharmacy • Signal processing algorithm / filter The lowest level of the complexity The above provides an example state that they use ‘AI’. We would • Text to speech module robot • Generative adversarial networks scale comprises single specific of a complex AI application. also like to encourage those • Knowledge based or • Mammogram expert system module • Time series analysis reasoning methods (e.g. neural investing in these technologies interpretation system Algorithms in healthcare are not • Signal processing & • Graphical models networks, pattern recognition to understand what type of AI is • ECG interpreter a new phenomenon and have classification module • Decision trees, rule induction e.g. CART algorithms). When these being developed, how complex it • Diagnostic decision been deployed for decades. • Recommender module • Clustering algorithm reasoning methods are combined is, and indeed question what the support system What we have attempted to • Classification algorithm with other functions (e.g. a ‘A’ in AI truly represents. • Speech driven radiology show here is how technology report tool with SNOMED • Regression – linear, multiple, logistic database or user interface), we utilising intelligence within its coded output • Inference engine for rules or frames get ‘modules’, which sit at the algorithms can fall under many • Argumentation, temporal or spatial reasoner e.g. QSIM next level of complexity and are different subsections and with • Text generator using DCGs the problem-solving components varying degrees of complexity. • Case-based reasoning algorithm of a system. At the top level of We encourage developers and complexity, we have applications industry to be transparent as to Devised by Jeremy Wyatt, Director and Professor of Digital Healthcare, Wessex Institute of Health Research, Clinical Advisor on New or packaged systems comprising what complexity or methodology Technologies, Royal College of Physicians, Fellow of American College of Medical Informatics & UK Faculty of Clinical Informatics) two or more of these modules they are utilising when they (e.g. an autonomous robot).
14 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 15 Complexity of current projects As part of the survey we asked complexity scale (see previous At the highest end of the respondents to list some of the section), it can be seen that many complexity scale, 8% of solutions AI methods employed in their of the current solutions are using employed machine translation solutions. This enabled them ‘lowest complexity’ advanced methods. 20% of solutions to be categorised in a way that statistical techniques rather than indicated they used ‘other’ AI shows how solutions in the AI more complex AI applications. methods, including a range of RESULTS OF space vary greatly in terms of Classification and neural network chat bot solutions (considered complexity. machine learning methods ‘highest complexity’ solutions). were by far the most popular By mapping some of the methods techniques, used by 60% and 51% THE NATIONAL employed by survey respondents of solutions, respectively. against Professor Jeremy Wyatt’s SURVEY ABOUT AI The percentage of solutions Case studies reporting using a method of AI delivering TECHNOLOGIES IN Lowest complexity value now Classification 60% A range of case studies Neural networks 51% HEALTH AND CARE identified through the Decision trees 39% survey at various stages Clustering 36% of maturity (from those at Time series analysis 33% research stage through to examples with regulatory Ensemble methods 25% approval and/or publicly Regression 25% available) are listed in Graphical models 16% Appendix 1. Generative adversarial networks 11% This section presents key findings from 131 self-reported entries in These solutions are Knowledge based/expert systems 37% delivering value to the Image processing 33% response to our survey that began in Spring 2018. The information health and care sector in has been used to create an online map that illustrates what sort of the following areas: Middle complexity • Unlocking value in data/ Text to speech 9% problems are being solved currently, who some of the key players analytics Natural language processing 38% • Leveraging skills and are, and how we can group or categorise current projects to help our capacity Highest complexity • Organisational understanding of the current reality of AI in health and care. Machine translation 8% processes Other (please specify) 20% • Condition recognition. Unsure/Not applicable 9%
16 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 17 Diagnostics Unlocking Condition With wide consensus that diagnostics presents value in data/ recognition some of the strongest early AI use cases, we chose to make it a special focus for this first AI map and analytics 75% 60% in focus survey. Across the range of diagnostics categories, AI is already offering opportunities to free up workforce capacity and to dramatically increase diagnostic The percentage of accuracy. Taking advantage of the convergence solutions aiming across diagnostics, personalised medicine and data science, some organisations on the map are to address each already seeking to mine big data sets to enable specified category identification of individuals at the earliest stage of disease, when interventions have a higher likelihood of success. Overall, 66% of the initial solutions featured on our map indicated they contained one or more categories of diagnostics. As can be seen below, many early solutions are in diagnostic imaging/ radiology (25%), where digital imaging has been Leveraging in widespread use for a number of years. This skills and compares to far fewer solutions listed in pathology capacity (9%) and endoscopy (3%), where the digital and AI 43% solutions are only recently starting to emerge. Organisational processes Other 50% 24% Key areas We wanted greater insight into what types of problems are being addressed across the range of solutions. Survey respondents were able to select 44% Not applicable where AI 25% 21% multiple entries from a list of four categories. Results can be seen below. The percentage Unlocking value in data/analytics was the top 11% Other of companies can deliver category (75%) addressed by solutions submitted Imaging/Radiology Genetics for the survey, followed by condition recognition (60%). Organisational processes were addressed & Genomics by category of by half of the solutions, reflecting the increasing 9% diagnostics impact 3% Pathology 20% use of AI to automate routine clinical, managerial Endoscopy and back office tasks (e.g. document management, paperwork and scheduling). Physiological measurement
18 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 19 The percentage of solutions, which indicated a point of care, delivering in each point of care site The percentage Yes 57% of projects linked to smart Patient’s own connected home 49% devices Other 13% No/Unsure Hospital 68% Residential care 43% or nursing home, assisted living 38% Mobile, semi-mobile units and other and Personal/ Point of Another way that AI is enabling (8%), vendor/supplier managed ‘pop up’ style settings wearable new models of care is by settings (19%) and mobile, of no fixed abode 19% technology using remote diagnostic and semi-mobile units (19%), were 35% care monitoring capabilities to selected by the least number of change where and how care is respondents. delivered. We asked solutions to Already, 57% of solutions within indicate the points of care where the survey say they able to link they deliver services (multiple Vendor/supplier to smart connected devices (e.g. selections were possible). Internet of Things). With super- managed clinic Excluding those entries that did fast 5G broadband networks facilities 19% not indicate a point of care, the being tested this year, it is likely Medical majority of solutions reported that the number of IoT-enabled transport delivering services in hospitals solutions offered in non-acute vehicle 8% (68%), followed by a patient’s points of care will increase over Community or own home. Care settings such the coming years as 5G networks primary care as medical transport vehicles are rolled out more widely. clinic 39%
20 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 21 Regulation Proprietary (closed source) 35% Our survey aimed to capture a device. Devices meeting the Unsure 19% The percentage of full range of AI activity across requirements can place a CE Prefer not to say 16% solutions according the health and care ecosystem, mark (or logo) on their product to Not applicable 14% ranging from ongoing research show that the medical device has Other (Please specify) 8% to type of licence projects to fully scaled met the requirements as set out GNU GLPv3 (open source) 4% the computational commercial products and in the conformity assessment. services. With new solutions The CE marking also means Apache Licence 2.0 (open source) 3% product has coming to market regularly, it is that the product can be freely MIT Licence (open source) 2% important for buyers (including marketed anywhere in the EU. commissioners and consumers) In the United States, the Food and users of AI to have and Drug Administration (FDA) mechanisms for distinguishing provides medical device approval. which solutions have the When we asked our respondents appropriate evidence base and about regulatory status, only 18% Licensing are ready for ‘at scale’ adoption. Currently, 35% of solutions in Proponents of open standards, of solutions indicated they had In the UK, medical devices secured approval in the UK/EU or the survey have been developed such as the Apperta Foundation, must demonstrate that they abroad. A further 23% indicated using proprietary (closed a not-for-profit community meet the requirements set they were in the process of source) software, distributed interest company supported by out in the Medical Devices securing approval. under licensing agreement to NHS England and NHS Digital, Directive by carrying out a named users who are given maintain that liberating both data conformity assessment. The authorisation to modify, copy and applications and making assessment route depends and republish applications. The them portable and interoperable on the classification of the source code for this software is eliminates lock-in, facilitates not shared publicly for anyone innovation and competition, to look at or modify. Proprietary and forces vendors to compete software developers often on quality, value and service. pride themselves on product A downside can include the ‘usability’ and providing a high significant capacity and capability level of ongoing support for required to run open platform maintenance, security, content ecosystems. updates and training. The percentage of projects with regulatory approval A further 19% are unsure what In contrast, only 9% of licence their computational respondents report using one of product uses altogether, and this the following three open source needs to be explored further to licences – GNU GLPv3, Apache understand the reasons for this. Licence 2.0 and MIT Licence. Open platforms are vendor 41% 23% 18% 18% and technology neutral and are based on open standards, meaning that any application built on an open platform will operate on an open platform. Not applicable In process of No Yes securing approval
22 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 23 Who responded? Survey respondents included Respondents cite a broad range CEOs (42%), senior managers of experience with AI, with many (15%) and others working across indicating that they wear multiple the AI ecosystem in England. hats when dealing with AI. They represented both large organisations with 250 staff or more (32%), as well as micro organisations with less than 10 staff (38%) across private, public and charitable sectors, as well as academia. REAL WORLD ANALYSIS ON Q: What describes your current experience with AI? I evaluate FEASIBILITY AND AI 43% I use AI I procure AI IMPLEMENTATION 53% 17% In order to inform government policy and the AHSN Network AI Initiative offer, we conducted a survey of 106 thought leaders and AI pioneers during May and June 2018. In this section we outline survey results, highlighting top barriers and enablers for catalysing an ethical, I regulate AI 5% evidence-based market for AI solutions in health and care. I develop AI Other 51% 25%
24 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 25 Game- The best AI-enabled solutions always solve a valuable problem or ‘use case’, as expressed by Development in drug discovery and medical research will also be hugely aided by AI. ‘80% of all changing users - citizens, carers and Respondent views ranged from professionals. Working with AI being ‘ubiquitous’, ‘pervasive’ users to understand their needs and ‘high impact’ that will ‘replace dermatology use cases and then working with them to front line tasks’ to rather less prototype and test solutions iteratively is key to refining the optimistic predictions. Many see AI as a tool to help doctors and all diagnoses will be done using AI within 3 years product’s value proposition and healthcare professionals become ensuring successful uptake and more efficient and deliver a adoption at scale. We asked respondents to identify higher standard of care at less cost to benefit patients. Most see - it will be better than the areas where the strongest AI having a key role in helping to early use cases are. make decisions across the board and in better planning for scarce dermatologists at Overall, the views were clear that the main game-changing use resources. diagnosing’. cases for AI will be in three key areas in the immediate period: Respondent prediction • Diagnostics • Non-clinical (operational and administrative efficiency) • Health promotion and preventative health. The top three use cases are • ‘Translation into routine processes (e.g. document explored in more depth below. practice, widespread use management, paperwork and Treatments of clinical decision support scheduling). Machine learning will Health and Diagnostics Non-clinical Keeping Diagnostics (accurate and tools for complex diagnostics, increasingly be used to process promotion and interventions (accurate (e.g. save up to date early detection) was cited genomics and lifestyle advice’ images and texts. preventative (including and early time with with medical overwhelmingly as a strong health surgery) detection) administration) research early AI use case, with 94% of A reduction in administrative • ‘80% of all dermatology respondents citing it as either diagnoses will be done using staff overheads is expected, and extremely important or very a positive view on how AI will Extremely important 47% 40% 80% 66% 35% important. Some predictions from AI within 3 years - it will be better than dermatologists at impact clinicians also emerged. survey respondents include: diagnosing’. • ‘AI and clinicians will work more Very important 31% 28% 14% 23% 30% • ‘Huge impact in radiology closely as one team’ Use cases for non-clinical for assisted reporting and applications, for instance saving Quite important 16% 26% 5% 9% 21% screening’ time with administration, were • ‘Supervised machine learning - Clinicians remain in control’. • ‘Increased use in radiology and seen as extremely important Somewhat important 5% 5% 1% 2% 10% other imaging applications, or very important by 89% of particularly in prioritisation/ respondents. AI will increasingly Not at all important 1% 2% 0% 0% 4% triage of scans to ensure these be used in the automation of routine clinical and managerial are brought to human attention first’ tasks and for back office
26 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 27 Some predictions include: • ‘Significant improvements in workflow management and Comments from respondents include: • ‘Move towards using AI as • ‘AI will be instrumental in detecting minuscule changes in individual’s records (data), Overall AI To gauge what factors might support the development of AI in health and care, we asked respondents to consider the extent to which the enablers making it possible to detect following actions or policies were important in data analysis coupled with the a tool for early prevention and catch problems even realising the potential of AI in health and care. emergence of intelligent clinical and diagnosis using large before they actually form. It will decision support systems’ population level datasets i.e. The numbers below highlight actions or policies that enable prevention in the most identifying individual risk. Use respondents viewed as very or extremely important: • ‘AI will become a standard literate sense of the word’. of AI in demand management part of devices and image and predictive modelling’ For an excellent overview of management systems’. AI use cases, refer to Future • ‘Better allocation of resources Health promotion and Advocacy’s Ethical, Social and by earlier detection of patterns preventative health was cited as extremely important or very and thus disease, with Political Challenges of Artificial Intelligence in Health and Care 92% 88% 87% 82% 81% better targeted preventative important by 78% of respondents. (April 2018)3, a report produced strategies as a result’ Overall, respondents expect AI to with the Wellcome Trust. be used in a more predictive way, • ‘AI will take a large amount 3 Ibid. facilitating the shift from reactive of the early identification of care to a more preventative disease, allowing clinicians Engagement Ethical frame- Capacity and Clarity around Education of health model in which people are to focus on the complicated of healthcare work to build/ capability to ownership of healthcare more empowered to take care of cases’ professionals preserve trust and deliver scope data professionals their own health. transparency AI enablers We then looked at the relative importance of factors impacting diagnostics, building on potential actions suggested by our AHSN Network AI Initiative core specific to advisory group members. ‘Better allocation The percentages below show the key factors that pioneers believe are very or extremely important of resources by diagnostics to address in order to realise the potential of AI in health and care: earlier detection of patterns and thus disease, with better 93% 93% 85% 82% 78% targeted preventative strategies as a result’ Respondent feedback Data sharing for Support the Consistent Reviewing Work with medical imaging spread of proven labelling governance models commissioners for AI training innovations methods for (eg ST 11-7) in light to help them imaging data of machine learning understand how algorithms to buy AI-enabled products and services
28 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 29 Trust, privacy and ethics Education of healthcare Education professionals of public According to survey respondents, the top two factors enabling the • ‘The speed at which AI will have an impact on healthcare Educating healthcare professionals and the public on Extremely important 50% 37% realisation of AI in health and care will depend very much on the the potential of AI in a balanced are ‘engagement of healthcare public’s (and therefore the way was also raised as a key Very important 31% 28% professionals’ and establishing government’s) trust in AI and issue by survey respondents. an ‘ethical framework to build/ the company using patient This is central to achieving Quite important 17% 25% preserve trust and transparency’. data to develop AI. This will not and maintaining trust in an impact all AI products but a environment where there is much Overall, respondents agreed with significant proportion’ negative media coverage on Somewhat important 2% 10% the need for a clear governance the risks of AI and its potential structure to guide decisions and build trust. This needs • ‘There has to be first an enabling framework within the impact on workforce. A narrative Not at all important 0% 0% around data sharing is needed. to be underpinned by a clear NHS. This would include ethical There is also the need to engage ethical framework to address considerations, the right for the public actively in order to help such issues as transparency in human interpretation of the AI define the problems that need algorithm development. algorithms’ Key comments include: Predictions for the future include: solving and co-develop solutions Comments from the respondents enabled by AI. • ‘We need a narrative around • ‘Greater public support for AI • ‘We need transparency of include: data sharing and trust …’ due to better understanding algorithm development’ • ‘There is a need for widespread [of] how AI works’ • ‘[the potential of AI] is based • ‘We need public education understanding of augmented and enabling regulatory • ‘There will be a new cohort intelligence, predictive on the governance structure developed and ability to forge frameworks’ of healthcare professionals analytics, deep learning and that will be educated to think machine learning’ trust’. • ‘It’s not enough to ‘educate’ how to empower their human the public- we need active abilities with AI driven tools’. participation of patients and other interested parties at all stages of the development Engagement Ethical framework to process’ of healthcare build/preserve trust • ‘Much more needs to be professionals and transparency done to educate healthcare professionals, listen to/ Extremely important 58% 61% understand their concerns, and get their buy in. At the Very important 33% 26% moment the conversation is too polarised between naysayers ‘We need a who say, “AI will never change narrative around Quite important 8% 9% healthcare significantly” and techno-utopians who say, “AI will replace all doctors and data sharing Somewhat important 0% 3% nurses” - the reality is of course much more nuanced than that.’ and trust …’ Not at all important 1% 0% Respondent feedback
30 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 31 Workforce Evidence of effectiveness knowledge of AI and regulation Workforce opportunities will be in the new era of AI will also be • ‘Ability for enough people to Linking strongly with the theme 78% of respondents felt that • ‘Regulation needs to be addressed in detail in the Topol essential, along with training in understand the back end of of trust, the requirement for regulation was extremely light touch, to allow patient Review (being led by Dr Eric technical and legal aspects of AI. AI, and even perhaps learn the evidence of effectiveness of important or very important confidentiality but at the same Topol and facilitated by Health coding within hospitals to help the digital health innovations in realising the potential of time allowing the industry to Above all, securing clinical Education England), but it is understanding, engagement internal management’ and intelligent clinical decision AI in health and care. Gaps in flourish so we can achieve important to note that at a high and buy-in to the co-design support (algorithms) was a topic regulating AI-enabled products efficiencies in the fast time level, education has come out as • ‘We need to understand and and use of AI will be important that ran throughout respondents’ and services, and uncertainty possible’ a clear enabler. design the human computer to leverage the potential of the comments. about the roles of the various interaction and how algorithms • ‘Regulation is important, but it 87% of respondents indicated technology. This will not only regulators and when a product, are used in practice’ A number of respondents called would be better to find a global that building capacity and assuage clinicians’ fears and service or algorithm should for the ability to explain the solution rather than country by capability is extremely important concerns, but will ensure that the • ‘Understanding financial and be subject to regulation were algorithm and providing enough country. Particularly concerned or very important to achieving AI algorithms developed augment clinical pathways in more detail also strong themes. There is a information to allow regulators to if the UK decides to go its own AI’s potential. This includes basic (rather than replace) and increase will be important…’ clear need for a new regulatory independently replicate results way post-Brexit, as the NHS education on AI and its potential the accuracy of human clinical framework to keep up with • ‘Helping to build interdisciplinary on a similar set of data, ensuring market isn’t large enough to applications for senior managers decision making. Helping senior advances in AI. teams so that clinicians with algorithms are safe and unbiased. be worth separate certification and directors in clinical, decision-makers to understand good ideas can have these Representative comments Comments included: beyond FDA [United States] & management, commercial and and have realistic expectations of what AI has to offer will also be realised by people with include: CE [EU]’. procurement roles. Training in • ‘Legacy regulations will important. computer programming skills’ • ‘Many AI-based tools will limit widespread adoption areas such as user-driven design, change management, ethics and Key points to consider from our • ‘Having highly skilled data struggle to get used through of diagnostics and health having difficult conversations survey participants include: scientists involved is crucial’. lack of evidence and/or clinical prevention applications’ conservative behaviour’ • ‘[Government should] address • ‘Clinical support should be barrier of regulation and gained by discussion of the the ability to rapidly iterate, Somewhat Extremely scientific case and justifying prototype, and validate important important the technology. Just offering prospectively’ 46% an algorithm lacks scientific 0% credibility’. Regulation of AI Not at all important Capacity and Extremely important 44% 1% capability Very important 34% to deliver Quite scope Quite important 17% important Very 12% important Somewhat important 5% 41% Not at all important 0%
32 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 33 Funding and Given AI’s potential for system wide impact, with funding flows and incentives crossing ‘Understand commercial models organisational boundaries and hierarchies, some respondents also commented on the opportunity to reimagine ‘value’ of public/ commercial models in the new Despite the financial challenges experienced in the NHS, ‘funding models for AI development and deployment’, which came in at 92% of respondents said they believed ‘supporting the era of AI: NHS data and how and budget restraints’ featured only tenth on the list of thirteen 12th and 13th (last) on the list respectively. spread of proven innovations’ is extremely important or very • ‘Understand ‘value’ of public/ NHS data and how this can be this can be sold factors affecting the potential of important to realising the sold to developers or used to AI in health and care, with 68% of respondents indicating that This result could be reflective of the early stage of the potential of AI in diagnostics. generate additional income’ to developers or funding was extremely important or very important. Featuring even development of the AI market in health and care. In contrast, • ‘Evaluate cross department business models - who used to generate in diagnostics, where the early additional income.’ owns hospital-wide clinical lower on the overall list of key AI use cases are strongest and efficiency? For example, factors were ‘NHS internal market where we are already starting to will radiology purchase an and procurement’ and ‘lack of see products come to market, clarity over appropriate business AI product whose benefit is Respondent feedback realised by reduced drug cost in neurology? How do those dots get joined up?’. Lack of clarity over appropriate business models for AI NHS internal Funding/budget development market and Extremely restraints and deployment procurement important Not at all 50% important 1% Extremely important 45% 34% 29% Very important 23% 25% 34% Support Quite important 27% 33% 21% the spread Somewhat important 2% of proven Somewhat important 4% 8% 13% innovations in Not at all important 1% 0% 3% Quite diagnostics Very important important 5% 42%
34 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 35 Data quality, sharing Consistent labelling Data sharing for and interoperability methods for imaging data medical imaging for AI training Extremely important 54% 69% The importance of a sound data Respondents’ comments include: infrastructure with high quality • ‘The current datasets in Very important 31% 24% data and the relevant standards healthcare are patchy, dirty on sharing and interoperability came through as key factors in and often incorrect! Garbage Quite important 11% 6% in garbage out. Often data are realising AI’s potential. not digitised. The first action in machine learning or AI is to Somewhat important 3% 1% Data quality clean up dirty data’ A key concern affecting the ability • ‘We need clean labelled un- Not at all important 1% 0% of AI to deliver on its potential gamed datasets’ is that of the quality of the data • ‘We need data compatibility itself, much of which is not through labelling and digitised or in machine-readable standardisation’. format. ‘Data readiness’ (getting data ready for AI) was a key Data sharing and interoperability theme. Overall, the view emerged that Key points voiced by respondents • ‘We need distribution across the underlying data infrastructure were: public and private sectors, is not fit for purpose for AI with patient access to any Open standards and requires standards to • ‘The role of private companies information generated and Clarity around to promote data and developers including facilitate data sharing and the ease of sharing this’ Data sharing ownership of sharing and ownership of and access to development of appropriate public and patient data and • ‘Clarification of concepts framework data interoperability commercial models to leverage how data sharing agreements around patients being curators the value of public/NHS data. are negotiated’ not owners of the (“their”) data; This is an especially pressing Extremely important 56% 54% 47% concern where public sector • ‘The underlying IT compliance with GDPR but still entities have entered into allowing retention of images/ infrastructure in the NHS is Very important 24% 28% 31% agreements with companies to poor and not AI ready. We need blood results/other data to feed Big Data’. process data. These datasets a large push to standardise IT Quite important 16% 10% 19% often end up in proprietary format or in difficult to access formats and data sharing’ repositories. Intellectual property • ‘Robust development, Somewhat important 4% 7% 2% of algorithms developed using testing and validation of AI these proprietary data sets often is key. Without appropriate governance it will be a liability’ Not at all important 1% 0% 1% rests with the companies (outside the public sector/NHS)4. 4 Naylor, A. and Jones, E. (2017). Unleashing the potential of health and care data. Future Care Capital. Available at: https://futurecarecapital.org.uk/policy/healthcare-data/.
36 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 37 Towards a A number of respondents endorsed the NHS Digital and NHS England are also laying the establishment of open data ecosystems in order groundwork for open innovation with a number of to leverage insights from multiple datasets, initiatives including: sustainable enabling the real power of AI to come into its own. • Apperta Foundation, which recently published For example, Transport for London provides a ‘Defining an Open Data Platform’ common API to access 80% of the UK’s transport data. Thousands of developers (including the • Code4Health, which provides a home for the ecosystem original Citymapper app) build on top of this open API platform. Similarly, Open Banking, recently introduced in the UK, will see the UK’s nine biggest increasing number of open source projects providing software suitable for use in health and care banks release data in a secure, standardised form, so that it can be shared more easily between • International exemplars in the area of open authorised organisations so they can then use it innovation platforms in the health and care space to create more products and services to benefit include REshape Centre Radboud (Netherlands) citizens. The intention is to put citizens in control and Boston Children’s Hospital (United States) of their own banking data, providing an easier way • Closer to home, University Hospitals Plymouth for them to move, manage and make more of their NHS Trust and Great Ormond Street Hospital are money. exemplars in building open data ecosystems and In order to unlock open innovation around data- fostering open innovation. driven health and social care services, any open data ecosystems must provide mechanisms for data to flow safely and securely across disparate Case Study health and care organisations, whilst ensuring informed consent and transparency. Enabling citizens to ‘donate’ their consumer data (e.g. from banking, retail, transport, telecommunications, utilities, etc) and data from sensors and IoT-enabled Great Ormond Street Hospital DRiVE Unit (Digital devices could also support citizens to stay healthy and in their own homes for longer5. Research, Informatics and Virtual Environments) Momentum is growing to establish open data GOSH’s DRiVE unit provides a good example in a secure environment in the cloud that ecosystems across health and care. This should of the type of open data ecosystem and is compliant with ICO and GDPR guidance accelerate over time as forthcoming Industrial infrastructure required for exploitation of AI’s regarding the use of data for research. Strategy Grand Challenge investments in initiatives potential within health and care. The DRiVE Clinicians, researchers and industry partners such as Digital Innovation Hubs (connecting regional unit provides both a concept and a physical looking to address specific problems can come health and care data with biomedical data in secure space dedicated to accelerating research together in secure virtual ‘workspaces’ to run environments) and the Healthy Ageing Challenge and evaluation of new AI-enabled technology analyses and APIs. The data therefore does start to bear fruit. and data analysis, with the aim of developing not leave GOSH’s control and governance, scalable solutions for child health. Working providing full transparency. The GOSH team with partners including University College and regulators have full data provenance, London (UCL)/ Alan Turing, major industry including details of IP addresses accessing the partners and NHS Digital, early areas of focus code and whether changes have been made. will include machine learning, assisted decision During the development phase, the GOSH making and the use of medical chatbots. team are also working to generate synthetic datasets for innovators to test their early GOSH’s open data ecosystem captures algorithms on, prior to validation on real data. and integrates data from multiple sources 5 Woods, T.M. and Kihlstrom, E. (2018). Data and the Future of Health and Social Care. Report, Proceedings and Key Recommendations. Round Table, 17th November 2017. FutureHealth Collective. Available at: https://www.colliderhealth.com/future-health-collective.
38 Accelerating Artificial Intelligence in health and care: results from a state of the nation survey Accelerating Artificial Intelligence in health and care: results from a state of the nation survey 39 Where are we now? Whilst AI solutions are increasing is currently reviewing technical 1. Build fairness and transparency in their complexity, most now and clinical safety requirement in digital health innovations, delivering impact are on the low and design standards before algorithms and clinical decision complexity end of the spectrum. publishing onto the library. support tools. Understanding the vast potential Ultimately, the NHS must of AI – as well as its limitations 2. Help identify the requirements protect its reputation as an and standards that - will be key moving forward. internationally trusted health and As one survey respondent organisations and suppliers care system, ensuring patient need to fulfil in order to show commented, ‘AI is still evolving… safety and high quality care, and it won’t solve all the problems that products are safe, secure preserving the trust between and maintain public trust. healthcare faces as the moment’ citizens, clinicians and the wider and we must avoid the trap of health and care system. In order 3. Identify gaps within regulatory SUMMARY ‘overhyping potential, unrealistic to do this we have collated a set and approval processes that claims, and poorly thought out of principles outlined in a Code of need to be addressed to products’. Conduct, which is in early stages accommodate developing Top AI enablers include of development. technologies. AND NEXT engagement with health The purpose of the Code is By working collaboratively with professionals, as well as to provide a source of clear academia, industry, innovators, grounding the use of AI in principles and guidance for the commissioners and AHSNs to real problems as expressed development of trusted digital iterate and continually update STEPS by citizens, carers and other health innovations and intelligent these principles, we can go health professionals. Providing algorithms within the UK NHS some way to staying abreast of mechanisms for improving data health and care sector. This evolving technologies, helping to quality and the underlying data code can be used by innovators, catalyse the scale and adoption infrastructure will also be key, industry, commissioners, of intelligent technologies. along with introducing a safe, academia and individuals, Addressing these requirements evidenced and transparent as a framework to support with the right solutions will spur approach to how algorithms and development and deployment of collaboration across the NHS, The analysis of survey responses, together with the constellation of innovations are developed. any DHI or intelligent algorithm social care and other partners in (IA). Whilst this code will ensure Currently in the NHS, we have the ecosystem and build public organisations in the AI map and illustrated by the case studies in this a number of programmes such that the DHI/IA being developed trust. Strong cross government are in line with the principles and report, reveals that AI in health and care is still at a relatively early as the Local Health and Care values of the UK health and care collaboration, including pooling Record Exemplars, NHS Test resources and partnering on joint system, it is still a requirement stage. At the same time, there are many promising early use cases Beds and the forthcoming Digital that the relevant regulatory initiatives is also underway and Innovation Hubs that give us the is the key objective of the AHSN for AI in this space, especially in diagnostics. The health and care opportunity to test and refine and/or approval processes are Network AI initiative. adhered to. digital health innovations (DHIs) AI ecosystem continues to grow at pace, with a range of promising and algorithms with our partners. This code, if followed, can ensure that within the NHS and the wider interventions in the pipeline, currently gathering evidence that they The NHS Apps Library and UK health and care sector we Digital Assessment Questions are safe, effective and offer value prior to regulatory approval and are examples of how the NHS collectively: widespread implementation.
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