1. A brief introduction to automation and AI


1-1 What are automation and AI?

Automation is the use of technology to perform rule-based tasks with minimal human input. Encyclopaedia Britannica describes it as ‘performing a process by means of programmed commands combined with automatic feedback control to ensure proper execution of the instructions’, while the International Society of Automation defines it specifically as ‘the creation and application of technology to monitor and control the production and delivery of products and services’. While automation typically involves the execution of tasks previously done by humans, this is not necessarily a defining feature, with increasingly sophisticated technology enabling the automatic performance of tasks humans could not do, such as rapid analysis of large datasets.

Automation is a major theme in discourse about changing labour markets and the future of work. For many tasks, automated systems have the potential to improve on human performance, by reducing errors and improving productivity, and analysis conducted in 2019 by the Office for National Statistics (ONS) found that around 1.5 million jobs in England were at high risk of having some of their duties and tasks automated in future. This report will look at both the potential automation of tasks usually undertaken by health care workers, as well as the use of automation and AI to assist health care workers in performing tasks.

Advances in robotics and AI are extending the reach and capability of automation, both in the realm of physical tasks and increasingly the realm of cognitive tasks; as the AI Index puts it, ‘robotics puts computing into motion and gives machines autonomy. AI adds intelligence, giving machines the ability to reason’. A 2016 House of Commons Science and Technology Committee report describes AI as statistical tools and algorithms that ‘enable computers to simulate elements of human behaviour such as learning, reasoning and classification’. Recent years have seen advances in AI due to the increasing availability and quality of data, and improvements in technology and processing power. These include developments in machine learning, where algorithms are trained to make predictions using large datasets, and especially in ‘deep learning’, a type of machine learning using artificial neural networks. Among other things, these systems can learn to recognise and classify patterns in digital representations of sounds, images and text.

Given the significant overlap between these fields, this report will often refer to AI and robotics alongside automation, including situations where these technologies are used to automate some parts of a task (such as information analysis for decision making) but not others (such as decision selection).

1.1.1. Modes of automation: replacing versus assisting

It’s useful to distinguish between some different ways in which automation can relate to human task performance in health care.

Replacing: As highlighted above, in some cases automated systems are intended to perform tasks previously carried out by humans, replacing human input.

  • This can happen where an automated system is able to perform tasks to a similar level to human workers (or at least to a ‘good enough’ standard), and so using an automated system to perform these tasks can free up health care staff to focus on other work. In Figure 1, this mode of automation is described as substituting for human input.
  • On other occasions, the performance of an automated system might significantly exceed human capabilities, so by replacing human input it provides an opportunity to improve task performance (for example, where an automated system can execute tasks at much greater speed), and this may provide a rationale for automation independently of the benefits of releasing staff time. In Figure 1, this mode of automation is described as superseding human input.

Assisting: More commonly, automated systems can be used in health care to assist workers, rather than replace them.

  • This can happen by using technology to automate just one component of a task or to provide additional capacity or functionality in a way that allows a worker to improve task performance – not because they can’t do what the technology is doing, but simply because having the technology effectively increases their capacity and allows them to focus on other aspects of task performance (for example, using dictation software to take notes). In Figure 1, this mode of automation is described as supporting human input.
  • On other occasions, technologies designed to assist human task performance may also extend human capabilities, even though they are not intended to operate autonomously. For example, surgical robots may allow a greater degree of precision than humans alone, while AI-driven clinical decision support tools may exceed human information-processing capabilities. In Figure 1, this mode of automation is described as strengthening human input. Indeed, research suggests that while AI systems can match or even exceed humans in some ‘high end’ tasks (those requiring a high level of cognitive ability), when these systems are combined with human experience, intuition and knowledge, the impact of AI and robotics can be increased.

Figure 1 illustrates these different modes of automation. Note that the same technology could be used in different ways; the mode of automation will depend on how it is deployed on any particular occasion. It is worth noting that when technologies are deployed for supporting or substituting (the bottom row), the primary motivation is often to free up staff time, whereas when technologies are deployed for strengthening or superseding (the top row), the primary motivation is often to improve task performance.

Figure 1: Modes of automation

The use of automation to assist (and enhance) rather than replace is one way in which the impact of automation in health care can differ from other sectors. In industries such as agriculture and manufacturing, new technologies have often replaced labour (for example, the combine harvester or industrial robots for painting), a trend that continues today. A 2019 report by Oxford Economics estimates that around 1.7 million manufacturing jobs have been replaced by robots since 2000, including 400,000 in Europe. In health care, by contrast, new technologies have for the most part tended to supplement rather than replace labour, providing the means for health care workers to improve care or do their job more efficiently. This is partly because, as we explore in Chapter 3, many tasks in health care are difficult to automate. Instead, it is the potential of automation to assist NHS staff to manage high workloads that has attracted interest as demand for staff continues to increase.

Box 3: A brief history of automation in health care

Automation has its roots in the Industrial Revolution when the introduction of the steam engine enabled the generation of vast amounts of energy, allowing the mechanisation of tasks previously undertaken by craftsmen or by individual artisans. Since then, innovations such as the spinning jenny, assembly line and personal computer have seen the automation of many types of work. Brynjolfsson and McAfee argue that automation can be divided into two periods: the first, in which machines were introduced to conduct physical tasks (such as assembling a product), and the second enabled by the development of computing, in which machines also conduct cognitive tasks (such as record-keeping).

Automation has a long history in health care. Many examples are now so well established that we might not think of them as automation. For instance, in the early 1900s the first electrocardiogram was developed to monitor heart rate, something previously done manually. From the 1940s onwards, the kidney dialysis machine evolved to become automatically functioning. In the late 1970s, the desktop computer began to enable the automation of administrative tasks, for example clerical tasks such as financial calculations. Computers also began to be integrated into clinical pathways, initially to enter orders and report results and then to hold databases, images and patient records, as well as for the continuous monitoring of patients. Pharmacists have also seen the automation of several aspects of their work; for example, the first digital pill counter was deployed in the late 1960s.

Robots, too, have been used in medicine and health and social care for over 30 years, from robot-assisted surgery and rehabilitation, to personal robots serving as companions or motivational coaches, or assisting people with domestic activities. In surgery, robots are being used to perform movements once made by humans, though typically requiring tele-operation or supervision by a health care professional. In addition, the ongoing miniaturisation of electronics is expanding the types of procedures that surgical robots can support, although it is important to note that the evidence base for the clinical effectiveness of robots in surgery is still developing.

The potential of new data analytics and AI to support clinical decision making, such as image analysis and risk prediction tools, understandably attracts considerable excitement. At the same time, there remains vast potential for the automation of less complex tasks, including work that has administrative components such as processing prescriptions, referrals and bookings, through technologies that are currently available or are already in the NHS but not being used to their full potential. For example, the 2016 Carter Review found that trusts were not getting ‘full meaningful use’ from technologies they had invested in such as e-prescribing software.

1.1.2. Automation and the future labour market

While automation is not a new concept (see Box 3 for a brief history of automation in health care), the evolving capabilities of AI and robotics to undertake increasingly complex cognitive tasks, as well as an ever-growing range of manual tasks, have become a key consideration in analyses of how jobs and occupations could change in the coming decades.

Studies modelling the impact of automation on the future labour market have produced a wide range of estimates, but suggest the impact could be both far-reaching and unevenly distributed. For example, Frey and Osbourne estimate that 47% of occupations could be automated over the next 20 years, with roles in office and administrative support, production, transportation and service occupations highly susceptible. On the other hand, the Organisation for Economic Co-operation and Development (OECD) estimates that only 9% of occupations are at high risk of automation because many still contain a substantial share of tasks that are hard to automate. The ONS, investigating work in England, suggests that the impact of automation will vary across the labour market, with lower-skilled roles more susceptible to automation and women, young people and part-time workers most likely to work in roles that are at high risk of being automated.

Predicting the precise effects of automation on employment is difficult, however. The OECD study argues that even if a job is at high risk of automation, it will not necessarily result in job losses because workers can adapt by switching tasks and expanding their roles, and because technological change will also create new jobs. Similarly, a 2019 European Commission report argues it is unclear whether the net effect of automation will be job replacement or augmentation, given that AI and robotics will create new jobs as well as eliminate others.

Modelling also suggests the impact of automation will vary by sector. In line with the observations above that automation in health care has tended to supplement rather than replace labour, studies estimate the future impact of automation on jobs in health and care to be lower than in other sectors. For example, PricewaterhouseCoopers (PwC) estimates the proportion of jobs at high risk of automation in health care could rise from around 3% in the early 2020s to 20% by the mid-2030s, with financial services seeing the biggest effects over the short term and the transport sector over the longer term. The PwC model suggests the impact on jobs in health and care, as well as in education, will be lower than in other sectors.

In light of these trends, policymakers are grappling with how to respond to the labour market impacts of automation. The 2016 Taylor Review, for example, explored how employment practices need to change in order to keep pace with the modern economy. Specifically in relation to automation, the Review highlighted the importance of supporting people to gain appropriate skills for the future workplace – in particular, skills that are less likely to be affected by automation, such as relationship building, empathy and negotiation, which over time could become more valuable. It also highlighted the importance of lifelong learning to enable people to adapt to, and remain relevant in, a changing labour market.

1-2 Policy approaches to support automation and AI in health care

In health and care, policymakers are also focusing on helping providers and patients exploit the potential of new technologies. In England, the Department of Health and Social Care (DHSC) and NHS England and NHS Improvement have focused on several broad challenges relating to digital technology and information technology (IT). The aim in particular has been to ensure the NHS has the right basic infrastructure in place, with systems that are interoperable and enable the exchange of data, as well as addressing challenges around leadership and skills. Key initiatives include:

  • The 2016 Wachter Review of health IT in secondary care, noting that the quality of IT systems across the NHS remains patchy, interoperability continues to be a challenge and progress towards digitisation of records is slow, made a series of recommendations to achieve digitisation.
  • The DHSC’s 2018 ‘vision’ for digital, data and technology in health and care aspires to harness fast-developing technologies including AI and robotics, but also acknowledges the need to ‘get the basics right’ and ensure the NHS has appropriate digital infrastructure in place. This is essential not just for basic clinical and administrative functions, but also as a platform to enable the use of more sophisticated technologies.
  • In 2018, NHS England and NHS Improvement launched the Local Health and Care Record Exemplars Programme, designed to enable the safe and secure sharing of health and care information across different parts of the NHS and social care.
  • The NHS Long Term Plan, published in January 2019, also committed to making better use of digital technology, including providing better digital access to services and ensuring health records and care plans are available to clinicians and patients electronically. It also restated ambitions to make greater use of AI to support clinical decision making and to put in place IT infrastructure that is secure and allows interoperability between systems.
  • NHSX, a joint unit between the DHSC and NHS England and NHS Improvement, created in 2019, provides leadership for digital technology in health and social care and will play an integral role in NHS England and NHS Improvement’s transformation directorate (see Box 7 on page 30 for further information). The 2019 Topol Review looked at how to equip the health care workforce to work effectively with new technologies, to inform the development of Health Education England’s (HEE’s) workforce strategy. HEE is seeking to address the workforce requirements set out in the Topol Review through the Digital Readiness Programme.

In Scotland, the Scottish government, the Convention of Scottish Local Authorities and NHS Scotland see digital technology as a critical enabler for improving health and care. The 2018 Digital Health and Care Strategy for Scotland seeks to empower citizens to manage their own health, live independently and access services through digital means, and also to put in place the architectural and information governance ‘building blocks’ necessary for the effective flow of information across the whole care system. Digital technology is also an important part of the Welsh government’s vision for health and care. A Healthier Wales: Our Plan for Health and Social Care seeks to use digital, data and communications technologies to help raise the quality and value of health and social care services. To help embed the development and use of digital services in health and care in Wales, the Welsh government launched a new special health authority, Digital Health and Care Wales, in April 2021.

The term ‘automation’ is not especially prominent in UK health care policy discourse, perhaps because it is often taken to mean the full automation of tasks (replacing human labour), whereas many of the technologies in question are seen primarily as tools to support staff to undertake tasks rather than to wholly automate them. Where automation has been discussed, the focus has been mainly on reducing the burden of administrative work for clinical staff, and improving efficiency and productivity, on the assumption that automation can free up time for patient care., Notwithstanding this lack of prominence of automation as a theme, there has been considerable policy interest in digital and data-driven technologies that have the potential to automate tasks, including AI:

  • As part of the Industrial Strategy’s ‘AI Grand Challenge’, in 2019 the UK government set an ambition of using data, AI and innovation to transform the prevention, early diagnosis and treatment of diseases like cancer, diabetes, heart disease and dementia. This included launching five new centres of excellence in digital pathology and imaging with AI.
  • In 2019, NHS England and NHS Improvement set an ambition for the NHS to become a world leader in AI and machine learning within five years, inviting technology innovators to submit proposals for ‘how the NHS can harness innovative solutions that can free up staff time and cut the time patients wait for results’.
  • £250m was invested in the creation of a new NHS AI Lab, that sits within NHSX and focuses on areas such as regulation, imaging, disease detection, ethics and supporting the development of AI products. As part of this, the Artificial Intelligence in Health and Care Award is making £140m available over three years to accelerate the testing and evaluation of AI technologies that support the aims of the NHS Long Term Plan. The AI Lab has also launched an ethics initiative to ensure that AI products used in the NHS and care settings do not exacerbate health inequalities, in partnership with the Health Foundation, the National Institute for Health and Research, the Ada Lovelace Institute and HEE.
  • The Accelerated Access Collaborative, a partnership between government, industry and the NHS, was established in 2018 to identify promising technologies, including AI, that the NHS should prioritise for adoption.
  • In 2019, the DHSC and NHS England and NHS Improvement launched a code of conduct (since updated to become the Guide to good practice for digital and data-driven health technologies) to guide the development and use of digital and data-driven technologies, designed to protect patient data and ensure only high-quality technologies are used by the NHS.
  • Prior to the announcement of the new National Institute for Health Protection, Public Health England’s Strategy 2020 to 2025 had set an ambition to develop ‘personalised prevention’ of ill health and enhance the data and surveillance capabilities of the public health system using technology such as AI.
  • In 2018, the UK government announced the creation of a new AI health research centre in Scotland. Based in Glasgow, the Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD) is focused on the exploration of how AI could improve patient diagnosis.
  • The Welsh government is currently supporting several AI projects, including the use of AI to detect harmful or potentially harmful incidents in real time for people affected by falls, people with dementia and people with cognitive impairments.
  • In 2020, the House of Lords Liaison Committee on AI published the report AI in the UK: No Room for Complacency, which considers the UK government’s progress against the recommendations made by the Select Committee on AI in its 2018 report, AI in the UK: ready, willing and able?
  • In 2021, the UK AI Council, an independent expert committee that provides advice to the UK government, published a road map to help the UK become one of the best places in the world to live with, work with and develop AI.

1-3 Public, patient and professional attitudes to automation and AI

Several themes recur in surveys exploring public views of automation and AI in health care. While access, speed and accuracy are often cited as potential benefits, many people clearly value human agency, interaction and judgement and don’t want to see them compromised or removed. For example:

  • An international survey in 2016 found that, while there is some support for using AI and robotics to meet health care needs, people in the UK were more sceptical compared to other countries. For example, UK respondents were least willing to undergo ‘surgery performed by a robot’. UK respondents felt that the main advantages of AI and robotics in health care were quicker and easier access, and faster and more accurate diagnosis. However, the main disadvantages cited were inability to trust automated decision making, the belief that only humans can make the right decisions and the view that health care needs a ‘human touch’.
  • A 2017 UK poll found that while many would be happy with AI playing a supportive role, there were concerns about the automation of work typically done by doctors and nurses. In particular, the poll showed that while many respondents would welcome the use of AI to help diagnose diseases, most did not think AI should be used for other tasks usually performed by doctors and nurses, such as suggesting treatment.,
  • Another 2017 UK poll found that people were optimistic about the potential of the technology to improve the accuracy and speed of diagnosis and were also ‘happy with the idea of doctors and machines working together to provide a better service’. Their main concern was the prospect of human interaction being lost. For example, respondents cited the importance of human involvement in final diagnosis and treatment planning, which they felt should be reviewed, authorised and communicated by a human doctor.

Turning to the perceptions of health care professionals and managers on the prospects for, and impact of, automation, there is a mixture of optimism and scepticism. For example:

  • In a 2018 US survey of radiologists exploring views about job security, respondents said that AI would make their job radically different in the next 10–20 years, but very few felt that it would make their roles obsolete., Most respondents were planning to learn about AI in relation to their jobs and a smaller majority were willing to help train an algorithm to do some of the tasks of a radiologist. In the UK, the Royal College of Radiologists has similarly taken a positive view of AI, welcoming the introduction of appropriately regulated technologies to enhance clinical practice, citing the potential to improve outcomes and efficiency, and release time for direct patient care and research.
  • A 2018 survey of managers and clinicians working in NHS trusts, clinical commissioning groups and NHS England and NHS Improvement found that while senior managers showed enthusiasm about AI, clinicians were more cautious, emphasising the need for safeguards.
  • There appears to be less professional caution around the prospect of automating administrative tasks compared to clinical tasks. For example, a recent UK survey of GPs showed that while the majority were sceptical about the potential for future technology to perform most primary care tasks as well as or better than humans, many were optimistic that in the near future technology would have the capacity to fully replace GPs in undertaking administrative duties related to patient documentation., The Royal College of General Practitioners has highlighted the automation of administrative tasks as one of four key areas where technology could be particularly beneficial.,

Box 4: Public and NHS staff attitudes to automation and AI in health care

To further investigate attitudes to automation and AI in health care, we commissioned surveys of the UK public and NHS staff, conducted online by YouGov in October 2020, the results of which are described at various points throughout this report.

To start with, we asked people about their familiarity with the topic: specifically, how much they’d heard, seen or read about automation and AI in health care (respondents were provided with definitions and examples of these technologies). Some 29% of the public said they’d heard, seen or read ‘nothing at all’ about it. This was also true of 24% of the NHS staff surveyed – a striking reminder that, while there is real interest in this topic in many policy, academic and clinical communities, it remains far removed from the working lives of many NHS staff. While majorities of both the public and NHS staff surveyed had encountered something on this issue before, only 2% of the public and 3% of NHS staff surveyed said they’d heard, seen or read ‘a lot’, with 17% of the public and 18% of NHS staff saying ‘a fair amount’ and 48% of the public and 52% of NHS staff saying ‘not very much’. So there is clearly work to be done to engage with patients, staff and society as a whole to inform decisions about the future use of automation and AI in health care.

Figure 2: Public and NHS staff familiarity with automation and AI

In general, how much, if anything, have you heard, seen or read about automation and AI in health care (eg in the news, on social media, or from family, friends, colleagues, etc.)?

Our surveys also asked how positive or negative people felt about the use of automation and AI in health care – as a crude ‘temperature test’. In both the public and NHS staff surveys, more felt positive than negative, but opinion was closely balanced, with the public feeling more positive than negative by 40% to 37% and with NHS staff surveyed feeling more positive than negative by 40% to 36%.

There were some interesting differences underneath these headline figures. In the public survey, some groups were less positive about automation and AI in health care than others, including women, people with a health condition or disability and people with a carer. While men felt more positive than negative about the use of automation and AI by 48% to 33%, women felt more negative than positive by 41% to 33%. Similarly, people with a carer felt more negative than positive by 42% to 34%, as did those with a health condition or disability by 41% to 38%. Age is also sometimes highlighted as a factor affecting attitudes to technology, and there were some modest age differences within our results, with younger people (aged 18–34) feeling more positive than negative by 41% to 31%, while for older people (aged 55 or older) this margin was just 1 percentage point: 41% to 40%. More research is needed to understand why these differences exist, but they underline the importance of engaging with patients and the public in the development and deployment of automation and AI to co-design solutions, in order to help make sure these technologies work for everyone and that different preferences are taken into account.

Among the NHS staff surveyed, there were some differences by professional group – perhaps reflecting the different perspectives that different staff groups will have on what these technologies might mean for health care. For example, medical and dental staff surveyed felt more positive than negative about the use of automation and AI in health care by 43% to 36%, and nurses and midwives by 39% to 38%, but health care assistants felt more negative than positive, by 41% to 33%.

However, these differences were dwarfed by the impact of familiarity with the topic. Among the public, those who said they had heard, seen or read ‘a lot’ or ‘a fair amount’ about automation and AI in health care felt much more positive than negative about the use of these technologies, by 70% to 26%, while those who answered ‘not very much’ or ‘nothing at all’ felt more negative than positive, by 41% to 35%. A similar pattern was evident in the NHS staff survey: those who said they had heard, seen or read ‘a lot’ or ‘a fair amount’ felt much more positive than negative about the use of automation and AI in health care, by 71% to 21%, while those who answered ‘not very much’ or ‘nothing at all’ felt more negative than positive, by 40% to 32%. This suggests that helping to familiarise people with this topic could play an important role in shaping attitudes to this agenda in future.

Figure 3: Public and NHS staff attitudes to the use of automation and AI in health care

Overall, how positive or negative do you feel about the use of automation and AI in health care?

Having explored the current context for automation and AI, in the next chapter we look at some of the opportunities to apply these technologies in health care.

* One fact that is often cited is the ongoing growth of the health care workforce. See for example The health care workforce in England: Make or break? The Health Foundation, The King’s Fund and the Nuffield Trust; 2018.

Some are concerned by the use of robotic surgery for common surgical procedures with limited evidence and unclear clinical benefit. A recent UK study found no evidence of a difference in 90-day postoperative hospital days between robotic and laparoscopic ventral hernia repair. Sheetz and colleagues argue that the use of robotic surgery has outpaced the generation of evidence to demonstrate its effectiveness. See Olavarria O, Bernardi K, Shah S, Wilson T, Wei S, Pedroza C, Avritscher E, Loor M, Ko T, Kao L, Liang M. Robotic versus laparoscopic ventral hernia repair: multi-center, blinded randomized controlled trial. BMJ. 2020; 370; Sheetz K, Claflin J, Dimick J. Trends in the Adoption of Robotic Surgery for Common Surgical Procedures. JAMA Network Open. 2020; 3(1): e1918911.

Respondents were asked for their views on a range of hypothetical scenarios.

§ A total of 45% felt that AI should be used for helping to diagnose diseases, as opposed to 34% who did not. On the other hand, 63% felt that AI should not be used for taking on tasks usually performed by doctors or nurses, compared with only 17% who felt it should be.

Survey of 69 trainees and resident diagnostic radiologists at a single radiology residency training programme.

** Most GPs believed it unlikely that technology will ever be able to fully replace physicians for diagnosing patients (68%), referring patients to other specialists (61%), formulating personalised treatment plans (61%) and delivering empathic care (94%). On the other hand, 80% believed it likely that future technology will be able to fully replace humans in undertaking documentation.

†† The other three are enhanced diagnostic decision making; delivery of remote care and self-management tools; and seamless sharing of patient information between care providers.

‡‡ UK public survey fieldwork done online by YouGov, 26–28 October 2020; total sample size 4,326 adults (85% from England, 8% Scotland, 5% Wales and 3% Northern Ireland); figures have been weighted and are representative of all UK adults (aged 18+). NHS staff survey fieldwork done online by YouGov 23 October–1 November 2020; total sample size 1,413 adults (80% from England, 13% Scotland, 6% Wales and 1% Northern Ireland); sample comprised the main occupational groups within the NHS’s clinical workforce (allied health professionals; medical and dental; ambulance; public health; nurses and midwives; nursing or health care assistants).

§§ Specifically, respondents were given the following information: ‘Automation is when computers and robots are used to do tasks that humans have traditionally done. In health care, examples of automation include using a machine to monitor a patient’s heart rate or using a robot to dispense medicines in a pharmacy. Artificial intelligence (AI) is when computers are able to copy aspects of human intelligence like learning and problem solving. In health care, examples of AI include using computers to predict which patients are more at risk of falling ill, or to analyse X-ray images in order to spot illness or injury.’

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