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Artificial intelligence is already used in healthcare; this first article in a three-part series on digital healthcare looks at the benefits and risks

Artificial intelligence is already being used to support advanced clinical decisions, improve the accuracy and safety of care, and plan and manage NHS resources. It can make machines do things that used to require human intelligence, and can draw on huge amounts of data to make calculations that are beyond any human being. Artificial intelligence-enabled robots are being developed to take on some nursing functions. Nurses need to examine how their own roles may be changed and advocate for patient involvement in light of emerging technologies. They will also need training and support to feel confident using artificial intelligence tools. This first article in a series on digital healthcare examines the benefits and risks of artificial intelligence.

Citation: Agnew T (2022) Digital nursing 1: exploring the benefits and risks of artificial intelligence. Nursing Times [online]; 118: 8.

Author: Thelma Agnew is a freelance health journalist.

The ministerial forward to Joshi and Morleys (2019) report, published by NHSX, provided one key reason to be excited about artificial intelligence (AI) in healthcare: put simply, this technology can make the NHS even better at what it does: treating and caring for people. AI is exciting, but what is it, exactly? This first in a series of articles about digital healthcare will discuss the benefits and risks of AI.

Increasingly, nurses are encouraged to lead and shape emerging digital technologies, to ensure the changes made are fit for purpose and ward against unintended consequences, such as increased workloads, dehumanised care and the exclusion of already marginalised groups of people. The areas that could be improved, or even transformed by AI, according to Joshi and Morleys (2019) report, include:

Despite this, it is difficult to approach, let alone lead on, technological advancements if nurses do not understand them, and AI may feel too big to grasp at times.

There are many definitions in the ever-growing literature released about AI. Joshi and Morleys (2019) report suggested that one of the most useful definitions in the field of healthcare is also the oldest; they explained that it dates from a research project in 1955 and stated that AI is the science of making machines do things that would require intelligence if done by people.

A publication by The Kings Fund namely, Mistry (2020) gave a more detailed, but still straightforward explanation, stating that AI is an umbrella term encompassing a number of different approaches where software replicates functions that have, until recently, been synonymous with human intelligence. This includes a wide spectrum of abilities such as visually identifying and classifying objects [and] converting speech to text and text to speech.

The origins of AI go back decades, so why are we hearing so much about it now? One reason is that recent developments in applied mathematics and computer science have made computers much better at reading patterns in large amounts of complex data, releasing AIs potential (Mistry, 2020). The possibilities of AI are being further expanded by machine learning, which has been defined by Mistry (2020) as a type of artificial intelligence that enables computers to learn without being explicitly programmed, meaning they can teach themselves to change when exposed to new data.

Most health staff still lack direct experience with AI technologies, as is highlighted in Nix et als (2022) report, developed by NHS AI Lab and Health Education England (Box 1). However, the increasing use of AI technologies in nursing, such as providing information for advanced clinical decision support, is thought to be inevitable (Booth et al, 2021; Robert, 2019).

Box 1. AI technologies: a strange science is about to become more familiar

A survey of >1,000 NHS staff in the UK by The Health Foundation namely, Hardie et al (2021) found that three-quarters of respondents had heard, seen or read not very much or nothing at all about AI. The survey also found:

The Health Foundation survey identified fears among health workers that AI technologies present a threat to their jobs. This has been echoed in several other studies, along with concerns about data governance, cyber security, patient safety and fairness (Nix et al, 2022).

The reservations about AI are unlikely to put the brakes on their adoption in healthcare. Nix et als (2022) report points to evidence that use of AI is accelerating, with an increasing number of AI technologies expected to be used in healthcare in the next three years. It highlights the AI roadmap report by Health Education England and Unity Insights (2021), which surveyed more than 200 AI technologies: 20% were estimated to be ready for large-scale deployment in 2022, with an additional 40% ready in the next three years.

AI = artificial intelligence

AI-enabled decision support systems potentially provide numerous benefits; for example, they have already dramatically improved the detection of sepsis (Horng et al, 2017). However, there are also risks, as AI is only as good as its data. Nix et als (2022) report warns that confidence in artificial intelligence (AI) is not always desirable when using it for clinical decision making, and nurses need to recognise when to balance it with other sources of clinical information (Box 2).

Box 2. Confidence in using AI for clinical decision making

A recent report from NHS AI Lab and Health Education England recommends:

The main recommendation from the report is to develop educational pathways and materials for all health professionals to equip them to confidently evaluate, adopt and use AI (Nix et al, 2022).

AI = artificial intelligence

AI systems that evolve themselves may reflect or reinforce societal biases (for example, racial biases) and other inequities present in the data (Obermeyer et al, 2019; Gianfrancesco et al, 2018). It is important for nurses to be involved in innovations such as AI to make sure they are developing systems in line with ethical frameworks and to advocate for patient involvement (Booth et al, 2021). There is also a risk that AI systems that perform extremely well in controlled conditions will be less impressive in the real world, and there are unanswered questions about their safety and cost effectiveness in healthcare settings (Maguire et al, 2021).

The NHS is already using AI and machine learning, at a population level, to help identify older people in local areas who are at risk of frailty and adverse health outcomes; one example of this is the Electronic Frailty Index, which draws on data that is routinely recorded by GP practices (NHS England, 2017).

Predictive analytics in electronic patient records should also, increasingly, help doctors and nurses to diagnose and treat the individual patient in front of them (HEE, 2019). AI has also played a key role in informing the governments response to the coronavirus pandemic: the launch of the NHS Covid-19 Data Store by NHSX has aided the analysis of vast amounts of data to:

AI is also central to the governments new digital health and social care plan (Department of Health and Social Care and NHS England, 2022). This includes using AI to develop new diagnostics capacity to enable image-sharing and clinical decision support... These technologies support testing close to home, streamlining of pathways, triaging of waiting lists, faster diagnosis and levelling up under-served areas

With the development of smaller and more-sophisticated electronic components, robots embedded with AI will likely become more widely used in healthcare. (Mistry, 2020; HEE, 2019). The highly influential Topol review predicted that robots would become the hardware for AI, performing manual and cognitive tasks, and freeing up healthcare staff to spend more time doing things that are uniquely human, such as interacting with patients (HEE, 2019).

This picture is complicated by the fact that robots have also been developed to provide social and emotional support to people, arguably blurring the line between machines and humans. Examples currently in use include:

A common theme in the literature on AI and robotics in healthcare is the expectation that patients, as well as staff, will receive multiple benefits from the introduction of intelligent machines, with improvements in early diagnosis, and the accuracy and safety of care (Mistry, 2020; HEE, 2019). Unlike human healthcare workers, robots never get bored or tired, are unaffected by hazards in clinical settings, such as X-ray radiation, and can endlessly repeat tasks that require precision without a drop in performance (Mistry, 2020). The potential is there for robotics to help with everything from moving patients to surgical procedures that are beyond the capabilities of surgeons (Mistry, 2020).

With the input of nurses, robots are also being developed to take on some nursing functions, including:

Developments do not mean that nurses are about to be replaced by intelligent machines, but they do suggest that nursing, as it is currently understood, will change. A 2019 study by former American Nurses Association executive vice president Nancy Robert suggested that the arrival of telehealth and smart robots in peoples homes will see nursing evolve into more of a coaching role, guiding patients to improve their health and providing continuity of care, but still being physically present at the bedside when it really matters (Robert, 2019). A nursing dean quoted in the study said they could not imagine ever choosing a robot over a human to care for them if they were dying, and stated that: Nuances in human behaviour will keep nurses on the front line of care (Robert, 2019).

It is hoped that AI and robotics will work together as assistants to nurses by, on the one hand, supporting advanced clinical decisions and, on the other, automating basic tasks that are time consuming but could be performed by someone or something else. In this vision of an AI-enabled future, machines free up nurses professionally to use their education, skills and experience (Robert, 2019). A barrier to nurses using the technology to fulfil their potential could be other healthcare disciplines resistance to nurses practising at the top of their licence (Robert, 2019).

There is also the risk that, as AI tools become more widely used in healthcare, they will influence how nurses practise, without nurses having the opportunity to influence them. A 2021 study on how the nursing profession should adapt for a digital future called for an immediate inquiry into the influence of AI on nursing practice for the next 10 years and beyond (Booth et al, 2021). The authors pointed out that the increased use of AI is bringing with it new policy, regulatory, legal and ethical issues; they called on the nursing profession to:

Robert (2019) suggests that nurses have a responsibility to ask about the data used to train AI systems they use, and ensure they have been checked for bias.

As outlined in Box 3, nurses will need training and support to feel confident in, and overcome the barriers to, using AI tools which will only work properly if the ageing technology infrastructure of the NHS improves (Joshi and Morley, 2019). The excitement about AI is justified but it is important not to get dazzled by the hype (Joshi and Morley, 2019).

Box 3. AI and robotics in healthcare: barriers and learning

Health Education Englands (2019) Topol review identified significant barriers to the deployment of AI and robotics in the NHS. These included:

Along with a code of conduct and guidance on the effectiveness of the technologies, the review called for workforce learning in three key areas:

AI = artificial intelligence.

Booth RG et al (2021) How the nursing profession should adapt for a digital future. BMJ; 373: n1190.

Department of Health and Social Care, NHS England (2022) A plan for digital health and social care. gov.uk. 29 June (accessed 29 June 2022).

Gianfrancesco MA et al (2018) Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine; 178: 11, 1544-1547.

Gould M et al (2020) The power of data in a pandemic. digileaders.com, 15 April (accessed 21 June 2022).

Hardie T et al (2021) Switched On: How Do We Get the Best out of Automation and AI in Health Care? The Health Foundation.

Health Education England and Unity Insights (2021) AI Roadmap: Methodology and Findings Report. HEE

Health Education England (2019) The Topol Review: Preparing the Healthcare Workforce to Deliver the Digital Future. HEE.

Horng S et al (2017) Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE; 12: 4, e0174708.

Joshi I, Morley J (2019) Artificial Intelligence: How to Get it Right Putting Policy into Practice for Safe Data-driven Innovation in Health and Care. NHSX.

Maguire D et al (2021) Shaping the Future of Digital Technology in Health and Social Care. The Kings Fund.

Mistry P (2020) The digital revolution: eight technologies that will change health and care. kingsfund.org.uk, 13 November (accessed 21 June).

NHS England (2017) Supporting Routine Frailty Identification and Frailty Through the GP Contract 2017/2018. NHS England.

Nix M et al (2022) Understanding Healthcare Workers Confidence in AI. NHS.

Obermeyer Z et al (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science; 366: 6464, 447-453.

Robert N (2019) How artificial intelligence is changing nursing. Nursing Management; 50:9, 30-39.

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