Digital technology is ubiquitous within almost everyone’s life. The COVID-19 pandemic hastened the switch to a routine use of online services; this is evident by the seismic shift in finance, grocery and other retail shopping, with data having been used to redesign banking and other services so that they are more convenient and allow self-service. Within healthcare, digital health is viewed as having the potential to transform the healthcare system through supporting integrated care systems (ICSs), thereby helping to deliver better citizen and patient health outcomes, while simultaneously enabling new ways of working to better manage the high levels of healthcare demand.
The King’s Fund (2021) reviewed data on how emerging technologies such as artificial intelligence (AI), smartphones, wearable devices and the internet of things are being used within care settings around the world, and found variable levels of evidence underpinning their use. They highlighted the need for further research regarding all emerging technologies and their impact on the healthcare system, costeffectiveness, reliability, security, acceptability and privacy. Currently, there is a lack of clarity in the UK regarding the rules and governance relating to personal health and care data, the regulation for digital health technologies (NHS Digital Technology and Assessment Criteria (DTAC), the National Institute for Health and Care Excellence (NICE) evidence standards framework for digital health technologies), and new device regulations, such as those relating to software as a medical device (Medicines and Healthcare products Regulatory Agency (MHRA)) and the responsibilities of developers and providers across different parts of the development, deployment and monitoring pathway of new technologies. There will be a need to maintain the right balance of regulation without inhibiting innovation.
The King’s Fund (2021) outlined three potential scenarios derived from conditions relating to trust in the use of data, and the capacity within health and social care to use digital technologies effectively:
- A health ‘techlash’ characterised by decline in public trust (patients and staff), and a lack of capability to take advantage of digital technology
- A willing system with uneven spread characterised by public trust is retained or reinforced through a trustworthy system of data governance, but the capacity to take advantage of emerging digital technologies is not spread widely throughout all health and care providers, nor evenly through the population at large (resembles current state)
- Spread and scale at pace characterised by high trust among the public and professionals with rare data breaches dealt with promptly, and major investment in digital technologies and staff capabilities to use the technologies throughout health and social care.
During the COVID-19 pandemic digital health became the default approach with increasing reliance upon digital tools, which changed how services were delivered and how people interacted with services (The King’s Fund, 2021). The ways in which data are used and shared have also changed. An independent report (Department of Health and Social Care (DHSC), 2021) has claimed that some of the success of the COVID-19 vaccination programme was due to the advanced data analytics underpinning the vaccine rollout and that 96 000 people were monitored at home or in a care home, saving the use of hospital beds during the pandemic. There is the expectation that electronic systems and decision support software will become the norm within all NHS England healthcare (NHS England, 2022). However, the King’s Fund (2021) offered a note of caution that there are many reasons why people may be digitally excluded, requiring all those involved in healthcare to create services that are accessible to the less digitally able and willing.
Digital healthcare
Much of the research and efforts have been focused on improving the efficiency and quality of hospital care through data analytics for clinical care and administration. NHE Vocera (2022) has made the case for the ‘smart’ hospital—a ‘digital’ hospital that uses communication to spread access to knowledge and information so that they are delivered to those who can act, enabling swift and efficient decisionmaking. This has huge potential benefits to patients because delays in care are avoided, and to the healthcare system because timely treatments minimise potential health deteriorations and additional healthcare costs. The importance of communication is no less important outside the hospital setting, that it is within.
The NHS Confederation (2021) has argued that community health services are well suited to maximise the opportunities afforded by digital health because the mobile workforce is accustomed to working remotely and flexibly to deliver care. While some community providers have achieved significant digital transformation, it is acknowledged that not all community providers have reached the same level of digital maturity, and in places, many of the digital tools simply replicate physical approaches and processes rather than real digital whole-system transformation (The King’s Fund, 2020). Nonetheless, community providers are developing their capacities to deliver remote monitoring and virtual wards, virtual consultations, and self-management tools as part of their offering to achieve the NHS Long Term Plan (NHS England, 2019).
Remote monitoring and virtual wards
The ageing population, together with the increasing number of people with long-term conditions who require timely medication management and more personalised therapeutic care, are creating an unsustainable demand upon community health services, general practitioner (GP) services and hospitals, including out-patient services. Remote monitoring and consultations have the potential to help manage this health need demand with timely interventions without in-person contacts. NHSX (2021) reported how all seven regions of NHS England had used remote monitoring to enable healthcare at home for over 78 000 people with long-term conditions and for acute COVID-19, between November 2020 — May 2021.
Additional examples of the benefits of remote monitoring are cited by Leicestershire Partnership NHS Trust with their support of people with chronic obstructive pulmonary disease (COPD) or heart failure (Davis-Cooper, 2020) and the South East London Integrated Care System rheumatology services (Martin, 2020). Nuffield Trust (2021) has described the implementation of vital sign measurement using digital technologies within domiciliary care as part of the Care City initiative in East London to generate a National Early Warning Score (NEWS) to identify health deterioration and clinical risk. Unsurprisingly, this evaluation yielded a number of lessons for the successful implementation of digital technologies in the community.
Virtual wards were trialled during the early 2000s to complement Hospital at Home and other enhanced care schemes in an attempt to better manage healthcare costs through less hospital usage (Jones and Carroll, 2014). The Wyre Forrest model selected potential patients using software to calculate hospitalisation risk through an analysis of hospital, primary care and social care data and reported a 10% reduction in emergency admissions (Jones and Carroll, 2014). NHS England (2021) has reported that 86% of integrated care systems have implemented technologyenabled virtual wards using digital platforms or were intending to do so, in many cases, to better manage COVID-19 care demands.
Table 1. Key requirements of trustworthy artificial intelligence (AI)
| Area | Requirements | Roles |
|---|---|---|
| Human agency and oversight |
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| Technical robustness and safety |
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| Privacy and data governance |
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Compliance with DPA must be ensured |
| Transparency |
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| Diversity, nondiscrimination, and fairness |
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| Environmental and societal well-being |
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| Accountability |
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The system should be auditable by independent third parties |
Source: European Commission (2019)
Health Innovation Network’s (HIN) (2021) rapid evaluation of Croydon Virtual Ward reported an estimated cost saving per virtual ward patient (n=250) of £742.44 (comprising telephone contact rather than home visits (£522.12)) and bed days avoided (£220.32) compared to the rapid responses control group (n=33). The success of Croydon Virtual Ward (maximum of 30 patients at any time) was attributed to being run by community services with pathways to emergency treatment when needed, upskilled staff to manage continuous monitoring and knowing when to use continuous rather than spot monitoring, and having access to a cross-system multi-disciplinary team. Patient satisfaction was generally very high, including the relative ease of using the telehealth kit and improved quality of life. Remarkably, the telehealth monitoring detected significant pathology in five patients earlier or would have not been detected with possible fatal outcomes without treatment. Boniface et al (2021) have also reported the positive outcomes of remote monitor ing of oxygen saturation (SpO2) in patients of 15 North Hampshire GP practices to detect early deterioration associated with COVID-19.
Artificial intelligence (AI)
AI-based software is ubiquitous in our lives and allows smartphones to be unlocked. It mimics human intelligence by analysing masses of data and pattern matching. In the 1980s, a new form of AI called ‘expert system’ emerged, which described a programme that solves problems about a specific domain of knowledge using logical rules that are derived from the knowledge of experts. In the 1990s, AI models began to resemble the way in which humans learn, sometimes referred to as connectionist models with decision trees, k-means algorithms (a set of instructions that have to be followed) and artificial neural networks (multiple input values generating one output value), using a data-driven approach. Instead of coding the machine by hand, the developer ‘trained’ the machine on examples or experiences so it could learn to make inferences on new data (machine learning). In 2010, a new sub-field of machine learning called deep learning emerged, with deep learning models always comprising multi-layered neural networks. Bajwa et al (2021) have argued that AI could transform the delivery of healthcare.
Future Advocacy (2018) identified five areas for AI use: preclinical research (e.g. drug development), clinical pathways (diagnostics), operational efficiencies (referred to as ‘process optimisation’), patient-facing applications (delivery of therapies or information) and population-level applications (identifying epidemics or understanding longterm conditions). Most research has focused on algorithms that perform useful functions in tasks in clinical pathways but some have compared algorithm performance to human performance. Liu et al’s (2019) systematic review of studies comparing algorithm image analysis performance against healthcare professionals across a number of diseases (including eye disease, breast cancer, trauma and orthopaedics, dermatological cancer, lung cancer, and respiratory disease) found that their accuracy was similar. AI also appears particularly useful in the analysis of structured and unstructured (such as free text) data in electronic health records (EHRs) (Christodoulou et al, 2019).
AI is used within community nursing service management, for example, in scheduling client visits, visit route planning, and in the allocation of resources within constraints. AI-based techniques can also be used to improve public health surveillance (including prediction-based surveillance) as well as the identification of disease outbreaks and response. The ability of AI to analyse large amounts of complex patient health data (including lifestyle and environment), together with genetic information can help healthcare professionals deliver clinical care and personalised approaches to diagnosis and treatment. AI can develop disease riskscores prediction models and pharmacogenetic applications with the aim of providing the best suited medication based on the individual’s genetic information on how they will respond to it. But every AI system is only as good as the data it is trained on, including its biases, and recalibration is needed in each ‘new’ setting, with the new algorithms validated against completely new datasets reflecting the ‘new’ setting. Importantly, AI cannot substitute for professional communication; a healthcare professional is able to interpret diagnostic tests and convey them to a patient in a trustworthy and understandable manner within a supportive relationship.
AI tools have the potential to support people to selfmanage long-term conditions such as cardiovascular diseases, diabetes or mental health problems by using conversation agents, known as ‘chatbots’, health monitoring and risk prediction tools. Wearable devices, including insulin pump patches, activity trackers and smart watches capture large amounts of data enabling the use of AI to monitor a person’s health, predict health risks in a more prompt and efficient manner, and take remedial actions such as notifying a healthcare provider of any emerging concern.
Trustworthiness is a significant challenge facing AI because of its dependency on diverse, high quality data for the development of the model. Poor data (underrepresented population groups; e.g. age, women, ethnicity, etc) or inaccurately labelled data can skew the model and result in wrong predictions. The European Commission (2019) noted that a trustworthy AI system needs to lawful, ethical and robust and set out seven key requirements for AI systems to be considered trustworthy (Table 1 ). Trustworthiness depends upon technical robustness and safety, with the avoidance of unfair bias and attention to diversity, non-discrimination, and fairness. These same criteria are echoed in the WHO (2021) guidance for AI in healthcare.
While some people do not seem to have any concerns with the use of AI in healthcare, others may be content with the use of AI for their diagnosis and treatment if a healthcare professional still has the final control. But many people have concerns, the most common being the lack of human interaction, the effects of bias, and the idea that the system cannot be trained sufficiently to be able to perform as well as a healthcare professional. An online survey of a small unrepresentative sample (n=98) of staff (including doctors, nurses and therapists) from an acute NHS Trust found that there was limited understanding of AI, with 64% reporting that they had not come across applications of AI in their work (Castagno and Khalifa, 2020). While recognising that AI could be useful in their field of work, 80% believed that there were major privacy issues with AI. Some 10% were worried that AI could replace their role. This small survey highlights the need for the education of healthcare staff and their involvement in the development of AI in their field of clinical practice.
Attitudes towards health technologies
The Queen’s Nursing Institute (QNI) (2018) survey described the different ways that community nurses used IT to support service delivery, including diagnostic tools, data collection, electronic prescribing, updating client records and making referrals. Some respondents reported a lack of confidence in the use of devices, connectivity issues, and device use as a barrier to person-centred care to explain a reluctance in device use. Williams (2022) argued that the digital era and accompanying innovations are inevitable, but she expressed sympathy with hesitant practitioners who need time to adjust with clear supportive leadership as well as reliable digital devices with good connectivity.
Collins (2020) noted that the successful implementation of a digital innovation can be thwarted by how long it takes for healthcare staff or patients to log on to the system, how difficult it is to correct a small inputting error, or how long it takes to get through to the call centre if a password is forgotten. In Australia, Kuek and Hakkennes (2020) reported that, even when there were high digital literacy levels accompanied by high frequency of use and confidence about interactions with various devices/systems, 20% of their sample expressed anxiety about using information systems, with clinical staff and those over 50 years more likely to express this anxiety. EHRs are now integral to community nursing; however, it seems that many community nurses return to their work bases to access EHRs rather than using them as a mobile resource (Lezard and Deave, 2021). Lezard and Deave’s (2021) focus group study revealed contrasting views about the practicality and time wasted/saved associated with EHRs, but there was general acknowledgement that EHRs and technology enable safe and effective care. Team leadership and team ethos were key to the remote use of EHRs highlighting the importance of staff engagement when introducing new ways of working and also ensuring that staff feel well supported with no loss of collegiate team working.
Hopkins et al’s (2022) small qualitative interview of district nursing clients (n=9; aged 65—99 years) revealed that most (n=6) did not use IT in their personal lives but were all aware of their friends and family using it. All participants had encountered IT use during their healthcare experiences, and had a basic understanding of EHRs. All participants were positive about nurses using tablet devices in their homes citing efficiency, accessibility and accuracy of records. Interestingly, the participants viewed the healthcare professionals as the main beneficiaries and believed that the change would not affect them significantly. Remarkably, while all the participants understood their care records, they had mixed feelings about no longer being able to read their paper records. Most of them felt that they had not been informed about the change to EHRs, although this was not an issue. Thus, it seems IT usage is expected within healthcare, including within the home, but it will be important to ensure that its increasing use does not impact upon nurse-client relationships.
Digital health inequalities
Healthcare in the UK should be equally available to all on the basis of need and yet, there is ample evidence that both health inequalities and health access inequalities exist (Centre for Ageing Better, 2021; Office for National Statistics, 2021). The NHS Long Term Plan (NHS England, 2019) seeks to improve healthcare provision by increasing the use of digital health tools, services and patient choice, but there is the danger these developments will further exclude those without access to digital technology and its use, thus further exacerbating health inequalities (Patients’ Association, 2022). Some 10% of the UK adult population are internet non-users and up to 42% of the population struggle to understand and use typical medical information. Furthermore, not all NHS websites are translated into other languages. Thus, people may be excluded from receiving information and acting on it, while medical appointments are increasingly booked online, with remote GPs and hospital consultations being commonplace. People are excluded for a range of reasons and therefore, different approaches are needed depending on their particular needs as is evidenced by the case studies in the Patient Coalition report (Patients’ Association, 2022). The Patient Coalition report recommended patient engagement to understand their particular needs, the provision of choice, and simplifying the language used in all correspondence, as well as including other languages on webpages.
Opportunities and ongoing challenges
The widespread use of digital technologies offers additional opportunities for community networking and health creation, although bridging of the digital divide will not be overcome easily and without considerable effort. But there is growing evidence that a large proportion of the population is able to manage digital technologies, especially if their engagement does not require advanced skills (Patients’ Association, 2022). Major investment in digital technologies and staff development is also required at the same time as the ‘public purse’ is already stretched due to the COVID-19 expenditure. Additionally, there needs to be ongoing financial provision for software updates and their rollout if digital health is to be a major component of the modern NHS. While remote monitoring and timely access to prompt treatment may reduce hospital usage and perhaps acute illness duration (Boniface et al, 2021; Health Innovation Network, 2021), only the most optimistic observer would anticipate that there will be a major reduction in healthcare need because recovery from an episode of ill health and long-term conditions are underpinned by physiological processes. Additionally, Bailey (2022) has highlighted the challenge of local NHS Trust policies keeping up with changing NICE (2022a; 2022b) guidance, as exemplified by the use of flash glucose sensors for blood glucose monitoring.
In the current troubled world, an additional consideration is the mitigation of cyber risk, especially with the increasing use of networked digital infrastructures in healthcare (Wells, 2022). Continued investment in hardware and software will be required to reduce technical vulnerabilities. However, users, namely, practitioners, demonstrate an ever-present vulnerability, especially if they feel overworked and are tired, and further, have been targeted through their social media profiles (Wells, 2022). System safety may well be dependent upon how nurses respond to a cyberattack; a high awareness of cybersecurity will be as important as other potential sources of a critical incident such as a fire.
Key points
- Digital health is becoming ubiquitous in post-COVID-19 healthcare
- Digital health has huge potential to enable healthcare
- But attitudes vary both across healthcare professional and patients
- There is a danger that digital health may open up another area of inequality unless inclusion and choice are a focus of healthcare delivery
CPD reflective questions
- What tools of digital health are part of your working life?
- How has digital health changed the way you practice over the last 3 years?
- How can your clients be better supported through the technological changes within healthcare and their experiences?
Conclusion
Digital health is transforming NHS services and has the potential to bridge some of the supply-demand gap created by a growing population of older people and those with long-term conditions. However, some community nurses and other healthcare professionals remain sceptical about its benefits, in part because they do not fully understand how it can enable their nursing care and have not felt sufficiently supported during its adoption. The ongoing challenge for service providers and community nurse leaders is meeting the education and support needs of their staff so that they feel fully engaged in the ‘digital health journey’ of the NHS.