Data-intensive nursing: developing trust, connecting care and involving patients, families and carers
A new role is emerging for nurses as digital advocates for patients, say Mary Tully and Iain Buchan
A new role is emerging for nurses as digital advocates for patients, say Mary Tully and Iain Buchan
The healthcare sector cannot just continue to do ‘business as usual’. Demands for care are escalating beyond resources in most communities, in which increasing numbers of people are living longer with multiple long-term conditions.
Therefore, healthcare is an important target for digital transformation, a term used by industry to describe the reconfiguration of services through data and digital technologies. It is doing business differently rather than simply computerising old ways of working.
Every week, technology companies announce more initiatives to assist patients and healthcare professionals by using artificial intelligence (AI) to turn ubiquitous data into actionable information. This is promising but challenging, not least in terms of earning the trust of patients and the public that personal data will be used properly.
Nursing management is central to the digital transformation of healthcare systems and this article suggests ways it can ‘rewire’ place-based healthcare systems for the better, and provides practical advice on how to talk to patients about data use and linkage.
New advocacy role for nurses
We live in an increasingly connected word where digital technologies link us to services and to one another, and where AI predicts what we might like or need (Brown et al 2017).
For tech-savvy individuals, online interaction is the norm. For people with low levels of active digital participation, passively collected data, such as the position and motion of a mobile phone, might signal healthcare needs.
Furthermore, the families, carers and informal care networks of most patients have access to online services that could augment the NHS. For example, an app to support leg ulcer management could be used to take regular photographs that are uploaded to AI services that track the ulcer, ask questions, guide self-management, trigger nurse visits and support stock control and delivery of dressings. It might be the patient who uses the app, or it might be a neighbour who does it as part of an informal care role.
Nurses will face new demands to act as digital advocates for patients, introducing ways to augment face-to-face NHS care with online services and connected devices. In doing so, nurses will need to explain issues of data privacy, consent and how to get the best out of digital services.
Data ownership, privacy, consent and expectations
Nurses are often the first healthcare professionals to interact with patients and their carers, and they interface frequently with other care professionals when managing the challenges of coordinating multiple services around patients’ needs. As such, nurses are best placed to discuss health record data with patients, especially regarding concerns about privacy, confidentiality and data sharing.
Data sharing is the process of allowing access to someone other than the person who created the data. There are two main types of data that patients may want to talk about, although some patients do not understand the differences between them. In many ways, ensuring that all patients understand how the NHS shares data is the initial step in this conversation.
The first type of sharing data is with other professionals for the ongoing care of individual patients: for example, when they are referred by a GP to a district nurse. Understandably, patients’ data would need to be personally identifiable and this requires consent, which in practice is usually implied as part of patients’ willingness to accept the referral.
The second type is about improving services through audit or research, usually by analysing information from multiple patients after their names, addresses and other identifiers have been removed.
In the UK, there is no lawful impediment to using such depersonalised data without the explicit consent of patients as long as the risks of reidentification are low. However, if data for secondary uses are personally identifiable, either patients must give consent for that data use or the data analyst must have approval from, for example, the Confidentiality Advisory Group in England and Wales.
Public opinion about NHS data sharing and use
Many people do not understand how data is shared and used in the NHS (Aitken et al 2016, Wellcome Trust 2016) and there may be confusion when data sharing initiatives are introduced when patients have assumed such information was already shared.
Therefore, a ‘no surprises’ approach to data and information governance requires raising awareness about what is and what is not done with data. For example, if patients are asked explicitly if they are willing for a hospital to see their GP records, they are often surprised to learn that this does not already happen.
Repeating the same information to different healthcare professionals is frustrating for patients, so most are willing to accept data sharing about their care. Nonetheless, a small percentage are passionate about their privacy and refuse to allow data sharing even in these circumstances. Although there is a lack of research about such patients, many people change their minds in general focus groups and ‘citizens’ juries’, and become more accepting of data sharing when they are more informed about data use (Health e-Research Centre 2016, Wellcome Trust 2016).
Important aspects of public opinion about health data sharing concern the intended uses of the data, the organisations responsible, the sensitivity of the information and the governance arrangements (Aitken et al 2016, Health e-Research Centre 2016, van Staa et al 2016).
There is generally support for data use when the public benefit case is strong, even when commercial organisations are processing the information (Wellcome Trust 2016), and public support for use of patient data to improve NHS services is very strong. Some people even believe that NHS records are a public resource paid for by public money and so should be used for the public benefit (Miller 2008).
However, public opinion about the need to seek consent for specific data uses varies, from trusting authorities such as ethics committees to act in the best interests of patients (Damschroder et al 2007) to wanting more control (Aitken et al 2016).
Data sharing and local community interests
The care.data debacle demonstrated how public opinion of NHS data sharing can be soured by poor quality information campaigns that fail to explain the public benefit case in ways that generate trust (van Staa et al 2016).
Trust is central to whether patients are willing to share their information and lack of trust is not merely about lack of knowledge. It is not enough, therefore, to expect trust to be generated by merely raising awareness and understanding.
The critical mass of support for data use in a community is also important, as are wider issues around ‘social licence’ (Carter et al 2015).
Giving something back to people and communities – reciprocity – is vital for earning and maintaining trust in data uses. This can range from feedback on how data have been used, to providing information that is personally actionable, such as booking your primary care appointment through your NHS app just after interacting with a cardiovascular risk assessment tool while ordering your repeat prescription for antihypertensive medication.
Nursing, above all other healthcare professions, understands that crude AI applied to patient data does not reflect the real-world complexity of healthcare. For example, the barriers to coordinating community-based care can be overcome only with local knowledge that does not sit in national databases. The Greater Manchester health system, for example, serves 2.8 million people and draws on more than 10,000 databases across the NHS and social care.
To deliver the public benefit of health systems that ‘learn’ and improve continuously from data (Ainsworth and Buchan 2015), public trust for the necessarily in-depth data uses needs to be local. The benefit compared to risk consideration of using people’s data to improve local services is likely to be more compelling than for developing less familiar services.
Small healthcare systems cannot afford the infrastructure to drive digital transformation, so there is a trade-off between regional coverage that is big enough to offer economies of scale in digital infrastructure but small enough to involve local communities sufficiently to generate the trust in data use required for digital transformation.
We call this optimal regional coverage the ‘diameter of trust’ and estimate it equates to healthcare systems serving populations of between two and five million. More detailed work is needed to define the diameters of trust for optimal data sharing in the UK, including mapping population densities, NHS referral patterns, travel distances and concentrations of social capital.
Conversations about data use
Conversations with people about the use of their data are crucial for creating an environment of trust.
Yet the language used in informatics can create barriers and prevent patients getting to grips with what is happening to their data. What are ‘pseudonymised data’ or ‘de-identified data for limited access’, for example, and are they different?
The Wellcome Trust (2017) has launched an initiative, Understanding Patient Data, to find out and recommend the best terms for healthcare professionals to use when describing data use to patients and the public, and its website provides resources to help start and maintain those conversations.
For example, a vital topic of conversation could be whether individuals are identifiable from the data. The term ‘anonymous data’ should be used only to describe information when it is not possible to link it to an individual, usually because data are presented as summary statistics, while ‘personally identifiable data’ should be used to describe data referring to a specific person.
It is the area in between these extremes where people become confused. The recommended term is ‘depersonalised’ data, which people understand to mean that something has been done to remove identifiers but, equally importantly, it recognises that it is possible to reverse that process. This honesty about the nature of the information and how it is protected helps to engender trust.
Digital transformation in healthcare is happening more slowly than in other sectors but it is inevitable, and nurses must be centre stage. A new role is emerging for nurses as patients’ digital advocates, at the front line of explaining data uses and protection, informing consent and supporting digital self-care skills.
At the population level, nursing management has an important role in generating the trust in data use needed to ‘rewire’ the coordination of care services through connected patients and practitioners. We consider that specialists in nursing informatics are core to realising learning health systems.
Ainsworth J, Buchan I (2015) Combining health data uses to ignite health system learning. Methods of Information in Medicine. 54, 6, 479-487.
Aitken M, de St Jorre J, Pagliari C et al (2016) Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies. BMC Medical Ethics. 17, 1, 73.
Brown B, Smeeth L, van Staa T et al (2017) Better care through better use of data in GP-patient partnerships. British Journal of General Practice. 67, 655, 54-55.
Carter P, Lauries G, Dixon-Woods M (2015) The social licence for research: why care.data ran into trouble. Journal of Medical Ethics. 41, 404-409.
Damschroder L, Pritts J, Neblo M et al (2007) Patients, privacy and trust: patients' willingness to allow researchers to access their medical records. Social Science and Medicine. 64, 1, 223-235.
Miller F (2008) Research on medical records without informed consent. Journal of Law Medicine and Ethics. 36, 3, 560-566.
van Staa T, Goldacre B, Buchan I et al (2016) Big health data: the need to earn public trust. British Medical Journal. 354, 3636.
To access the RCNi collection of selected resources on digital literacy, visit here.
Mary Tully is a reader in pharmacy practice and Iain Buchan is a professor of public health informatics at University of Manchester