Public opinion on sharing data from health services for clinical and research purposes without explicit consent: an anonymous online survey in the UK

#BeSeen: understanding young people's views of the motivation and impacts of sharing self-harm imagery online and use of their social media data for research-a UK participatory arts-led qualitative study

OBJECTIVES

This study explored the views of young people from diverse backgrounds, with or without a history of self-harm, on the motivation and impacts of sharing self-harm imagery online and the use of their social media data for mental health research.

DESIGN

Thematic analysis of 27 semi-structured one-to-one interviews.

SETTING

Two workshops were conducted in 2021.

PARTICIPANTS

We recruited 27 study participants aged 16-24 (60% male). Sixteen (59%) participants were refugee and asylum seekers (RAS).

RESULTS

Two main themes were generated: (1) Online imagery of self-harm captured perceived motivations for sharing such images, the potential impacts on others and possible need of censorship. This theme was characterised by mixed attitudes towards motivations for sharing, with some perceiving this as attention seeking, while others thought of it as help seeking or sharing of pain. Overall, participants agreed that images of self-harm can be triggering and should include trigger warnings. (2) Data sharing for mental health and self-harm research captured views on the use of social media posts and images for research purposes, and levels of trust in public and private organisations. It outlined positive views on their data being shared for research for public benefit, but highlighted issues of consent. The two most trusted organisations to hold and conduct research were the National Health Service and Universities. Participants from the RAS group were more inclined to agree to their data being used and had higher levels of trust in government.

CONCLUSION

Young people care about their privacy and use of their data even when it is publicly available. Coproduction with young people of resources to support understanding and develop innovative solutions to gaining informed consent for data sharing and research for public benefit is required. Young people from excluded communities, post-immigration RAS and males should be purposively involved in future social media research.

NHS national data opt-outs: trends and potential consequences for health data research

BACKGROUND

The English NHS data opt-out allows people to prevent use of their health data for purposes other than direct care. In 2021, the number of opt-outs increased in response to government-led proposals to create a centralised pseudonymised primary care record database.

AIM

To describe the potential impact of NHS national data opt-outs in 2021 on health data research.

DESIGN & SETTING

We conducted a descriptive analysis of opt-outs using publicly available data and the potential consequences on research are discussed.

METHOD

Trends in opt-outs in England were described by age, sex, and region. Using a hypothetical study, we explored statistical and epidemiological implications of opt-outs.

RESULTS

During the lead up to a key government-led deadline for registering opt-outs (from 31 May 2021-30 June 2021), 1 339 862 national data opt-outs were recorded; increasing the percentage of opt-outs in England from 2.77% to 4.97% of the population. Among females, percentage opt-outs increased by 83% (from 3.02% to 5.53%) compared with 76% in males (from 2.51% to 4.41%). Across age groups, the highest relative increase was among people aged 40-49 years, which rose from 2.89% to 6.04%. Considerable geographical variation was not clearly related to deprivation. Key research consequences of opt-outs include reductions in sample size and unpredictable distortion of observed measures of the frequency of health events or associations between these events.

CONCLUSION

Opt-out rates varied by age, sex, and place. The impact of this and variation by other characteristics on research is not quantifiable. Potential effects of opt-outs on research and consequences for health policies based on this research must be considered when creating future opt-out solutions.

How to Elucidate Consent-Free Research Use of Medical Data: A Case for "Health Data Literacy"

Unlabelled

The extensive utilization of personal health data is one of the key success factors of modern medical research. Obtaining consent to the use of such data during clinical care, however, bears the risk of low and unequal approval rates and risk of consequent methodological problems in the scientific use of the data. In view of these shortcomings, and of the proven willingness of people to contribute to medical research by sharing personal health data, the paradigm of informed consent needs to be reconsidered. The European General Data Protection Regulation gives the European member states considerable leeway with regard to permitting the research use of health data without consent. Following this approach would however require alternative offers of information that compensate for the lack of direct communication with experts during medical care. We therefore introduce the concept of "health data literacy," defined as the capacity to find, understand, and evaluate information about the risks and benefits of the research use of personal health data and to act accordingly. Specifically, health data literacy includes basic knowledge about the goals and methods of data-rich medical research and about the possibilities and limits of data protection. Although the responsibility for developing the necessary resources lies primarily with those directly involved in data-rich medical research, improving health data literacy should ultimately be of concern to everyone interested in the success of this type of research.

Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

UK homecare providers' views about, and experiences of, digitalisation: A national survey

Objective

Using digital systems to support the management and delivery of social care is a priority for UK governments. This study explored progress towards, and experiences of, digitalisation in the homecare sector and providers' views on contributing client data to a national policy/research dataset.

Methods

Over 150 UK homecare providers completed an on-line survey (October-December 2022). The survey was hosted on Qualtrics and comprised fixed- and free-text response questions. The recruited sample aligned with the profile of UK homecare providers in terms of use of digital systems, organisation type and size.

Results

Almost all respondents (95.5%) were using digital systems, in part or exclusively, to support care delivery. However, many (42.7%) reported a desire to further digitalise or a dissatisfaction with existing systems. Findings highlight the time and work involved in choosing a a software system, with the decision regarded as relatively high risk. Over 50 different software systems were being used across the sample. Most respondents (72.5%) supported the creation of a national dataset on homecare users. However, support and recompense are likely to needed to secure buy-in from what is a predominantly private sector context.

Conclusions

Findings suggest a complex and changing situation, with numerous different digital systems being used and the sector at different stages of digitalisation. The high-pressure, low margin context of UK homecare appeared to be exerting an influence on progress towards digitalisation. Evaluations of government strategies to stimulate and support digitalisation in this diverse and predominantly private sector context will be valuable.

Views on sharing mental health data for research purposes: qualitative analysis of interviews with people with mental illness

BACKGROUND

Improving the ways in which routinely-collected mental health data are shared could facilitate substantial advances in research and treatment. However, this process should only be undertaken in partnership with those who provide such data. Despite relatively widespread investigation of public perspectives on health data sharing more generally, there is a lack of research on the views of people with mental illness.

METHODS

Twelve people with lived experience of mental illness took part in semi-structured interviews via online video software. Participants had experience of a broad range of mental health conditions including anxiety, depression, schizophrenia, eating disorders and addiction. Interview questions sought to establish how participants felt about the use of routinely-collected health data for research purposes, covering different types of health data, what health data should be used for, and any concerns around its use.

RESULTS

Thematic analysis identified four overarching themes: benefits of sharing mental health data, concerns about sharing mental health data, safeguards, and data types. Participants were clear that health data sharing should facilitate improved scientific knowledge and better treatments for mental illness. There were concerns that data misuse could become another way in which individuals and society discriminate against people with mental illness, for example through insurance premiums or employment decisions. Despite this there was a generally positive attitude to sharing mental health data as long as appropriate safeguards were in place.

CONCLUSIONS

There was notable strength of feeling across participants that more should be done to reduce the suffering caused by mental illness, and that this could be partly facilitated by well-managed sharing of health data. The mental health research community could build on this generally positive attitude to mental health data sharing by following rigorous best practice tailored to the specific concerns of people with mental illness.

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

Mapping and evaluating national data flows: transparency, privacy, and guiding infrastructural transformation

The importance of big health data is recognised worldwide. Most UK National Health Service (NHS) care interactions are recorded in electronic health records, resulting in an unmatched potential for population-level datasets. However, policy reviews have highlighted challenges from a complex data-sharing landscape relating to transparency, privacy, and analysis capabilities. In response, we used public information sources to map all electronic patient data flows across England, from providers to more than 460 subsequent academic, commercial, and public data consumers. Although NHS data support a global research ecosystem, we found that multistage data flow chains limit transparency and risk public trust, most data interactions do not fulfil recommended best practices for safe data access, and existing infrastructure produces aggregation of duplicate data assets, thus limiting diversity of data and added value to end users. We provide recommendations to support data infrastructure transformation and have produced a website (https://DataInsights.uk) to promote transparency and showcase NHS data assets.

Data-driven research and healthcare: public trust, data governance and the NHS

It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced in recent years. In this paper, we examine the question of, what is it about sharing NHS data for research and innovation with for-profit companies that challenges public trust? To address this question, we draw from political theory to provide an account of public trust that helps better understand the relationship between the public and the NHS within a democratic context, as well as, the kind of obligations and expectations that govern this relationship. Then we examine whether the way in which the NHS is managing patient data and its collaboration with the private sector fit under this trust-based relationship. We argue that the datafication of healthcare and the broader 'health and wealth' agenda adopted by consecutive UK governments represent a major shift in the institutional character of the NHS, which brings into question the meaning of public good the NHS is expected to provide, challenging public trust. We conclude by suggesting that to address the problem of public trust, a theoretical and empirical examination of the benefits but also the costs associated with this shift needs to take place, as well as an open conversation at public level to determine what values should be promoted by a public institution like the NHS.

De-identified Bayesian personal identity matching for privacy-preserving record linkage despite errors: development and validation

BACKGROUND

Epidemiological research may require linkage of information from multiple organizations. This can bring two problems: (1) the information governance desirability of linkage without sharing direct identifiers, and (2) a requirement to link databases without a common person-unique identifier.

METHODS

We develop a Bayesian matching technique to solve both. We provide an open-source software implementation capable of de-identified probabilistic matching despite discrepancies, via fuzzy representations and complete mismatches, plus de-identified deterministic matching if required. We validate the technique by testing linkage between multiple medical records systems in a UK National Health Service Trust, examining the effects of decision thresholds on linkage accuracy. We report demographic factors associated with correct linkage.

RESULTS

The system supports dates of birth (DOBs), forenames, surnames, three-state gender, and UK postcodes. Fuzzy representations are supported for all except gender, and there is support for additional transformations, such as accent misrepresentation, variation for multi-part surnames, and name re-ordering. Calculated log odds predicted a proband's presence in the sample database with an area under the receiver operating curve of 0.997-0.999 for non-self database comparisons. Log odds were converted to a decision via a consideration threshold θ and a leader advantage threshold δ. Defaults were chosen to penalize misidentification 20-fold versus linkage failure. By default, complete DOB mismatches were disallowed for computational efficiency. At these settings, for non-self database comparisons, the mean probability of a proband being correctly declared to be in the sample was 0.965 (range 0.931-0.994), and the misidentification rate was 0.00249 (range 0.00123-0.00429). Correct linkage was positively associated with male gender, Black or mixed ethnicity, and the presence of diagnostic codes for severe mental illnesses or other mental disorders, and negatively associated with birth year, unknown ethnicity, residential area deprivation, and presence of a pseudopostcode (e.g. indicating homelessness). Accuracy rates would be improved further if person-unique identifiers were also used, as supported by the software. Our two largest databases were linked in 44 min via an interpreted programming language.

CONCLUSIONS

Fully de-identified matching with high accuracy is feasible without a person-unique identifier and appropriate software is freely available.

Canadians' opinions towards COVID-19 data-sharing: a national cross-sectional survey

OBJECTIVES

COVID-19 research has significantly contributed to pandemic response and the enhancement of public health capacity. COVID-19 data collected by provincial/territorial health authorities in Canada are valuable for research advancement yet not readily available to the public, including researchers. To inform developments in public health data-sharing in Canada, we explored Canadians' opinions of public health authorities sharing deidentified individual-level COVID-19 data publicly.

DESIGN/SETTING/INTERVENTIONS/OUTCOMES

A national cross-sectional survey was administered in Canada in March 2022, assessing Canadians' opinions on publicly sharing COVID-19 datatypes. Market research firm Léger was employed for recruitment and data collection.

PARTICIPANTS

Anyone greater than or equal to 18 years and currently living in Canada.

RESULTS

4981 participants completed the survey with a 92.3% response rate. 79.7% were supportive of provincial/territorial authorities publicly sharing deidentified COVID-19 data, while 20.3% were hesitant/averse/unsure. Datatypes most supported for being shared publicly were symptoms (83.0% in support), geographical region (82.6%) and COVID-19 vaccination status (81.7%). Datatypes with the most aversion were employment sector (27.4% averse), postal area (26.7%) and international travel history (19.7%). Generally supportive Canadians were characterised as being ≥50 years, with higher education, and being vaccinated against COVID-19 at least once. Vaccination status was the most influential predictor of data-sharing opinion, with respondents who were ever vaccinated being 4.20 times more likely (95% CI 3.21 to 5.48, p=0.000) to be generally supportive of data-sharing than those unvaccinated.

CONCLUSIONS

These findings suggest that the Canadian public is generally favourable to deidentified data-sharing. Identifying factors that are likely to improve attitudes towards data-sharing are useful to stakeholders involved in data-sharing initiatives, such as public health agencies, in informing the development of public health communication and data-sharing policies. As Canada progresses through the COVID-19 pandemic, and with limited testing and reporting of COVID-19 data, it is essential to improve deidentified data-sharing given the public's general support for these efforts.

"Data makes the story come to life:" understanding the ethical and legal implications of Big Data research involving ethnic minority healthcare workers in the United Kingdom-a qualitative study

The aim of UK-REACH ("The United Kingdom Research study into Ethnicity And COVID-19 outcomes in Healthcare workers") is to understand if, how, and why healthcare workers (HCWs) in the United Kingdom (UK) from ethnic minority groups are at increased risk of poor outcomes from COVID-19. In this article, we present findings from the ethical and legal stream of the study, which undertook qualitative research seeking to understand and address legal, ethical, and social acceptability issues around data protection, privacy, and information governance associated with the linkage of HCWs' registration data and healthcare data. We interviewed 22 key opinion leaders in healthcare and health research from across the UK in two-to-one semi-structured interviews. Transcripts were coded using qualitative thematic analysis. Participants told us that a significant aspect of Big Data research in public health is varying drivers of mistrust-of the research itself, research staff and funders, and broader concerns of mistrust within participant communities, particularly in the context of COVID-19 and those situated in more marginalised community settings. However, despite the challenges, participants also identified ways in which legally compliant and ethically informed approaches to research can be crafted to mitigate or overcome mistrust and establish greater confidence in Big Data public health research. Overall, our research indicates that a "Big Data Ethics by Design" approach to research in this area can help assure (1) that meaningful community and participant engagement is taking place and that extant challenges are addressed, and (2) that any new challenges or hitherto unknown unknowns can be rapidly and properly considered to ensure potential (but material) harms are identified and minimised where necessary. Our findings indicate such an approach, in turn, will help drive better scientific breakthroughs that translate into medical innovations and effective public health interventions, which benefit the publics studied, including those who are often marginalised in research.