Patients' and public views and attitudes towards the sharing of health data for research: a narrative review of the empirical evidence

Ethical and social reflections on the proposed European Health Data Space

The COVID-19 pandemic demonstrated the benefits of international data sharing. Data sharing enabled the health care policy makers to make decisions based on real-time data, it enabled the tracking of the virus, and importantly it enabled the development of vaccines that were crucial to mitigating the impact of the virus. This data sharing is not the norm as data sharing needs to navigate complex ethical and legal rules, and in particular, the fragmented application of the General Data Protection Regulation (GDPR). The introduction of the draft regulation for a European Health Data Space (EHDS) in May 2022 seeks to address some of these legal issues. If passed, it will create an obligation to share electronic health data for certain secondary purposes. While there is a clear need to address the legal complexities involved with data sharing, it is critical that any proposed reforms are in line with ethical principles and the expectations of the data subjects. In this paper we offer a critique of the EHDS and offer some recommendations for this evolving regulatory space.

[Foundations for the scientific use of extensive health care data in Germany-results of the Data Sharing working group of the Medical Informatics Initiative]

Healthcare data are an important resource in applied medical research. They are available multicentrically. However, it remains a challenge to enable standardized data exchange processes between federal states and their individual laws and regulations. The Medical Informatics Initiative (MII) was founded in 2016 to implement processes that enable cross-clinic access to healthcare data in Germany. Several working groups (WGs) have been set up to coordinate standardized data structures (WG Interoperability), patient information and declarations of consent (WG Consent), and regulations on data exchange (WG Data Sharing). Here we present the most important results of the Data Sharing working group, which include agreed terms of use, legal regulations, and data access processes. They are already being implemented by the established Data Integration Centers (DIZ) and Use and Access Committees (UACs). We describe the services that are necessary to provide researchers with standardized data access. They are implemented with the Research Data Portal for Health, among others. Since the pilot phase, the processes of 385 active researchers have been used on this basis, which, as of April 2024, has resulted in 19 registered projects and 31 submitted research applications.

From code to care: Clinician and researcher perspectives on an optimal therapeutic web portal for acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML), a rapidly progressing cancer of the blood and bone marrow, is the most common and fatal type of adult leukemia. Therapeutic web portals have great potential to facilitate AML research advances and improve health outcomes by increasing the availability of data, the speed and reach of new knowledge, and the communication between researchers and clinicians in the field. However, there is a need for stakeholder research regarding their optimal features, utility, and implementation.

METHODS

To better understand stakeholder perspectives regarding an ideal pan-Canadian web portal for AML research, semi-structured qualitative interviews were conducted with 17 clinicians, researchers, and clinician-researchers. Interview guides were inspired by De Laat's "fictive scripting", a method where experts are presented with scenarios about a future technology and asked questions about its implementation. Content analysis relied on an iterative process using themes extracted from both existing scientific literature and the data.

RESULTS

Participants described potential benefits of an AML therapeutic portal including facilitating data-sharing, communication, and collaboration, and enhancing clinical trial matchmaking for patients, potentially based on their specific genomic profiles. There was enthusiasm about researcher, clinician, and clinician-researcher access, but some disagreement about the nature of potential patient access to the portal. Interviewees also discussed two key elements they believed to be vital to the uptake and thus success of a therapeutic AML web portal: credibility and user friendliness. Finally, sustainability, security and privacy concerns were also documented.

CONCLUSIONS

This research adds to existing calls for digital platforms for researchers and clinicians to supplement extant modes of communication to streamline research and its dissemination, advance precision medicine, and ultimately improve patient prognosis and care. Findings are applicable to therapeutic web portals more generally, particularly in genomic and translational medicine, and will be of interest to portal end-users, developers, researchers, and policymakers.

Data sharing and big data in health professions education: Ottawa consensus statement and recommendations for scholarship

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare.

From opt-out to opt-in consent for secondary use of medical data and residual biomaterial: An evaluation using the RE-AIM framework

BACKGROUND

Patient records, imaging, and residual biomaterial from clinical procedures are crucial resources for medical research. In the Netherlands, consent for secondary research has historically relied on opt-out consent. For ethical-legal experts who purport passive consent undermines patient autonomy, opt-in consent (wherein affirmative action is required) is seen as the preferred standard. To date, there is little empirical research exploring patient feasibility, organizational consequences, and the potential risks for research based on secondary data. Thus, we applied the RE-AIM framework to evaluate the impact of migrating from an opt-out to an opt-in consent process.

METHODS

This evaluation was carried out in Radboud University Medical Center, a large tertiary hospital located in the southeast of the Netherlands. All non-acute, mentally competent patients ≥16 years of age registered between January 13, 2020 and June 30, 2023 were targeted (N = 101,437). In line with the RE-AIM framework, individual and organizational consequences were evaluated across five domains: reach, efficacy, adoption, implementation, and maintenance.

RESULTS

101,437 eligible patients were approached of whom 66,214 (65.3%) consented, 8,059 (7.9%) refused consent and 27,164 (26.8%) had no response. Of the 74,273 patients with a response, 89.1% consented to secondary use. The migration to an opt-in consent system was modestly successful; yet notably, differential response patterns by key sociodemographic characteristics were observed. Adaptions to the process flow improved its effectiveness and resulted in a reasonable response over time. Implementation was most affected by budgetary restraints, thus impeding the iterative approach which could have further improved domain outcomes.

CONCLUSION

This evaluation provides an overview of logistical and pragmatic issues encountered when migrating from opt-out to opt-in consent. Response bias remains a major concern. Though not always directly transferable, these lessons can be broadly used to inform other health care organizations of the potential advantages and pitfalls of an opt-in consent system.

Return of participant-level clinical trial results to participants: pilot of a simplified centralised approach

OBJECTIVES

Public access databases such as clinicaltrials.gov achieve dissemination of clinical trial design and aggregated study results. However, return of participant-level data is rarely done. A key barrier includes the proprietary ownership of data by the sponsor. Additionally, investigators may not have access to centralised data, and per International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Good Clinical Practice, must maintain the confidentiality of participants. This study piloted an approach to return both individual and aggregate clinical trial data to parents of children participating in a series of open-label clinical trials.

SETTING AND DESIGN

A small biotech company obtained central ethics approval (centralised institutional review board [IRB], non-exempt). The study was advertised via parent advocacy groups. Parents of trial participants were offered the option to contact an employee (coordinator) within the company, requesting return of their child's study results. Ethics approval covered participation in six countries. The study focused on the sequential clinical trials of vamorolone VBP15-002 (NCT02760264) and VBP15-003 (NCT02760277) (post-results).

INTERVENTIONS

Contact initiated by the parent enabled the coordinator to obtain informed consent (and separate General Data Protection Regulations consent), with phone translation when needed. Using date of birth and study site location provided by the parent, the data manager reported the participant number to the coordinator. The coordinator retrieved and compiled data, along with an aggregate summary, which was mailed via a password protected and encrypted memory device to the parent. Prereturn and postreturn surveys were sent to consented parents (n=19; 40% of 48 total trial participants) and investigators.

RESULTS

Prereturn surveys indicated a request for as much data as offered, in all formats offered. Postreturn survey showed high satisfaction with the process and data returned. Survey of the physician site investigators (n=10; 100% participation of investigators) voiced general satisfaction with the process, with some reservations.

CONCLUSIONS

This pilot study demonstrates an innovative, cost-effective, centralised and labour conservative approach to return of participant-level and aggregate data to participants in studies.

Patient and Public Willingness to Share Personal Health Data for Third-Party or Secondary Uses: Systematic Review

BACKGROUND

International advances in information communication, eHealth, and other digital health technologies have led to significant expansions in the collection and analysis of personal health data. However, following a series of high-profile data sharing scandals and the emergence of COVID-19, critical exploration of public willingness to share personal health data remains limited, particularly for third-party or secondary uses.

OBJECTIVE

This systematic review aims to explore factors that affect public willingness to share personal health data for third-party or secondary uses.

METHODS

A systematic search of 6 databases (MEDLINE, Embase, PsycINFO, CINAHL, Scopus, and SocINDEX) was conducted with review findings analyzed using inductive-thematic analysis and synthesized using a narrative approach.

RESULTS

Of the 13,949 papers identified, 135 were included. Factors most commonly identified as a barrier to data sharing from a public perspective included data privacy, security, and management concerns. Other factors found to influence willingness to share personal health data included the type of data being collected (ie, perceived sensitivity); the type of user requesting their data to be shared, including their perceived motivation, profit prioritization, and ability to directly impact patient care; trust in the data user, as well as in associated processes, often established through individual choice and control over what data are shared with whom, when, and for how long, supported by appropriate models of dynamic consent; the presence of a feedback loop; and clearly articulated benefits or issue relevance including valued incentivization and compensation at both an individual and collective or societal level.

CONCLUSIONS

There is general, yet conditional public support for sharing personal health data for third-party or secondary use. Clarity, transparency, and individual control over who has access to what data, when, and for how long are widely regarded as essential prerequisites for public data sharing support. Individual levels of control and choice need to operate within the auspices of assured data privacy and security processes, underpinned by dynamic and responsive models of consent that prioritize individual or collective benefits over and above commercial gain. Failure to understand, design, and refine data sharing approaches in response to changeable patient preferences will only jeopardize the tangible benefits of data sharing practices being fully realized.

Police and hospital data linkage for traffic injury surveillance: A systematic review

This systematic review examines studies of traffic injury that involved linkage of police crash data and hospital data and were published from 1994 to 2023 worldwide in English. Inclusion and exclusion criteria were the basis for selecting papers from PubMed, Web of Science, and Scopus, and for identifying additional relevant papers using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and supplementary snowballing (n = 60). The selected papers were reviewed in terms of research objectives, data items and sample size included, temporal and spatial coverage, linkage methods and software tools, as well as linkage rates and most significant findings. Many studies found that the number of clinically significant road injury cases was much higher according to hospital data than crash data. Under-estimation of cases in crash data differs by road user type, pedestrian cases commonly being highly under-counted. A limited number of the papers were from low- and middle-income countries. The papers reviewed lack consistency in what was reported and how, which limited comparability.

The viewpoints of parents of children with mental disorders regarding the confidentiality and security of their children's information in the Iranian national electronic health record system

INTRODUCTION

Electronic health records help collect and communicate patient information among healthcare providers. The confidentiality of information, especially for patients with mental disorders, is paramount due to its profound impacts on individuals' lives' social and personal aspects. This study aimed to investigate the viewpoints and concerns of parents of children with mental disorders regarding the confidentiality and security of their children's information in the Iranian National Electronic Health Record System (IEHRS).

METHODS

This is a survey study on parents or guardians of children with mental disorders who visited Kerman's specialised child psychiatry treatment centres. The data collection tool was a researcher-made questionnaire with 28 questions organised in seven sections, including demographic information of parents, children's medical history, Internet use, knowledge about IEHRS, the necessity of data collection, IEHRS security concerns, and privacy concerns. The data were analysed in SPSS 24 software using descriptive statistics and logistic and ordinal regressions to assess the relationship between parents' demographic characteristics and their viewpoints regarding information security and confidentiality concerns.

RESULTS

The results showed that more than 85 % of the parents believed that the security of their children's information in IEHRS was moderate to high. More than two-thirds (71 %) of the parents also believed that IEHRS should tighten its privacy policies. Most participants (87 %) were concerned about their children's information security in IEHRS. In this study, the parents' concerns about the privacy and security of information in IEHRS were not significantly associated with their age, gender, or knowledge about IEHRS.

CONCLUSIONS

Most parents of children with mental disorders were concerned about the security and confidentiality of their children's information in IEHRS. Thus, health policymakers should maintain a high level of security and establish appropriate privacy and confidentiality rules in IEHRS. In addition, they should be transparent about the system's security mechanisms and confidentiality regulations to win public trust.

A systematic literature review of the 'commercialisation effect' on public attitudes towards biobank and genomic data repositories

Initiatives that collect and share genomic data to advance health research are widespread and accelerating. Commercial interests in these efforts, while vital, may erode public trust and willingness to provide personal genomic data, upon which these initiatives depend. Understanding public attitudes towards providing genomic data for health research in the context of commercial involvement is critical. A PRISMA-guided search of six online academic databases identified 113 quantitative and qualitative studies using primary data pertaining to public attitudes towards commercial actors in the management, collection, access, and use of biobank and genomic data. The presence of commercial interests yields interrelated public concerns around consent, privacy and data security, trust in science and scientists, benefit sharing, and the ownership and control of health data. Carefully considered regulatory and data governance and access policies are therefore required to maintain public trust and support for genomic health initiatives.

Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM

Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers.Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site.Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting.Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data.Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings.

Patients' knowledge, preferences, and perspectives about data protection and data control: an exploratory survey

Background: In the European Union, the General Data Protection Regulation (GDPR) plays a central role in the complex health research legal framework. It aims to protect the fundamental right to the protection of individuals' personal data, while allowing the free movement of such data. However, it has been criticized for challenging the conduct of research. Existing scholarship has paid little attention to the experiences and views of the patient community. The aim of the study was to investigate 1) the awareness and knowledge of patients, carers, and members of patient organizations about the General Data Protection Regulation, 2) their experience with exercising data subject rights, and 3) their understanding of the notion of "data control" and preferences towards various data control tools. Methods: An online survey was disseminated between December 2022 and March 2023. Quantitative data was analyzed descriptively and inferentially. Answers to open-ended questions were analyzed using the thematic analysis method. Results: In total, 220 individuals from 28 European countries participated. The majority were patients (77%). Most participants had previously heard about the GDPR (90%) but had not exercised any of their data subject rights. Individual data control tools appeared to be marginally more important than collective tools. The willingness of participants to share personal data with data altruism organizations increased if patient representatives would be involved in the decision-making processes of such organizations. Conclusion: The results highlighted the importance of providing in-depth education about data protection. Although participants showed a slight preference towards individual control tools, the reflection based on existing scholarship identified that individual control holds risks that could be mitigated through carefully operationalized collective tools. The discussion of results was used to provide a critical view into the proposed European Health Data Space, which has yet to find a productive balance between individual control and allowing the reuse of personal data for research.

Building a Sustainable Learning Health Care System for Pregnant and Lactating People: Interview Study Among Data Access Providers

BACKGROUND

In many areas of health care, learning health care systems (LHSs) are seen as promising ways to accelerate research and outcomes for patients by reusing health and research data. For example, considering pregnant and lactating people, for whom there is still a poor evidence base for medication safety and efficacy, an LHS presents an interesting way forward. Combining unique data sources across Europe in an LHS could help clarify how medications affect pregnancy outcomes and lactation exposures. In general, a remaining challenge of data-intensive health research, which is at the core of an LHS, has been obtaining meaningful access to data. These unique data sources, also called data access providers (DAPs), are both public and private organizations and are important stakeholders in the development of a sustainable and ethically responsible LHS. Sustainability is often discussed as a challenge in LHS development. Moreover, DAPs are increasingly expected to move beyond regulatory compliance and are seen as moral agents tasked with upholding ethical principles, such as transparency, trustworthiness, responsibility, and community engagement.

OBJECTIVE

This study aims to explore the views of people working for DAPs who participate in a public-private partnership to build a sustainable and ethically responsible LHS.

METHODS

Using a qualitative interview design, we interviewed 14 people involved in the Innovative Medicines Initiative (IMI) ConcePTION (Continuum of Evidence from Pregnancy Exposures, Reproductive Toxicology and Breastfeeding to Improve Outcomes Now) project, a public-private collaboration with the goal of building an LHS for pregnant and lactating people. The pseudonymized transcripts were analyzed thematically.

RESULTS

A total of 3 themes were identified: opportunities and responsibilities, conditions for participation and commitment, and challenges for a knowledge-generating ecosystem. The respondents generally regarded the collaboration as an opportunity for various reasons beyond the primary goal of generating knowledge about medication safety during pregnancy and lactation. Respondents had different interpretations of responsibility in the context of data-intensive research in a public-private network. Respondents explained that resources (financial and other), scientific output, motivation, agreements collaboration with the pharmaceutical industry, trust, and transparency are important conditions for participating in and committing to the ConcePTION LHS. Respondents also discussed the challenges of an LHS, including the limitations to (real-world) data analyses and governance procedures.

CONCLUSIONS

Our respondents were motivated by diverse opportunities to contribute to an LHS for pregnant and lactating people, primarily centered on advancing knowledge on medication safety. Although a shared responsibility for enabling real-world data analyses is acknowledged, their focus remains on their work and contribution to the project rather than on safeguarding ethical data handling. The results of our interviews underline the importance of a transparent governance structure, emphasizing the trust between DAPs and the public for the success and sustainability of an LHS.

Participant characteristics and reasons for non-consent to health information linkage for research: experiences from the ATHENA COVID-19 study

BACKGROUND

The linkage of primary care, hospital and other health registry data is a global goal, and a consent-based approach is often used. Understanding the attitudes of why participants take part is important, yet little is known about reasons for non-participation. The ATHENA COVID-19 feasibility study investigated: 1) health outcomes of people diagnosed with COVID-19 in Queensland, Australia through primary care health data linkage using consent, and 2) created a cohort of patients willing to be re-contacted in future to participate in clinical trials. This report describes the characteristics of participants declining to participate and reasons for non-consent.

METHODS

Patients diagnosed with COVID-19 from January 1st, 2020, to December 31st, 2020, were invited to consent to having their primary healthcare data extracted from their GP into a Queensland Health database and linked to other data sets for ethically approved research. Patients were also asked to consent to future recontact for participation in clinical trials. Outcome measures were proportions of patients consenting to data extraction, permission to recontact, and reason for consent decline.

RESULTS

Nine hundred and ninety-five participants were approached and 842(85%) reached a consent decision. 581(69%), 615(73%) and 629(75%) consented to data extraction, recontact, or both, respectively. Mean (range) age of consenters and non-consenters were 50.6(22-77) and 46.1(22-77) years, respectively. Adjusting for age, gender and remoteness, older participants were more likely to consent than younger (aOR 1.02, 95%CI 1.01 to 1.03). The least socio-economically disadvantaged were more likely to consent than the most disadvantaged (aOR 2.20, 95% 1.33 to 3.64). There was no difference in consent proportions regarding gender or living in more remote regions. The main reasons for non-consent were 'not interested in research' (37%), 'concerns about privacy' (15%), 'not registered with a GP' (8%) and 'too busy/no time' (7%). 'No reason' was given in 20%.

CONCLUSION

Younger participants and the more socio-economically deprived are more likely to non-consent to primary care data linkage. Lack of patient interest in research, time required to participate and privacy concerns, were the most common reasons cited for non-consent. Future health care data linkage studies addressing these issues may prove helpful.

Walk a mile in my shoes: perspectives towards sharing of health and experience data among individuals living with sickle cell disorder

Background

Advancements in digital health technologies (DHTs) mean people are increasingly recording and managing personal health data. As observed during the COVID-19 pandemic, sharing of such data may provide unrivalled opportunities in advancing our understanding of conditions otherwise poorly understood, including rare conditions.

Methods

A semi-structured focus group (n=25) explored perspectives and experiences of sharing health data among those with a group of rare haematological conditions, sickle cell disorder (SCD). The focus group explored (I) what 'feeling well' looks like; (II) how this could be monitored using DHTs; (III) which data healthcare professionals (HCPs) should pay greater attention to and; (IV) types of data willing to be shared, with whom, and under which conditions. Key themes were further assessed via an online survey (n=50).

Results

Patient-relevant measures of condition-management focused on "everything else that comes with" SCD, suggesting HCPs did not pay sufficient attention to day-to-day symptom variability. This was juxtaposed against the "fixed and one-off" electronic health record (EHR), collecting pre-specified data at pre-determined snapshots of time, not considered reflective of outcomes associated with "feeling well" day-to-day. Forty-four-point-seven percent of respondents had previously shared health data. Most were willing to share data concerning symptoms and health service utilisation, but were less willing to share genomic and EHR data. Sixty-one-point-seven percent believed HCPs did not pay enough attention to daily fluctuations in mental and physical health. Financial benefits (74.5%), trust in organisations seeking data (72.3%), and knowing how data will be used (61.7%) were key facilitators of data sharing. Seventy-one percent, 70% and 65.2% had not previously shared health data with the pharmaceutical industry, charitable organisations and digital health interventions respectively, but were open to doing so in the future.

Conclusions

Those living with the rare condition SCD were supportive of collecting and sharing data to foster research and improve understanding and outcomes. However, specific requirements were identified to respect privacy and informational needs regarding future use of data. DHTs can be a valuable tool in improving understanding of the day-to-day impact of health conditions, but understanding patient needs is critical in ensuring involvement in the process, as not all data types are considered of equal value, benefit, or risk.

Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence

In spite of an exponential increase in the volume of medical data produced globally, much of these data are inaccessible to those who might best use them to develop improved health care solutions through the application of advanced analytics such as artificial intelligence. Data liberation and crowdsourcing represent two distinct but interrelated approaches to bridging existing data silos and accelerating the pace of innovation internationally. In this article, we examine these concepts in the context of medical artificial intelligence research, summarizing their potential benefits, identifying potential pitfalls, and ultimately making a case for their expanded use going forward. A practical example of a crowdsourced competition using an international medical imaging dataset is provided. Keywords: Artificial Intelligence, Data Liberation, Crowdsourcing © RSNA, 2023.

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.

Public Preferences for Digital Health Data Sharing: Discrete Choice Experiment Study in 12 European Countries

BACKGROUND

With new technologies, health data can be collected in a variety of different clinical, research, and public health contexts, and then can be used for a range of new purposes. Establishing the public's views about digital health data sharing is essential for policy makers to develop effective harmonization initiatives for digital health data governance at the European level.

OBJECTIVE

This study investigated public preferences for digital health data sharing.

METHODS

A discrete choice experiment survey was administered to a sample of European residents in 12 European countries (Austria, Denmark, France, Germany, Iceland, Ireland, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom) from August 2020 to August 2021. Respondents answered whether hypothetical situations of data sharing were acceptable for them. Each hypothetical scenario was defined by 5 attributes ("data collector," "data user," "reason for data use," "information on data sharing and consent," and "availability of review process"), which had 3 to 4 attribute levels each. A latent class model was run across the whole data set and separately for different European regions (Northern, Central, and Southern Europe). Attribute relative importance was calculated for each latent class's pooled and regional data sets.

RESULTS

A total of 5015 completed surveys were analyzed. In general, the most important attribute for respondents was the availability of information and consent during health data sharing. In the latent class model, 4 classes of preference patterns were identified. While respondents in 2 classes strongly expressed their preferences for data sharing with opposing positions, respondents in the other 2 classes preferred not to share their data, but attribute levels of the situation could have had an impact on their preferences. Respondents generally found the following to be the most acceptable: a national authority or academic research project as the data user; being informed and asked to consent; and a review process for data transfer and use, or transfer only. On the other hand, collection of their data by a technological company and data use for commercial communication were the least acceptable. There was preference heterogeneity across Europe and within European regions.

CONCLUSIONS

This study showed the importance of transparency in data use and oversight of health-related data sharing for European respondents. Regional and intraregional preference heterogeneity for "data collector," "data user," "reason," "type of consent," and "review" calls for governance solutions that would grant data subjects the ability to control their digital health data being shared within different contexts. These results suggest that the use of data without consent will demand weighty and exceptional reasons. An interactive and dynamic informed consent model combined with oversight mechanisms may be a solution for policy initiatives aiming to harmonize health data use across Europe.

Association Between the Characteristics of mHealth Apps and User Input During Development and Testing: Secondary Analysis of App Assessment Data

BACKGROUND

User involvement is increasingly acknowledged as a central part of health care innovation. However, meaningful user involvement during the development and testing of mobile health apps is often not fully realized.

OBJECTIVE

This study aims to examine in which areas user input is most prevalent and whether there is an association between user inclusion and compliance with best practices for mobile health apps.

METHODS

A secondary analysis was conducted on an assessment data set of 1595 health apps. The data set contained information on whether the apps had been developed or tested with user input and whether they followed best practices across several domains. Background information was also available regarding the apps' country of origin, targeted condition areas, subjective user ratings, download numbers, and risk (as per the National Institute for Health and Care Excellence Evidence Standards Framework [ESF]). Descriptive statistics, Mann-Whitney U tests, and Pearson chi-square analyses were applied to the data.

RESULTS

User involvement was reported by 8.71% (139/1595) of apps for only the development phase, by 33.67% (537/1595) of apps for only the testing phase, by 21.88% (349/1595) of apps for both phases, and by 35.74% (570/1595) of apps for neither phase. The highest percentage of health apps with reported user input during development was observed in Denmark (19/24, 79%); in the condition areas of diabetes (38/79, 48%), cardiology (15/32, 47%), pain management (20/43, 47%), and oncology (25/54, 46%); and for high app risk (ESF tier 3a; 105/263, 39.9%). The highest percentage of health apps with reported user input during testing was observed in Belgium (10/11, 91%), Sweden (29/34, 85%), and France (13/16, 81%); in the condition areas of neurodiversity (42/52, 81%), respiratory health (58/76, 76%), cardiology (23/32, 72%), and diabetes (56/79, 71%); and for high app risk (ESF tier 3a; 176/263, 66.9%). Notably, apps that reported seeking user input during testing demonstrated significantly more downloads than those that did not (P=.008), and user inclusion was associated with better compliance with best practices in clinical assurance, data privacy, risk management, and user experience.

CONCLUSIONS

The countries and condition areas in which the highest percentage of health apps with user involvement were observed tended to be those with higher digital maturity in health care and more funding availability, respectively. This suggests that there may be a trade-off between developers' willingness or ability to involve users and the need to meet challenges arising from infrastructure limitations and financial constraints. Moreover, the finding of a positive association between user inclusion and compliance with best practices indicates that, where no other guidance is available, users may benefit from prioritizing health apps developed with user input as the latter may be a proxy for broader app quality.

Public preference on sharing health data to inform research, health policy and clinical practice in Australia: A stated preference experiment

OBJECTIVE

To investigate public willingness to share sensitive health information for research, health policy and clinical practice.

METHODS

A total of 1,003 Australian respondents answered an online, attribute-driven, survey in which participants were asked to accept or reject hypothetical choice sets based on a willingness to share their health data for research and frontline-medical support as part of an integrated health system. The survey consisted of 5 attributes: Stakeholder access for analysis (Analysing group); Type of information collected; Purpose of data collection; Information governance; and Anticipated benefit; the results of which were analysed using logistic regression.

RESULTS

When asked about their preference for sharing their health data, respondents had no preference between data collection for the purposes of clinical practice, health policy or research, with a slight preference for having government organisations manage, govern and curate the integrated datasets from which the analysis was being conducted. The least preferred option was for personal health records to be integrated with insurance records or for their data collected by privately owned corporate organisations. Individuals preferred their data to be analysed by a public healthcare provider or government staff and expressed a dislike for any private company involvement.

CONCLUSIONS

The findings from this study suggest that Australian consumers prefer to share their health data when there is government oversight, and have concerns about sharing their anonymised health data for clinical practice, health policy or research purposes unless clarity is provided pertaining to its intended purpose, limitations of use and restrictions to access. Similar findings have been observed in the limited set of existing international studies utilising a stated preference approach. Evident from this study, and supported by national and international research, is that the establishment and preservation of a social license for data linkage in health research will require routine public engagement as a result of continuously evolving technological advancements and fluctuating risk tolerance. Without more work to understand and address stakeholder concerns, consumers risk being reluctant to participate in data-sharing and linkage programmes.

Conceptualizing fairness in the secondary use of health data for research: A scoping review

With the introduction of the European Health Data Space (EHDS), the secondary use of health data for research purposes is attracting more attention. Secondary health data processing promises to address novel research questions, inform the design of future research and improve healthcare delivery generally. To comply with the existing data protection regulations, the secondary data use must be fair, among other things. However, there is no clear understanding of what fairness means in the context of secondary use of health data for scientific research purposes. In response, we conducted a scoping review of argument-based literature to explore how fairness in the secondary use of health data has been conceptualized. A total of 35 publications were included in the final synthesis after abstract and full-text screening. Using an inductive approach and a thematic analysis, our review has revealed that balancing individual and public interests, reducing power asymmetries, setting conditions for commercial involvement, and implementing benefit sharing are essential to guarantee fair secondary use research. The findings of this review can inform current and future research practices and policy development to adequately address concerns about fairness in the secondary use of health data.

Health Research with Data in a Time of Privacy: Which Information do Patients Want?

When hospitals ask broad consent for the secondary use of patient data for scientific research, it is unknown for which studies the data will be used. We investigated what patients at a cancer hospital consider to be an adequate level and most suitable method of information provision using questionnaires (n = 71) and interviews (n = 24). A part of the respondents indicated that they would feel sufficiently informed by either being notified about potential further use, or by receiving a general brochure before being asked for consent. Others stated that additional information would be interesting and appreciated. Yet, when discussing required resources needed to provide additional information, interviewees lowered the bar of what they considered minimally required, voicing the importance of spending resources on research.

Identifying benefits and concerns with using digital health services during COVID-19: Evidence from a hospital-based patient survey

Despite large-scale adoption during COVID-19, patient perceptions on the benefits and potential risks with receiving care through digital technologies have remained largely unexplored. A quantitative content analysis of responses to a questionnaire (N = 6766) conducted at a multi-site acute trust in London (UK), was adopted to identify commonly reported benefits and concerns. Patients reported a range of promising benefits beyond immediate usage during COVID-19, including ease of access; support for disease and care management; improved timeliness of access and treatment; and better prioritisation of healthcare resources. However, in addition to known risks such as data security and inequity in access, our findings also illuminate some less studied concerns, including perceptions of compromised safety; negative impacts on patient-clinician relationships; and difficulties in interpreting health information provided through electronic health records and mHealth apps. Implications for future research and practice are discussed.

The Learning Electronic Health Record

Electronic medical records (EMRs) constitute the electronic version of all medical information included in a patient's paper chart. The electronic health record (EHR) technology has witnessed massive expansion in developed countries and to a lesser extent in underresourced countries during the last 2 decades. We will review factors leading to this expansion, how the emergence of EHRs is affecting several health-care stakeholders; some of the growing pains associated with EHRs with a particular emphasis on the delivery of care to the critically ill; and ongoing developments on the path to improve the quality of research, health-care delivery, and stakeholder satisfaction.

Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.

Expanding citizen engagement in the secondary use of health data: an opportunity for national health data access bodies to realise the intentions of the European health data space

The European Health Data Space (EHDS) aims to make the primary use of health data for healthcare provision more continuous, effective, and (cost) efficient. Moreover, it pursues to facilitate the secondary use of health data for purposes such as research, innovation, and policy making. In the context of secondary use, the EHDS legislative proposal (published on 3 May 2022) argues that Member States should develop Health Data Access Bodies (HDABs) whose responsibilities include facilitating the secondary use of health data, issuing data permits, and implementing high levels of accountability and security. In Belgium, the setup in 2023 of a federal Health Data Agency (HDA) that is developing and implementing a policy strategy and framework for the secondary use of health data, aligns well with the responsibilities set out for HDABs. Even though the EHDS aspires the empowerment of citizens, for instance by giving them access to their health data and control over the healthcare professionals who can consult these data, this call for citizen empowerment resonates less loudly regarding secondary use. We think, however, that elaborating and implementing citizen engagement in the domain of secondary use is required to align secondary use with socio-ethical sensitivities, preferences, and values and to provide social legitimacy and ethical solidity to a health data governance system. When implementing the EHDS legislation on a national level, the Belgian HDA and the future HDABs in general might be excellent opportunities to realise this ambition of citizen involvement and empowerment. More specifically, we urge HDABs, firstly, to expand the field of citizen engagement towards the domain of secondary use and, secondly, to respect and facilitate the diversity of citizen engagement. This would offer citizens genuine, continuous and diversified possibilities of involvement and co-creation concerning the development of a solid ethical governance framework for health data.

Patient Perspectives on Artificial Intelligence in Radiology

There are two major areas for patient engagement in radiology artificial intelligence (AI). One is in the sharing of data for AI development; the second is the use of AI in patient care. In general, individuals support sharing deidentified data if used for the common good, to help others with similar health conditions, or for research. However, there is concern with risk to privacy including reidentification and use for other than intended purposes. Lack of trust is mentioned as a barrier for data sharing. Individuals want to be involved in the data-sharing process. In the use of AI in medical care, patients generally support AI as an assist to the radiologist but lack trust in unsupervised AI. Patients worry about liability in case of bad outcomes. Patients are concerned about loss of the human connection and the loss of empathy during a vulnerable time in their lives. Patients expressed concern about risk of discrimination due to bias in AI algorithms. Building trust in AI requires transparency, explainability, security, and privacy protection. Radiologists can take action to prepare their patients to become more trusting of AI. Developing and implementing data-sharing agreements allows patients to voluntarily help in the algorithm development process. Developing AI disclosure guidelines and having AI use disclosure discussions with patients will help them understand the use of AI in their care. As the use of AI increases, there is an opportunity for radiologists to develop and maintain close relationships with their patients and to become more involved in their care.

Participatory evaluation of the process of co-producing resources for the public on data science and artificial intelligence

BACKGROUND

The growth of data science and artificial intelligence offers novel healthcare applications and research possibilities. Patients should be able to make informed choices about using healthcare. Therefore, they must be provided with lay information about new technology. A team consisting of academic researchers, health professionals, and public contributors collaboratively co-designed and co-developed the new resource offering that information. In this paper, we evaluate this novel approach to co-production.

METHODS

We used participatory evaluation to understand the co-production process. This consisted of creative approaches and reflexivity over three stages. Firstly, everyone had an opportunity to participate in three online training sessions. The first one focused on the aims of evaluation, the second on photovoice (that included practical training on using photos as metaphors), and the third on being reflective (recognising one's biases and perspectives during analysis). During the second stage, using photovoice, everyone took photos that symbolised their experiences of being involved in the project. This included a session with a professional photographer. At the last stage, we met in person and, using data collected from photovoice, built the mandala as a representation of a joint experience of the project. This stage was supported by professional artists who summarised the mandala in the illustration.

RESULTS

The mandala is the artistic presentation of the findings from the evaluation. It is a shared journey between everyone involved. We divided it into six related layers. Starting from inside layers present the following experiences (1) public contributors had space to build confidence in a new topic, (2) relationships between individuals and within the project, (3) working remotely during the COVID-19 pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) requirements that co-production needs to be inclusive and accessible to everyone, (6) expectations towards data science and artificial intelligence that researchers should follow to establish public support.

CONCLUSIONS

The participatory evaluation suggests that co-production around data science and artificial intelligence can be a meaningful process that is co-owned by everyone involved.

Data sharing in neurodegenerative disease research: challenges and learnings from the innovative medicines initiative public-private partnership model

Efficient data sharing is hampered by an array of organizational, ethical, behavioral, and technical challenges, slowing research progress and reducing the utility of data generated by clinical research studies on neurodegenerative diseases. There is a particular need to address differences between public and private sector environments for research and data sharing, which have varying standards, expectations, motivations, and interests. The Neuronet data sharing Working Group was set up to understand the existing barriers to data sharing in public-private partnership projects, and to provide guidance to overcome these barriers, by convening data sharing experts from diverse projects in the IMI neurodegeneration portfolio. In this policy and practice review, we outline the challenges and learnings of the WG, providing the neurodegeneration community with examples of good practices and recommendations on how to overcome obstacles to data sharing. These obstacles span organizational issues linked to the unique structure of cross-sectoral, collaborative research initiatives, to technical issues that affect the storage, structure and annotations of individual datasets. We also identify sociotechnical hurdles, such as academic recognition and reward systems that disincentivise data sharing, and legal challenges linked to heightened perceptions of data privacy risk, compounded by a lack of clear guidance on GDPR compliance mechanisms for public-private research. Focusing on real-world, neuroimaging and digital biomarker data, we highlight particular challenges and learnings for data sharing, such as data management planning, development of ethical codes of conduct, and harmonization of protocols and curation processes. Cross-cutting solutions and enablers include the principles of transparency, standardization and co-design - from open, accessible metadata catalogs that enhance findability of data, to measures that increase visibility and trust in data reuse.

Factors Influencing the Sharing of Personal Health Data Based on the Integrated Theory of Privacy Calculus and Theory of Planned Behaviors Framework: Results of a Cross-Sectional Study of Chinese Patients in the Yangtze River Delta

BACKGROUND

The health care system in China is fragmented, and the distribution of high-quality resources remains uneven and irrational. Information sharing is essential to the development of an integrated health care system and maximizing its benefits. Nevertheless, data sharing raises concerns regarding the privacy and confidentiality of personal health information, which affect the willingness of patients to share information.

OBJECTIVE

This study aims to investigate patients' willingness to share personal health data at different levels of maternal and child specialized hospitals in China, to propose and test a conceptual model to identify key influencing factors, and to provide countermeasures and suggestions to improve the level of data sharing.

METHODS

A research framework based on the Theory of Privacy Calculus and the Theory of Planned Behavior was developed and empirically tested through a cross-sectional field survey from September 2022 to October 2022 in the Yangtze River Delta region, China. A 33-item measurement instrument was developed. Descriptive statistics, chi-square tests, and logistic regression analyses were conducted to characterize the willingness of sharing personal health data and differences by sociodemographic factors. Structural equation modeling was used to assess the reliability and validity of the measurement as well as to test the research hypotheses. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cross-sectional studies was applied for reporting results.

RESULTS

The empirical framework had a good fit with the chi-square/degree of freedom (χ2/df)=2.637, root-mean-square residual=0.032, root-mean-square error of approximation=0.048, goodness-of-fit index=0.950, and normed fit index=0.955. A total of 2060 completed questionnaires were received (response rate: 2060/2400, 85.83%). Moral motive (β=.803, P<.001), perceived benefit (β=.123, P=.04), and perceived effectiveness of government regulation (β=.110, P=.001) had a significantly positive association with sharing willingness, while perceived risk (β=-.143, P<.001) had a significant negative impact, with moral motive having the greatest impact. The estimated model explained 90.5% of the variance in sharing willingness.

CONCLUSIONS

This study contributes to the literature on personal health data sharing by integrating the Theory of Privacy Calculus and the Theory of Planned Behavior. Most Chinese patients are willing to share their personal health data, which is primarily motivated by moral concerns to improve public health and assist in the diagnosis and treatment of illnesses. Patients with no prior experience with personal information disclosure and those who have tertiary hospital visits were more likely to share their health data. Practical guidelines are provided to health policy makers and health care practitioners to encourage patients to share their personal health information.

Physicians differ in their perceptions of sensitive medical records: Survey and interview study

Physician categorizations of electronic health record (EHR) data (e.g., depression) into sensitive data categories (e.g., Mental Health) and their perspectives on the adequacy of the categories to classify medical record data were assessed. One thousand data items from patient EHR were classified by 20 physicians (10 psychiatrists paired with ten non-psychiatrist physicians) into data categories via a survey. Cluster-adjusted chi square tests and mixed models were used for analysis. 10 items were selected per each physician pair (100 items in total) for discussion during 20 follow-up interviews. Interviews were thematically analyzed. Survey item categorization yielded 500 (50.0%) agreements, 175 (17.5%) disagreements, 325 (32.5%) partial agreements. Categorization disagreements were associated with physician specialty and implied patient history. Non-psychiatrists selected significantly (p = .016) more data categories than psychiatrists when classifying data items. The endorsement of Mental Health and Substance Use categories were significantly (p = .001) related for both provider types. During thematic analysis, Encounter Diagnosis (100%), Problems (95%), Health Concerns (90%), and Medications (85%) were discussed the most when deciding the sensitivity of medical information. Most (90.0%) interview participants suggested adding additional data categories. Study findings may guide the evolution of digital patient-controlled granular data sharing technology and processes.

Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy

A large amount of personal health data that is highly valuable to the scientific community is still not accessible or requires a lengthy request process due to privacy concerns and legal restrictions. As a solution, synthetic data has been studied and proposed to be a promising alternative to this issue. However, generating realistic and privacy-preserving synthetic personal health data retains challenges such as simulating the characteristics of the patients' data that are in the minority classes, capturing the relations among variables in imbalanced data and transferring them to the synthetic data, and preserving individual patients' privacy. In this paper, we propose a differentially private conditional Generative Adversarial Network model (DP-CGANS) consisting of data transformation, sampling, conditioning, and network training to generate realistic and privacy-preserving personal data. Our model distinguishes categorical and continuous variables and transforms them into latent space separately for better training performance. We tackle the unique challenges of generating synthetic patient data due to the special data characteristics of personal health data. For example, patients with a certain disease are typically the minority in the dataset and the relations among variables are crucial to be observed. Our model is structured with a conditional vector as an additional input to present the minority class in the imbalanced data and maximally capture the dependency between variables. Moreover, we inject statistical noise into the gradients in the networking training process of DP-CGANS to provide a differential privacy guarantee. We extensively evaluate our model with state-of-the-art generative models on personal socio-economic datasets and real-world personal health datasets in terms of statistical similarity, machine learning performance, and privacy measurement. We demonstrate that our model outperforms other comparable models, especially in capturing the dependence between variables. Finally, we present the balance between data utility and privacy in synthetic data generation considering the different data structures and characteristics of real-world personal health data such as imbalanced classes, abnormal distributions, and data sparsity.

One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case

Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other's health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a "how-to" analysis of Tracy and Mego's daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This "how-to" can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and "how-to's" to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management.

Patient and researcher stakeholder preferences for use of electronic health record data: a qualitative study to guide the design and development of a platform to honor patient preferences

OBJECTIVE

This qualitative study aimed to understand patient and researcher perspectives regarding consent and data-sharing preferences for research and a patient-centered system to manage consent and data-sharing preferences.

MATERIALS AND METHODS

We conducted focus groups with patient and researcher participants recruited from three academic health centers via snowball sampling. Discussions focused on perspectives on the use of electronic health record (EHR) data for research. Themes were identified through consensus coding, starting from an exploratory framework.

RESULTS

We held two focus groups with patients (n = 12 patients) and two with researchers (n = 8 researchers). We identified two patient themes (1-2), one theme common to patients and researchers (3), and two researcher themes (4-5). Themes included (1) motivations for sharing EHR data, (2) perspectives on the importance of data-sharing transparency, (3) individual control of personal EHR data sharing, (4) how EHR data benefits research, and (5) challenges researchers face using EHR data.

DISCUSSION

Patients expressed a tension between the benefits of their data being used in studies to benefit themselves/others and avoiding risk by limiting data access. Patients resolved this tension by acknowledging they would often share their data but wanted greater transparency on its use. Researchers expressed concern about incorporating bias into datasets if patients opted out.

CONCLUSIONS

A research consent and data-sharing platform must consider two competing goals: empowering patients to have more control over their data and maintaining the integrity of secondary data sources. Health systems and researchers should increase trust-building efforts with patients to engender trust in data access and use.

Commercializing Personal Health Information: A Critical Qualitative Content Analysis of Documents Describing Proprietary Primary Care Databases in Canada

BACKGROUND

Commercial data brokers have amassed large collections of primary care patient data in proprietary databases. Our study objective was to critically analyze how entities involved in the collection and use of these records construct the value of these proprietary databases. We also discuss the implications of the collection and use of these databases.

METHODS

We conducted a critical qualitative content analysis using publicly available documents describing the creation and use of proprietary databases containing Canadian primary care patient data. We identified relevant commercial data brokers, as well as entities involved in collecting data or in using data from these databases. We sampled documents associated with these entities that described any aspect of the collection, processing, and use of the proprietary databases. We extracted data from each document using a structured data tool. We conducted an interpretive thematic content analysis by inductively coding documents and the extracted data.

RESULTS

We analyzed 25 documents produced between 2013 and 2021. These documents were largely directed at the pharmaceutical industry, as well as shareholders, academics, and governments. The documents constructed the value of the proprietary databases by describing extensive, intimate, detailed patient-level data holdings. They provided examples of how the databases could be used by pharmaceutical companies for regulatory approval, marketing and understanding physician behaviour. The documents constructed the value of these data more broadly by claiming to improve health for patients, while also addressing risks to privacy. Some documents referred to the trade-offs between patient privacy and data utility, which suggests these considerations may be in tension.

CONCLUSION

Documents in our analysis positioned the proprietary databases as socially legitimate and valuable, particularly to pharmaceutical companies. The databases, however, may pose risks to patient privacy and contribute to problematic drug promotion. Solutions include expanding public data repositories with appropriate governance and external regulatory oversight.

From intention to action: The factors affecting health data sharing intention and action

INTRODUCTION

Effective prevention and treatment of diseases requires utilization of health-related lifestyle data, which has thus become increasingly important. According to some studies, participants were willing to share their health data for use in medical care and research. Although intention does not always accurately reflect action, few studies have examined the question of whether data-sharing intention leads to data-sharing action.

OBJECTIVE

The aim of this study was to examine the extent of actualizing data-sharing intention to data-sharing action and to identify the factors that influence data-sharing intention and action.

METHODS

A web-based survey of members of a university examined the data-sharing intention and issues of concern when making decisions on data sharing. The participants were asked to deposit their armband data for use in research at the end of the survey. A comparison of data-sharing intention and action in relation to the participants' characteristics was performed. Factors having a significant effect on data-sharing intention and action were identified using logistic regressions.

RESULTS

Of 386 participants, 294 expressed willingness to share health data. However, only 73 participants deposited their armband data. The primary reason for refusal to deposit armband data was the inconvenience of the data transfer process (56.3%). Appropriate compensation had a significant effect on data-sharing intention (OR: 3.3, CI: 1.86-5.75) and action (OR: 2.8, CI: 1.14-8.21). The compensation for data sharing (OR:2.8, CI:1.14-8.21) and familiarity with data (OR:3.1, CI:1.36-8.21) were significant predictors of data sharing action, however, data-sharing intention was not (OR: 1.5, CI:0.65-3.72).

CONCLUSION

Despite expressing willingness to share their health data, the participants' intention was not actualized to data-sharing behavior for depositing armband data. Implementation of a streamlined data transfer process and providing appropriate compensation might facilitate data-sharing. These findings could be useful in development of strategies to facilitate sharing and reuse of health data.

Exploring patient perspectives on the secondary use of their personal health information: an interview study

BACKGROUND

The increased digitalisation of health records has resulted in increased opportunities for the secondary use of health information for advancing healthcare. Understanding how patients want their health information used is vital to ensure health services use it in an appropriate and patient-informed manner. The aim of this study was to explore patient perceptions of the use of their health information beyond their immediate care.

METHODS

Semi-structured in-depth interviews were conducted with current users of health services in Aotearoa New Zealand. Different scenarios formed the basis of the discussions in the interviews covering different types of information use (current practice, artificial intelligence and machine learning, clinical calculators, research, registries, and public health surveillance). Transcripts were analysed using thematic analysis.

RESULTS

Twelve interviews were conducted with individual's representative of key ethnicity groups and rural/urban populations, and at the time of recruitment, had been accessing a diverse range of health services. Participants ranged from high users of health care (e.g., weekly dialysis) through to low users (e.g., one-off presentation to the emergency department). Four interrelated overarching themes were identified from the transcripts describing the main issues for participants: helping others, sharing of data is important, trust, and respect.

CONCLUSIONS

People currently engaging with health services are supportive of their health information being used to help others, advance science, and contribute to the greater good but their support is conditional. People need to be able to trust the health service to protect, care for, and respect their health information and ensure no harm comes from its use. This study has identified key considerations for services and researchers to reflect on when using patient health information for secondary purposes to ensure they use it in a patient-informed way.

TRIAL REGISTRATION

NA.

Patient perspectives on data sharing regarding implementing and using artificial intelligence in general practice - a qualitative study

BACKGROUND

Due to more elderly and patients with complex illnesses, there is an increasing pressure on the healthcare system. General practice especially feels this pressure as being the first point of contact for the patients. Developments in digitalization have undergone fast progress and data-driven artificial intelligence (AI) has shown great potential for use in general practice. To develop AI as a support tool for general practitioners (GPs), access to patients' health data is needed, but patients have concerns regarding data sharing. Furthermore, studies show that trust is important regarding the patient-GP relationship, data sharing, and AI. The aim of this paper is to uncover patient perspectives on trust regarding the patient-GP relationship, data sharing and AI in general practice.

METHOD

This study investigated 10 patients' perspectives through qualitative interviews and written vignettes were chosen to elicit the patients (interviewees) perspectives on topics that they were not familiar with prior to the interviews. The study specifically investigated perspectives on 1) The patient-GP relationship, 2) data sharing regarding developing AI for general practice, and 3) implementation and use of AI in general practice using thematic analysis. The study took place in the North Denmark Region and the interviewees included had to be registered in general practice and be above 18 years in age. We included four men between 25 to 74 years in age and six women between 27 to 46 years in age.

RESULTS

The interviewees expressed a high level of trust towards their GP and were willing to share their health data with their GP. The interviewees believed that AI could be a great help to GPs if used as a support tool in general practice. However, it was important for the interviewees that the GP would still be the primary decision maker.

CONCLUSION

Patients may be willing to share health data to help implement and use AI in general practice. If AI is implemented in a way that preserves the patient-GP relationship and used as a support tool for the GP, our results indicate that patients may be positive towards the use of AI in general practice.

Digital technology and patient and public involvement (PPI) in routine care and clinical research-A pilot study

BACKGROUND

Patient and public involvement (PPI) has growing impact on the design of clinical care and research studies. There remains underreporting of formal PPI events including views related to using digital tools. This study aimed to assess the feasibility of hosting a hybrid PPI event to gather views on the use of digital tools in clinical care and research.

METHODS

A PPI focus day was held following local procedures and published recommendations related to advertisement, communication and delivery. Two exemplar projects were used as the basis for discussions and qualitative and quantitative data was collected.

RESULTS

32 individuals expressed interest in the PPI day and 9 were selected to attend. 3 participated in person and 6 via an online video-calling platform. Selected written and verbal feedback was collected on two digitally themed projects and on the event itself. The overall quality and interactivity for the event was rated as 4/5 for those who attended in person and 4.5/5 and 4.8/5 respectively, for those who attended remotely.

CONCLUSIONS

A hybrid PPI event is feasible and offers a flexible format to capture the views of patients. The overall enthusiasm for digital tools amongst patients in routine care and clinical research is high, though further work and standardised, systematic reporting of PPI events is required.

Understanding how to build a social licence for using novel linked datasets for planning and research in Kent, Surrey and Sussex: results of deliberative focus groups

Introduction

Digital programmes in the newly created NHS integrated care boards (ICBs) in the United Kingdom mean that curation and linkage of anonymised patient data is underway in many areas for the first time. In Kent, Surrey and Sussex (KSS), in Southeast England, public health teams want to use these datasets to answer strategic population health questions, but public expectations around use of patient data are unknown.

Objectives

We aimed to engage with citizens of KSS to gather their views and expectations of data linkage and re-use, through deliberative discussions.

Methods

We held five 3-hour deliberative focus groups with 79 citizens of KSS, presenting information about potential uses of data, safeguards, and mechanisms for public involvement in governance and decision making about datasets. After each presentation, participants discussed their views in facilitated small groups which were recorded, transcribed and analysed thematically.

Results

The focus groups generated 15 themes representing participants' views on the benefits, risks and values for safeguarding linked data. Participants largely supported use of patient data to improve health service efficiency and resource management, preventative services and out of hospital care, joined-up services and information flows. Most participants expressed concerns about data accuracy, breaches and hacking, and worried about commercial use of data. They suggested that transparency of data usage through audit trails and clear information about accountability, ensuring data re-use does not perpetuate stigma and discrimination, ongoing, inclusive and valued involvement of the public in dataset decision-making, and a commitment to building trust, would meet their expectations for responsible data use.

Conclusions

Participants were largely favourable about the proposed uses of patient linked datasets but expected a commitment to transparency and public involvement. Findings were mapped to previous tenets of social license and can be used to inform ICB digital programme teams on how to proceed with use of linked datasets in a trustworthy and socially acceptable way.

Setting up a Governance Framework for Secondary Use of Routine Health Data in Nursing Homes: Development Study Using Qualitative Interviews

BACKGROUND

In the nursing home sector, reusing routinely recorded data from electronic health records (EHRs) for knowledge development and quality improvement is still in its infancy. Trust in appropriate and responsible reuse is crucial for patients and nursing homes deciding whether to share EHR data for these purposes. A data governance framework determines who may access the data, under what conditions, and for what purposes. This can help obtain that trust. Although increasing attention is being paid to data governance in the health care sector, little guidance is available on development and implementation of a data governance framework in practice.

OBJECTIVE

This study aims to describe the development process of a governance framework for the "Registry Learning from Data in Nursing Homes," a national registry for EHR data on care delivered by nursing home physicians (in Dutch: specialist ouderengeneeskunde) in Dutch nursing homes-to allow data reusage for research and quality improvement of care.

METHODS

Relevant stakeholders representing practices, policies, and research in the nursing home sector were identified. Semistructured interviews were conducted with 20 people from 14 stakeholder organizations. The main aim of the interviews was to explore stakeholders' perspectives regarding the Registry's aim, data access criteria, and governing bodies' tasks and composition. Interview topics and analyses were guided by 8 principles regarding governance for reusing health data, as described in the literature. Interview results, together with legal advice and consensus discussions by the Registry's consortium partners, were used to shape the rules, regulations, and governing bodies of the governance framework.

RESULTS

Stakeholders valued the involvement of nursing home residents and their representatives, nursing home physicians, nursing homes' boards of directors, and scientists and saw this as a prerequisite for a trustworthy data governance framework. For the Registry, involvement of these groups can be achieved through a procedure in which residents can provide their consent or objection to the reuse of the data, transparency about the decisions made, and providing them a position in a governing body. In addition, a data request approval procedure based on predefined assessment criteria indicates that data reuse by third parties aligns with the aims of the Registry, benefits the nursing home sector, and protects the privacy of data subjects.

CONCLUSIONS

The stakeholders' views, expertise, and knowledge of other frameworks and relevant legislation serve to inform the application of governance principles to the contexts of both the nursing home sector and the Netherlands. Many different stakeholders were involved in the development of the Registry Learning from Data in Nursing Homes' governance framework and will continue to be involved. Engagement of the full range of stakeholders in an early stage of governance framework development is important to generate trust in appropriate and responsible data reuse.

Application of the program for artificial intelligence analytics of paper text and segmentation by specified parameters in clinical practice

The development of novel technologies using elements of artificial intelligence (AI) in medicine is addressed to practical clinical implementation and provision of key issues, including improvement in the use of routine clinical data, aimed at practical relevance, standardization, confidentiality and patient safety.

Aim. To evaluate the effectiveness of the RuPatient electronic heart record (EHR) system in real clinical practice for extracting and structuring medical data.

Material and methods. Extraction and recognition of data using EHR from various following sources: outpatient records, statements, routine medical reports, epicrisis and other structured and unstructured medical information based on the developed technology of intelligent text analytics, optical character recognition, for specified words and phrases, and the use of machine learning elements. A particular criterion for evaluating the effectiveness of EHR is the time spent on filling out electronic medical records compared to real clinical practice.

Results. The time of entering and processing information by the recognition system of medical documentation included in the RuPatient EHR was shorter than in standard practice (20,3±1,4 minutes, 25,1±1,5 minutes, respectively, p<0,001), the average time of recognition of documents was 30±4,3 seconds. During the ROC analysis, we determined that the threshold value that allows high accuracy to recognize images of discharge epicrisis using the RuPatient system was 83,5% with an area under the curve (AUC) value of 0,76.

Conclusions. The developed RuPatient EHR has a medical documentation recognition module for creating structured data based on AI technology elements and can be used in creating an electronic medical history and accumulation of structured data for the implementation of tasks for the practical and scientific use of big data and AI projects in medicine. When using the RuPatient system, the burden on medical staff during document management can be reduced and access to primary medical information simplified.

Data and model bias in artificial intelligence for healthcare applications in New Zealand

Introduction

Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand.

Methods

This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME.

Results

This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research.

Discussions

This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

Patients and Members of the Public’s Wishes Regarding Transparency in the Context of Secondary Use of Health Data: A Scoping Review (Preprint)

BACKGROUND

Secondary use of health data has reached unequaled potential to improve health systems governance, knowledge, and clinical care. Transparency regarding this secondary use is frequently cited as necessary to address deficits in trust and conditional support and to increase patient awareness.

OBJECTIVE

We aimed to review the current published literature to identify different stakeholders' perspectives and recommendations on what information patients and members of the public want to learn about the secondary use of health data for research purposes and how and in which situations.

METHODS

Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we conducted a scoping review using Medline, CINAHL, PsycINFO, Scopus, Cochrane Library, and PubMed databases to locate a broad range of studies published in English or French until November 2022. We included articles reporting a stakeholder's perspective or recommendations of what information patients and members of the public want to learn about the secondary use of health data for research purposes and how or in which situations. Data were collected and analyzed with an iterative thematic approach using NVivo.

RESULTS

Overall, 178 articles were included in this scoping review. The type of information can be divided into generic and specific content. Generic content includes information on governance and regulatory frameworks, technical aspects, and scientific aims. Specific content includes updates on the use of one's data, return of results from individual tests, information on global results, information on data sharing, and how to access one's data. Recommendations on how to communicate the information focused on frequency, use of various supports, formats, and wording. Methods for communication generally favored broad approaches such as nationwide publicity campaigns, mainstream and social media for generic content, and mixed approaches for specific content including websites, patient portals, and face-to-face encounters. Content should be tailored to the individual as much as possible with regard to length, avoidance of technical terms, cultural competence, and level of detail. Finally, the review outlined 4 major situations where communication was deemed necessary: before a new use of data, when new test results became available, when global research results were released, and in the advent of a breach in confidentiality.

CONCLUSIONS

This review highlights how different types of information and approaches to communication efforts may serve as the basis for achieving greater transparency. Governing bodies could use the results: to elaborate or evaluate strategies to educate on the potential benefits; to provide some knowledge and control over data use as a form of reciprocity; and as a condition to engage citizens and build and maintain trust. Future work is needed to assess which strategies achieve the greatest outreach while striking a balance between meeting information needs and use of resources.

EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation

BACKGROUND

Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability.

RESULTS

To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor.

CONCLUSIONS

We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.

Perceptions, attitudes, and willingness of the public in low- and middle-income countries of the Arab region to participate in biobank research

Population-based genomics studies have proven successful in identifying genetic variants associated with diseases. High-quality biospecimens linked with informative health data from diverse segments of the population have made such research possible. However, the success of biobank research depends on the willingness of the public to participate in this type of research. We aimed to explore the factors associated with the willingness of the public to participate in biobank research from four low- and middle-income countries in the Arab region (Egypt, Jordan, Morocco, and Sudan). We used a previously validated questionnaire to assess several constructs that included the public's perceptions, attitudes, and willingness to participate in biobank research. We recruited 967 participants. More than half did not have prior awareness of biobanks. Participants' willingness to donate biospecimens and health data was less than 10%. Our results also showed that participants harbored concerns with trust, privacy, and with data-sharing involving international researchers. Predictors of willingness to participate in biobank research included no previous involvement in research and positive attitudes toward biobanks. Finally, our study showed several differences between the four countries regarding several of the investigated constructs. We conclude there should be additional efforts to raise public awareness and enhance perceptions of the public in biobanking research to enhance trust. We further recommend qualitative research to explore the underlying factors that contribute to the public's concerns with international data sharing that would enhance global health.

Use of Population-Level Administrative Data in Developmental Science

Population-level administrative data-data on individuals' interactions with administrative systems (e.g., health, criminal justice, and education)-have substantially advanced our understanding of life-course development. In this review, we focus on five areas where research using these data has made significant contributions to developmental science: (a) understanding small or difficult-to-study populations, (b) evaluating intergenerational and family influences, (c) enabling estimation of causal effects through natural experiments and regional comparisons, (d) identifying individuals at risk for negative developmental outcomes, and (e) assessing neighborhood and environmental influences. Further advances will be made by linking prospective surveys to administrative data to expand the range of developmental questions that can be tested; supporting efforts to establish new linked administrative data resources, including in developing countries; and conducting cross-national comparisons to test findings' generalizability. New administrative data initiatives should involve consultation with population subgroups including vulnerable groups, efforts to obtain social license, and strong ethical oversight and governance arrangements.

Use of the Hashtag #DataSavesLives on Twitter: Exploratory and Thematic Analysis

Background

“Data Saves Lives” is a public engagement campaign that highlights the benefits of big data research and aims to establish public trust for this emerging research area.

Objective

This study explores how the hashtag #DataSavesLives is used on Twitter. We focused on the period when the UK government and its agencies adopted #DataSavesLives in an attempt to support their plans to set up a new database holding National Health Service (NHS) users’ medical data.

Methods

Public tweets published between April 19 and July 15, 2021, using the hashtag #DataSavesLives were saved using NCapture for NVivo 12. All tweets were coded twice. First, each tweet was assigned a positive, neutral, or negative attitude toward the campaign. Second, inductive thematic analysis was conducted. The results of the thematic analysis were mapped under 3 models of public engagement: deficit, dialogue, and participatory.

Results

Of 1026 unique tweets available for qualitative analysis, discussion around #DataSavesLives was largely positive (n=716, 69.8%) or neutral (n=276, 26.9%) toward the campaign with limited negative attitudes (n=34, 3.3%). Themes derived from the #DataSavesLives debate included ethical sharing, proactively engaging the public, coproducing knowledge with the public, harnessing potential, and gaining an understanding of big data research. The Twitter discourse was largely positive toward the campaign. The hashtag is predominantly used by similar-minded Twitter users to share information about big data projects and to spread positive messages about big data research when there are public controversies. The hashtag is generally used by organizations and people supportive of big data research. Tweet authors recognize that the public should be proactively engaged and involved in big data projects. The campaign remains UK centric. The results indicate that the communication around big data research is driven by the professional community and remains 1-way as members of the public rarely use the hashtag.

Conclusions

The results demonstrate the potential of social media but draws attention to hashtag usage being generally confined to “Twitter bubbles”: groups of similar-minded Twitter users.

The Role of Pain Catastrophizing, Emotional Intelligence, and Pain Intensity in the Quality of Life of Cancer Patients with Chronic Pain

Pain catastrophizing (PC) is a negative cognitive distortion to actual or anticipated pain. This study aims to investigate the relationship between pain catastrophizing, emotional intelligence, pain intensity, and quality of life (QoL) in cancer patients with chronic pain. Eighty-nine outpatients with chronic pain attending pain clinics and palliative care units were recruited. Participants were men (42.7%) and women (57.3%) with an average age of 56.44 years (SD = 14.82). Self-report psychological measures were completed, including a measure of emotional intelligence, a standard measure of PC, a scale assessing pain intensity, and a scale measuring QoL. The PC scale was found to assess three correlated yet different dimensions of pain catastrophizing (helplessness, magnification, and rumination). Moreover, as expected, patients with PC scale scores ≥ 30 had lower scores in functional QoL dimensions and higher scores in the fatigue, pain, and insomnia symptom dimensions. Regression analyses demonstrated that PC (B = − 0.391, p = 0.004), pain intensity (B = − 1.133, p < 0.001), and education (B = 2.915, p = 0.017) remained the only significant variables related to QoL, when controlling for demographic and clinical confounders. Regarding mediating effects, PC and pain intensity were jointly found to be significant mediators in the relationship between emotional intelligence and QoL. Results are discussed in the context of the clinical implications regarding interventions designed to improve cancer patients’ quality of life and offer new insight, understanding, and evaluation targets in the field of pain management.

Patients' and Publics' Preferences for Data-Intensive Health Research Governance: Survey Study

Background

Patients and publics are generally positive about data-intensive health research. However, conditions need to be fulfilled for their support. Ensuring confidentiality, security, and privacy of patients’ health data is pivotal. Patients and publics have concerns about secondary use of data by commercial parties and the risk of data misuse, reasons for which they favor personal control of their data. Yet, the potential of public benefit highlights the potential of building trust to attenuate these perceptions of harm and risk. Nevertheless, empirical evidence on how conditions for support of data-intensive health research can be operationalized to that end remains scant.

Objective

This study aims to inform efforts to design governance frameworks for data-intensive health research, by gaining insight into the preferences of patients and publics for governance policies and measures.

Methods

We distributed a digital questionnaire among a purposive sample of patients and publics. Data were analyzed using descriptive statistics and nonparametric inferential statistics to compare group differences and explore associations between policy preferences.

Results

Study participants (N=987) strongly favored sharing their health data for scientific health research. Personal decision-making about which research projects health data are shared with (346/980, 35.3%), which researchers/organizations can have access (380/978, 38.9%), and the provision of information (458/981, 46.7%) were found highly important. Health data–sharing policies strengthening direct personal control, like being able to decide under which conditions health data are shared (538/969, 55.5%), were found highly important. Policies strengthening collective governance, like reliability checks (805/967, 83.2%) and security safeguards (787/976, 80.6%), were also found highly important. Further analysis revealed that participants willing to share health data, to a lesser extent, demanded policies strengthening direct personal control than participants who were reluctant to share health data. This was the case for the option to have health data deleted at any time (P<.001) and the ability to decide the conditions under which health data can be shared (P<.001). Overall, policies and measures enforcing conditions for support at the collective level of governance, like having an independent committee to evaluate requests for access to health data (P=.02), were most strongly favored. This also applied to participants who explicitly stressed that it was important to be able to decide the conditions under which health data can be shared, for instance, whether sanctions on data misuse are in place (P=.03).

Conclusions

This study revealed that both a positive attitude toward health data sharing and demand for personal decision-making abilities were associated with policies and measures strengthening control at the collective level of governance. We recommend pursuing the development of this type of governance policy. More importantly, further study is required to understand how governance policies and measures can contribute to the trustworthiness of data-intensive health research.