Potentials and Challenges of the Health Data Cooperative Model

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.

Association of Digital Engagement With Relaxation Tools and Stress Level Reduction: Retrospective Cohort Study

BACKGROUND

Stress is an emotional response caused by external triggers and is a high-prevalence global problem affecting mental and physical health. Several different digital therapeutic solutions are effective for stress management. However, there is limited understanding of the association between relaxation components and stress levels when using a digital app.

OBJECTIVE

This study investigated the contribution of relaxation tools to stress levels over time. We hypothesized that participation in breathing exercises and cognitive behavioral therapy-based video sessions would be associated with a reduction in stress levels. We also hypothesized a significant reduction specifically in participants' perceived sense of burden and lack of productivity when engaged with breathing exercises and video sessions.

METHODS

Stress levels were evaluated in a real-world data cohort using a behavioral health app for digital intervention and monitoring change. This retrospective real-world analysis of users on a mobile platform-based treatment followed users (N=490) who started with moderate and above levels of stress and completed at least 2 stress assessments. The levels of stress were tracked throughout the first 10 weeks. A piecewise mixed effects model was applied to model the trajectories of weekly stress mean scores in 2 time segments (1-6 weeks and 6-10 weeks). Next, a simple slope analysis was used for interpreting interactions probing the moderators: breathing exercises and video sessions. Piecewise mixed-effects models were also used to model the trajectories of specific perceived stress item rates in the stress questionnaire in the 2 segments (1-6 weeks and 6-10 weeks) and whether they are moderated by the relaxation engagements. Simple slope analysis was also used here for the interpretation of the interactions.

RESULTS

Analysis revealed a significant decrease in stress symptoms (β=-.25; 95% CI -0.32 to -0.17; P<.001) during the period of 1-6 weeks of app use that was maintained during the period of 6-10 weeks. Breathing exercises significantly moderated the reduction in stress symptoms during the period of 1-6 weeks (β=-.07; 95% CI -0.13 to -0.01; P=.03), while engagement in digital video sessions did not moderate stress scores. Engagement in digital video sessions, as well as breathing exercises, significantly moderated the reduction in perceived sense of burden and lack of productivity during weeks 1-6 and remained stable during weeks 6-10 on both items.

CONCLUSIONS

This study sheds light on the association between stress level reduction and specific components of engagement in a digital health app, breathing exercises, and cognitive behavioral therapy-based video sessions. Our findings provide a basis for further investigation of current and moderating factors that contribute to the personalization of digital intervention. In addition, results may aid in developing a more comprehensive understanding of how digital intervention tools work for mental health and for whom they are most effective.

What can data trusts for health research learn from participatory governance in biobanks?

New models of data governance for health data are a focus of growing interest in an era of challenge to the social licence. In this article, we reflect on what the data trust model, which is founded on principles of participatory governance, can learn from experiences of involving and engagement of members of the public and participants in the governance of large-scale biobanks. We distinguish between upstream and ongoing governance models, showing how they require careful design and operation if they are to deliver on aspirations for deliberation and participation. Drawing on this learning, we identify a set of considerations important to future design for data trusts as they seek to ensure just, proportionate and fair governance. These considerations relate to the timing of involvement of participants, patterns of inclusion and exclusion, and responsiveness to stakeholder involvement and engagement. We emphasise that the evolution of governance models for data should be matched by a commitment to evaluation.

'It’s not something you can take in your hands'. Swiss experts’ perspectives on health data ownership: an interview-based study

Objectives

The evolution of healthcare and biomedical research into data-rich fields has raised several questions concerning data ownership. In this paper, we aimed to analyse the perspectives of Swiss experts on the topic of health data ownership and control.

Design

In our qualitative study, we selected participants through purposive and snowball sampling. Interviews were recorded, transcribed verbatim and then analysed thematically.

Setting

Semi-structured interviews were conducted in person, via phone or online.

Participants

We interviewed 48 experts (researchers, policy makers and other stakeholders) of the Swiss health-data framework.

Results

We identified different themes linked to data ownership. These include: (1) the data owner: data-subjects versus data-processors; (2) uncertainty about data ownership; (3) labour as a justification for data ownership and (4) the market value of data. Our results suggest that experts from Switzerland are still divided about who should be the data owner and also about what ownership would exactly mean. There is ambivalence between the willingness to acknowledge patients as the data owners and the fact that the effort made by data-processors (eg, researchers) to collect and manage the data entitles them to assert ownership claims towards the data themselves. Altogether, a tendency to speak about data in market terms also emerged.

Conclusions

The development of a satisfactory account of data ownership as a concept to organise the relationship between data-subjects, data-processors and data themselves is an important endeavour for Switzerland and other countries who are developing data governance in the healthcare and research domains. Setting clearer rules on who owns data and on what ownership exactly entails would be important. If this proves unfeasible, the idea that health data cannot truly belong to anyone could be promoted. However, this will not be easy, as data are seen as an asset to control and profit from.

Collecting, exploring and sharing personal data: Why, how and where

New, multi-channel personal data sources (like heart rate, sleep patterns, travel patterns, or social activities) are enabled by ever increased availability of miniaturised technologies embedded within smartphones and wearables. These data sources enable personal self-management of lifestyle choices (e.g., exercise, move to a bike-friendly area) and, on a large scale, scientific discoveries to improve health and quality of life. However, there are no simple and reliable ways for individuals to securely collect, explore and share these sources. Additionally, much data is also wasted, especially when the technology provider ceases to exist, leaving the users without any opportunity to retrieve own datasets from “dead” devices or systems. Our research reveals evidence of what we term human data bleeding and offers guidance on how to address current issues by reasoning upon five core aspects, namely technological, financial, legal, institutional and cultural factors. To this end, we present preliminary specifications of an open platform for personal data storage and quality of life research. The Open Health Archive (OHA) is a platform that would support individual, community and societal needs by facilitating collecting, exploring and sharing personal health and QoL data.

Digital health for optimal supportive care in oncology: benefits, limits, and future perspectives

BACKGROUND

Digital health provides solutions that capture patient-reported outcomes (PROs) and allows symptom monitoring and patient management. Digital therapeutics is the provision to patients of evidence-based therapeutic interventions through software applications aimed at prevention, monitoring, management, and treatment of symptoms and diseases or for treatment optimization. The digital health solutions collecting PROs address many unmet needs, including access to care and reassurance, increase in adherence and treatment efficacy, and decrease in hospitalizations. With current developments in oncology including increased availability of oral drugs and reduced availability of healthcare professionals, these solutions offer an innovative approach to optimize healthcare resource utilization.

DESIGN

This scoping review clarifies the role and impact of the digital health solutions in oncology supportive care, with a view of the current segmentation according to their technical features (connection to sensors, PRO collection, remote monitoring, self-management in real time…), and identifies evidence from clinical studies published about their benefits and limitations and drivers and barriers to adoption. A qualitative summary is presented.

RESULTS

Sixty-six studies were identified and included in the qualitative synthesis. Studies supported the use of 38 digital health solutions collecting ePROs and allowing remote monitoring, with benefits to patients regarding symptom reporting and management, reduction in symptom distress, decrease in unplanned hospitalizations and related costs and improved quality of life and survival. Among those 38 solutions 21 provided patient self-management with impactful symptom support, improvement of QoL, usefulness and reassurance. Principal challenges are in developing and implementing digital solutions to suit most patients, while ensuring patient compliance and adaptability for use in different healthcare systems and living environments.

CONCLUSIONS

There is growing evidence that digital health collecting ePROs provide benefits to patients related to clinical and health economic endpoints. These digital solutions can be integrated into routine supportive care in oncology practice to provide improved patient-centered care.

Emerging health data platforms: From individual control to collective data governance

Health data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how “traditional” health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.

Community-based Health Data Cooperatives Towards Improving the Immigrant Community Health: A Scoping Review to Inform Policy and Practice

Background

In the case of immigrant health and wellness, data are the key limiting factor, where comprehensive national knowledge on immigrant health and health service utilisation is limited. New data and data silos are an inherent response to the increase in technology in the collection and storage of data. The Health Data Cooperative (HDC) model allows members to contribute, store, and manage their health-related information, and members are the rightful data owners and decision-makers to data sharing (e g. research communities, commercial entities, government bodies).

Objective

This review attempts to scope the literature on HDC and fulfill the following objectives: 1) identify and describe the type of literature that is available on the HDC model; 2) describe the key themes related to HDCs; and 3) describe the benefits and challenges related to the HDC model.

Methods

We conducted a scoping review using the five-stage framework outlined by Arskey and O’Malley to systematically map literature on HDCs using two search streams: 1) a database and grey literature search; and 2) an internet search. We included all English records that discussed health data cooperative and related key terms. We used a thematic analysis to collate information into comprehensive themes.

Results

Through a comprehensive screening process, we found 22 database and grey literature records, and 13 Internet search records. Three major themes that are important to stakeholders include data ownership, data security, and data flow and infrastructure.

Conclusions

The results of this study are an informative first step to the study of the HDC model, or an establishment of a HDC in immigrant communities.

Key words

community health, health data, cooperative, and citizen data empowermen

The Limits of Empowerment: How to Reframe the Role of mHealth Tools in the Healthcare Ecosystem

This article highlights the limitations of the tendency to frame health- and wellbeing-related digital tools (mHealth technologies) as empowering devices, especially as they play an increasingly important role in the National Health Service (NHS) in the UK. It argues that mHealth technologies should instead be framed as digital companions. This shift from empowerment to companionship is advocated by showing the conceptual, ethical, and methodological issues challenging the narrative of empowerment, and by arguing that such challenges, as well as the risk of medical paternalism, can be overcome by focusing on the potential for mHealth tools to mediate the relationship between recipients of clinical advice and givers of clinical advice, in ways that allow for contextual flexibility in the balance between patiency and agency. The article concludes by stressing that reframing the narrative cannot be the only means for avoiding harm caused to the NHS as a healthcare system by the introduction of mHealth tools. Future discussion will be needed on the overarching role of responsible design.

Trusts, co-ops, and crowd workers: Could we include crowd data workers as stakeholders in data trust design?

Data trusts have been proposed as a mechanism through which data can be more readily exploited for a variety of aims, including economic development and social-benefit goals such as medical research or policy-making. Data trusts, and similar data governance mechanisms such as data co-ops, aim to facilitate the use and re-use of datasets across organizational boundaries and, in the process, to protect the interests of stakeholders such as data subjects. However, the current discourse on data trusts does not acknowledge another common stakeholder in the data value chain—the crowd workers who are employed to collect, validate, curate, and transform data. In this paper, we report on a preliminary qualitative investigation into how crowd data workers themselves feel datasets should be used and governed. We find that while overall remuneration is important to those workers, they also value public-benefit data use but have reservations about delayed remuneration and the trustworthiness of both administrative processes and the crowd itself. We discuss the implications of our findings for how data trusts could be designed, and how data trusts could be used to give crowd workers a more enduring stake in the product of their work.

Challenges of Implementing Picture Archiving and Communication System in Multiple Hospitals: Perspectives of Involved Staff and Users

Today, despite the advantages of the PACS system, its implementation in some healthcare organizations faces many challenges. One of the important factors in the successful implementation of a PACS system is identifying and prioritizing the challenges from the perspectives of involved staff and user of this system. Therefore, the aim of this study was to determine and compare the challenges of implementing PACS from perspectives these users in educational hospitals. This study was conducted on all IT and medical equipment staff, and radiology residents (n = 140) in Kerman University of Medical Sciences (KUMS) and Shiraz University of Medical Sciences (SUMS) in 2016. The data were collected through two researcher-made questionnaires. Their validity was approved by radiologists, IT staff, and medical informatics specialists and their reliability through calculation of Cronbach's Alpha (0.969 and 0.795). We used Multivariate Analysis of Variance (MANOVA) to compare the scores given by three groups of participants in the challenges and Univariate Analysis of Variance (ANOVA) to compare the scores in two universities. The participants believed that technical challenges were more important than other challenges (x̄=3.74, SD = 0.7). IT experts (x̄=3.87, SD = 1) and radiology residents (x̄=3.95, SD = 0.9) gave the higher scores to the "shortage of high quality monitors" factor and medical equipment experts (x̄=4.26, SD = 0.87) to the "low speed of communication networks" factor among all technical challenges. The mean scores given to technical (x̄=76.1, SD = 13.5) and managerial (x̄=16, SD = 5.9) challenges in SUMS were more than the scores of the same challenges in KUMS (x̄=69.9, SD = 15.7) and (x̄=11.9, SD = 6.4) (p < 0.05). The technical challenges are the most common challenges to PACS implementation, and different universities experience different levels of technical challenges. Eliminating implementation challenges can reduce the risk of failure in the utilization process. Based on the results of this study, providing necessary infrastructures such as appropriate monitors and upgraded IT equipment can prevent many of the PACS implementation challenges.