Sharing health data and information¹ across stakeholder groups is the bedrock of a learning health system. As data and information are increasingly combined across various sources, their generative value to transform health, health care, and health equity increases significantly. Facilitating this potential is an escalating surge of digital technologies (i.e., cloud computing, broadband and wireless solutions, digital health technologies, and application programming interfaces [APIs]) that, with each successive generation, not only enhance data sharing, but also improve in their ability to preserve privacy and identify and mitigate cybersecurity risks. These technological advances, coupled with notable policy developments, new interoperability standards (particularly the Fast Healthcare Interoperability Resources [FHIR] standard), and the launch of innovative payment models within the last decade, have resulted in a greater recognition of the value of health data sharing among patients, providers, and researchers. Consequently, a number of data sharing collaborations are emerging across the health care ecosystem.

Unquestionably, the COVID-19 pandemic has had a catalytic effect on this trend. The criticality of swift data exchange became evident at the outset of the pandemic, when the scientific community sought answers about the novel SARS-CoV-2 virus and emerging disease. Then, as the crisis intensified, data sharing graduated from a research imperative to a societal one, with a clear need to urgently share and link data across multiple sectors and industries to curb the effects of the pandemic and prevent the next one.

In spite of these evolving attitudes toward data sharing and the ubiquity of data-sharing partnerships, barriers persist. The practice of health data sharing occurs unevenly, prominent in certain stakeholder communities while absent in others. A stark contrast is observed between the volume, speed, and frequency with which health data is aggregated and linked—oftentimes with non-traditional forms of health data—for marketing purposes, and the continuing challenges patients experience in contributing data to their own health records. In addition, there are varying levels of data sharing. Not all types of data are shared in the same manner and at the same level of granularity, creating a patchwork of information. As highlighted by the gaps observed in the haphazard and often inadequate sharing of race and ethnicity data during the pandemic, the consequences can be severe—impacting the allocation of much-needed resources and attention to marginalized communities. Therefore, it is important to recognize the value of data sharing in which stakeholder participation is equitable and comprehensive— not only for achieving a future ideal state in health care, but also for redressing long-standing inequities.

Prior to the COVID-19 pandemic, the National Academy of Medicine (NAM), in consultation with the Patient-Centered Outcomes Research Institute (PCORI), undertook an initiative committed to this vision. During the fall of 2018 through the summer of 2019, the NAM held a series of convenings involving three stakeholder groups—patients and family leaders, researchers and research oversight leaders, and health care executives—to identify the barriers to data sharing that each of these groups experienced or perceived. The culminating Special Publication, Health Data Sharing to Support Better Health Outcomes: Building a Foundation of Stakeholder Trust, elucidated the list of most pressing barriers, shown in Figure 1 (Whicher et al., 2020).

FIGURE 1

Prioritized Cultural, Ethical, Regulatory, and Financial Barriers to Data Sharing, Linkage, and Use. SOURCE: National Academy of Medicine. 2020. Health Data Sharing to Support Better Outcomes: Building a Foundation of Stakeholder Trust. D. Whicher, M. (more...)

Underpinning these concerns is the lack of trust among and within the three stakeholder groups. “The patient and family community does not trust that health care systems and researchers will make data and the conclusions based on the data available to them and will not misuse their data by rationing care and sharing with unauthorized third parties. Researchers have a similar mistrust in the intentions of third-party users. Meanwhile, health systems worry that patients will misinterpret data or use data inappropriately, such as allowing it to be combined with other elements and rendering the data identifiable. Health systems are also reluctant to share data with industry partners for fear of losing their competitive advantage” (Whicher et al., 2020).

Authors of the progenitor publication (Whicher et al., 2020) also coalesced around a set of action items that could be taken in the near-term (1-3 years) to begin addressing these issues. They include:

• Building a consortium of organizations committed to data sharing to help mobilize stakeholders around the idea of sharing.
• Identifying priority use cases and data sharing exemplars to demonstrate how barriers could be overcome.
• Reframing the risk discussion or business case related to data sharing to highlight evidence-based arguments about the risks of not sharing data.
• Engaging in a national dialogue with various stakeholder groups about the benefits of bidirectional data exchange and the current barriers to accessing and contributing data. A component of the national dialogue would be to prepare and empower various stakeholder groups to meaningfully participate in health data sharing.

From this list, the authors concluded that the most fruitful next step would be to develop a compilation of case studies of successful health data sharing across different stakeholder groups with the intent that this resource could serve as the basis for informing and catalyzing work on the other aforementioned priority action items. In addition, the prospect of a case study compilation was independently raised in several other NAM forums, including National Academy of Medicine’s Leadership Consortium: Collaboration for a Value & Science-Driven Health System’s Action Collaboratives (NAM, 2020a; NAM, 2020b).

This companion Special Publication consists of a series of 11 case studies that illustrate diverse approaches to data sharing with the aim of responding to the most pressing issues detailed in the progenitor publication, Health Data Sharing to Support Better Health Outcomes: Building a Foundation of Stakeholder Trust. In consultation with PCORI, case study candidates were selected, drawing upon specific exemplars identified in the progenitor publication and supplemented by more contemporary efforts related to the pandemic. Additional consideration was given to geographic and organizational diversity and to ensuring that the full complement of case studies reflected a spectrum of data-sharing interactions anchored to the three stakeholder groups spotlighted in the previous publication (i.e., patients and families, health care executives, and researchers and research oversight leaders, as detailed in Table 1). Nonetheless, the authors acknowledge that the health data-sharing ecosystem is vast and diverse, involving many other stakeholder groups not highlighted in this publication.

TABLE 1

Case Study Characteristics.

While the individual case studies demonstrate different data sharing use cases, collectively, they address the breadth of priority issues (see Figure 1) and exemplify data sharing as the linchpin to achieving each of the entities’ immediate strategic goals and consequently their ability to improve health, health care, and health equity.

Case study narratives were constructed from interviews conducted by NAM staff with leaders of these organizations in fall and winter of 2020. NAM staff used a semi-structured interview guide (see Appendix A) to solicit responses about the impetus for the collaboration; contextual factors giving rise to the opportunity for and success of the data sharing arrangement; barriers that were overcome and ones that endure; details about the type and level of data shared, governance model, and technical infrastructure; and advice for the field. Interviewees were forthcoming in their responses about the challenges as well as the positive outcomes of their work. In the process of developing each case study narrative, the editorial team remained attuned to the paramount importance of providing a balanced summary of each group’s work. While enthusiasm about achievements may be well-placed, the insights that arise from understanding the barriers is pivotal to progress. Hence, barriers are carefully elucidated for all profiled entities.

Following the interviews, a survey was administered to collect information about each initiative’s operations and funding. Each narrative includes an “At a Glance” sidebar showing the summary of barriers addressed and insights for the field.

The featured entities vary in size, longevity, and financing mechanisms (see Figure 2). Many of the interviewed organizations were funded by more than one source; thus, the sum of the percentages in Figure 2 exceed 100%. They also differ in their approach to data sharing. While each organization may have taken a different tactic based on a different motivating rationale, the examples point to how organizations, with an intrepid spirit, can best collaborate to share and link data while overcoming obstacles and addressing reservations about data sharing. The editors of this Special Publication hope this compendium of case studies proves to be an accessible reference for the field and helps to cultivate the will and trust for data sharing.

FIGURE 2

Funding Sources for the Data-Sharing Initiatives.

Footnotes

1

As defined in the progenitor publication, Health Data Sharing to Support Better Health Outcomes: Building a Foundation of Stakeholder Trust, health data is all the information that accumulates about a person or population that may affect health outcomes. This includes, but is not limited to: 1) health data generated during clinical encounters and stored in electronic health records or other data systems; 2) health insurance claims data; 3) data gathered from clinical and health services research; 4) genomic, proteomic, and immunomic data; 5) data related to the social and environmental determinants of health collected during clinical encounters or outside of the health care system through community, state, and federal organizations; and 6.) patient-generated health data, which has been defined as health-related data created, recorded, or gathered by or from patients (or family members or other caregivers). Health information results from the analysis and synthesis of various pieces of data.