The Stew BLOG
The Universal Difficulty (But Not Impossibility) of Sharing Data
The recent ReThink Health Pulse Check report provides an excellent frame for thinking about the growing number of multi-sector partnerships to improve population and community health. It arrays 237 such partnerships that responded to a 2016 survey and represent varying phases of development along ReThink Health’s Pathway for Transforming Regional Health. Each developmental phase presents its own set of pitfalls, or barriers to progress, as well as opportunities for building momentum.
To leverage and further extend the valuable insights and experiences from our respective programs—the Community Health Peer Learning (CHP) Program and Data Across Sectors for Health (DASH)—we co-founded All In: Data for Community Health. All In is a nationwide learning collaborative designed to help communities build capacity to address the social determinants of health through multi-sector data sharing collaborations. The BUILD Health Challenge and the Colorado Health Foundation have since joined All In, and numerous others have engaged less formally in this knowledge sharing network. Collectively, All In includes over 50 community collaborations across the U.S.
In the last 16 months, we too have worked to locate our respective cohorts along a continuum of developmental readiness, focusing primarily on the domains of collaboration and data sharing. In the process of providing technical assistance and fostering peer connections, we have learned a great deal about the range and specific nature of challenges encountered by local partnerships working at the intersection of daily life and health.
While some of these challenges are context-specific, many are common across efforts, and data-sharing problems are nearly universal. Complicating matters is that data sharing is a “Gordian Knot” and very layered issue – requiring navigation of:
- Real and perceived legal barriers;
- Misunderstandings, communication failures, and misalignments rooted in differing community and organizational cultures;
- Outdated and/or differing technical capacity, standards, and data quality limitations; and
- Lack of trusted and scalable tools and services to support integration of disparate data sources and formats.
Though these issues can slow progress and frustrate even the most collaborative partners, it’s important to note that those on the front lines have made progress, and have some useful insights and critical lessons to share.
Common Pitfall #1: Failure to Co-Define and Communicate a Mutual Value Proposition
The Providence Center for Outcomes Research and Education (CORE) is working in Southwest Washington State to build a regional community health dataset that connects data across multiple sectors. CORE co-designed an approach with the Healthy Living Collaborative to leverage their shared data assets to foster learning and collaboration for improving community health. They engaged in early discussions with partners, and in doing so identified a barrier: potential data contributors struggled to proceed with developing data-sharing agreements without more tailored information about their role in contributing to the overall vision and clarity on the value that would accrue to their sector (e.g., housing, education).
To avert this pitfall, the CORE team knew they needed to more clearly articulate this information in the hope that this would help partners successfully translate to their specific sector data elements needed for the data-sharing agreement. The team developed a strategic memo, using partner contributed terminology to convey both the context and rationale for data sharing, including why certain key data elements were or were not essential and how they would be used to generate value for all partners involved. The resulting strategic memo template maps in plain language to the sections of a data sharing agreement and has been used to “pave a smoother pathway” toward securing data-sharing agreements with two sectors, with a third underway.
Common Pitfall #2: Thinking Short-Term About Technology
Another barrier relates to moving beyond the “one-off” custom development of a technical interface and bringing data integration to scale. For example, local health departments in Baltimore, Chicago, and Allegheny County are developing data integrations and analytic engines designed for specific use case interventions: respectively, reducing falls among the elderly, more efficiently identifying communities at risk for lead poisoning, and reducing cardiovascular disease at the census-tract level.
Despite their different near-term objectives, each has proceeded with a design frame that takes a longer-term view, which will allow them to apply the data integrations to other use cases in the future. The forward-thinking frame and potential reusability of these initiatives has proven especially important as community partnerships across the country have the opportunity to meet together in local peer-to-peer visits sponsored by All In.
Common Pitfall #3: Over-Assigning Blame to HIPAA
While true comprehension and appropriate interpretation of HIPAA requires effort (and often legal counsel), a very common pitfall is the natural impulse to assume understanding without investigation and confirmation. To avoid this requires that data sharing parties enter the conversation with a “how can we get to yes” mentality (see Pitfall #1 above) without immediately making hasty judgements about what the regulation does or does not allow. Also important is a willingness to consider – and then acknowledge – which challenges relate directly to HIPAA, and which have more to do with human errors of interpretation and workflow, organizational culture, or fear of potential consequences.
For example, on a recent site visit, a community partner revealed that while health sector leaders were willing to proceed with data sharing discussions, it was the front line staff, who would be key intermediaries in the data sharing process, who incorrectly asserted that HIPAA would not permit the desired data sharing. This misinterpretation is especially stifling of innovation when data stewards use HIPAA as the justification for not sharing data that could otherwise be available for broader use, and is only addressed through further discussion, education, and staff training.
Our recent experience helping communities navigate this relatively new and dynamic field is relevant to ReThink Health’s Pulse Check findings, specifically those related to the importance of foundational activities that can help earlier and mid-phase efforts to progress. Chief among these is the need to build and sustain a robust infrastructure, including integrated data, to support core functions such as prioritization of health objectives, intervention targeting and design, outcome tracking and measurement and policy development. Because of the great diversity in format and quality of community data sources, local partners are pursuing a variety of data-sharing mechanisms, like leveraging existing health information exchange (HIE) infrastructure, building new care coordination models that integrate human services and healthcare data, and striving to connect systems rather achieve an unlikely standard for interoperability.
Because there is no standard roadmap for this emerging field, the expertise and local lessons being generated across the country represent an evolving knowledgebase. Efforts like the Pulse Check and All In share a common desire to capture and illuminate these insights, lessons, and promising practices that will accelerate the spread and scale of data-informed interventions for community health improvement, and help to mitigate pitfalls identified in the Pulse Check report.
If you are a partner in a data-sharing collaborative, and would like to engage with others who are working at the local level to improve community health, we invite you to share your ideas and observations. Please add your comments below. Visit www.allindata.org to learn more, or sign up for the All In newsletter to get regular updates about multi-sector data sharing for health.