Insights from Data-Driven Health Collaborations

Riders (alongside pedestrians) getting off a RTD Light Rail Train near 16th street in Downtown Denver. The RTD Light Rail system serves the Denver Metropolitan Area and has 5 lines and 39.4 miles of track, and has a daily ridership of 63,100 people.

This blog profiles community projects working to harness data from multiple sectors to develop more effective health interventions and policies.

We know that the key drivers of health are social, economic, environmental and behavioral, but forging cross-sector connections at the community level is hard work. This blog profiles twenty-five community projects working to harness data from multiple sectors to develop more effective health interventions and policies. They are part of a nationwide learning collaborative called All In: Data for Community Health, which seeks to build the capacity of local communities to address social determinants of health by connecting and integrating data from multiple sectors.

With increasing investment in this area, and growing excitement about the potential to drive systemic change, it is critical for early innovators to share their insights and lessons learned. In a previous blog post, we profiled four local projects that are sharing data across sectors to create healthier environments. In this post, we share four key insights that might be useful those in the early stages of multi-sector data sharing.

What Have We Learned?

Invest in data mapping.

Investments in data mapping (i.e., diagramming out data sources, types, flows, and conditions) during early stages will save both time and resources, and help clarify data needs and sharing parameters.

  • Example: The Louisiana Public Health Institute is using health information technology to improve care coordination and access to services across sectors for those with severe and persistent mental illness. They have worked with partners in their community to map out current data sources, types, and current flows, and then layered in the changes desired for their program. This has helped them identify what is absolutely necessary, and expedited the data sharing agreement process.

Start early to generate buy-in for data sharing agreements.

Developing legal agreements is a complicated process that can take months and even years, so project leadership should ensure broad conceptual support from key community leaders, stakeholders and lawyers early in the process—and then work diligently to negotiate the specifics.

  • Example: The Baltimore City Health Department is creating a real-time data surveillance system to track fall-related emergency department visits and hospitalizations. In preparation for this project, they worked closely with executive leadership, including the City Solicitor, attorneys, and the Health Commissioner, to obtain their buy-in and map a tiered legal framework for data sharing that outlines different legal strategies based on specific applications of data, from public health surveillance activities to sharing protected health information.

Relationships are key to data sharing.

Effective multi-sector data sharing is bolstered and accelerated by a strong history of collaboration with core partners. Working together toward common objectives and thinking about the mutual strategic imperatives for data sharing helps to build trust and moderate concerns.

  • Example: Public Health Seattle & King County is partnering with public housing authorities to link Medicaid claims records and housing data to improve the health of King County residents. Before embarking on this project, Public Health Seattle & King County had already worked successfully with public housing authorities over many years on various initiatives, from creating tobacco-free living environments to asthma prevention. Sharing data was a natural next step that could help both sectors achieve a greater collective impact.

Disseminate shared data early and widely.

There is great power in sharing data early and often with community members, in addition to encouraging their questions of the existing evidence to drive further inquiry. Community engagement, or the act of involving the individuals and organizations who may be impacted by the work, is a critical component of any successful data sharing initiative.

  • Example: The Cincinnati Children’s Medical Center is working to integrate electronic health records and geographic information systems to help reduce inpatient hospitalizations for pediatric patients. After gaining the trust of their community and community hospital, they have been able to track that their inpatient bed-day measure is sensitive to long lengths of stay. Overall, this process has uncovered immediate opportunities, including novel interventions (e.g., to improve housing conditions) and identification of new data sources that can support program aims.

Join Us as We Move Forward

All In: Data for Community Health is committed to creating meaningful connections and developing a shared vision that can guide and advance the field of multi-sector data sharing. As projects work to tackle common challenges, the findings and best practices that emerge will be disseminated to help build capacity for this critically important work. We want to learn from you too, so please share your experiences!

To learn more, you can:

Send us your questions, suggestions, and feedback by contacting allin@dashconnect.org.

This blog was originally published on the Build Healthy Places Network Expert Insights blog