In the Absence of a Road Map, Data Sharing Collaborations Can Start by Asking Questions
By Alison Rein, Senior Director for Evidence Generation and Translation at AcademyHealth
The movement to build health into the fabric of our communities is taking hold and stimulating the formation of many local collaborations focused on understanding and addressing the social, environmental and other factors that drive individual and population health. As they navigate these new relationships and work toward building a shared data infrastructure to support ongoing improvement and impact assessment, these local collaboratives must overcome a number of hurdles—some technical and legal—but many more related to process and culture change. Fortunately for those in the early stages, many early population health improvement pioneers have made progress and have useful insights and lessons to share.
The BUILD Health Challenge recently welcomed 19 new multi-sector partnerships that are embarking on a two-year endeavor to transform health in their communities. Leaders from All In: Data for Community Health recently joined the BUILD 2.0 kickoff meeting, and share some lessons from local collaboratives that already have started to travel this road.
While part 1 of this blog series illuminated some of the challenges to sharing data across sectors expressed by attendees of the BUILD kickoff, this second blog offers an approach and a series of questions that can help to get started—and stay on track. First, it provides a list of practical questions that are essential—both for initial framing and as common reference points once efforts are underway. Second, it shares examples from BUILD 1.0, Data Across Sectors for Health (DASH), and the Community Health Peer Learning (CHP) Program projects that already have asked and answered these questions within their own collaborations.
1. Have you identified a compelling reason to collaborate to share data?
While there may be compelling and intuitive reasons for pursuing multi-sector collaboration, change is hard—especially when it involves engagement of organizations with different motivations and cultures. It is critical to engage all partners in a deliberative process of exploring their individual organizational and/or sectoral pain points, and then to try and understand where these intersect (i.e., shared challenges) to guide collaborative action. Identifying common interests and shared challenges, and staying laser focused on those issues (at least at the outset), can help to launch and maintain progress on multi-sector collaborative work. Finding this shared purpose that unites partners around a common goal (i.e., finding a collective north star) is what will help to smooth the path forward during inevitable points of friction.
Example: Cincinnati Children’s Hospital Medical Center
When the Cincinnati Children’s Hospital Medical Center saw disproportionately higher bed-day rates for children living just steps away from their facility, they found that they could not tackle the problem alone. To effectively characterize, locate within the community, and address social and environmental risk factors contributing to poor health outcomes, they needed to engage community members, social workers, pharmacists, and school nurses. These individuals and organizations represent different interests and, organizationally, have diverse missions, but all recognized that they have a stake in improving the management of pediatric asthma, and mitigating the risk of hospitalization, among at-risk children in their community. This shared value has brought (and kept) these parties at the table, where they are working to leverage diverse data and test strategies for improving health outcomes for pediatric patients. Read more in this CHP bright spot and project profile.
2. Do you know what you have (and what you lack) in terms of community assets to move forward?
Clearly mapping community assets—including partner roles, human and other resources, and current/potentially available data sources—can help to illuminate and characterize what exists relative to what is needed to achieve the desired objective. Done well and in a systematic manner, this process can also help to increase awareness and appreciation among partners for the diverse perspectives, roles and resources they each bring to the collaboration. From a data asset perspective, this is an essential step for determining current limitations (e.g., quality, timeliness), as well as points in the collection and sharing process where there may be opportunities to increase efficiency and/or address gaps.
Example: Louisiana Public Health Institute
To improve outcomes for individuals with serious mental illness, the Louisiana Public Health Institute convened a unique group of stakeholders to map the process through which individuals and their data flow through the behavioral health crisis system in New Orleans. The exercise involved mapping key client touch points in the health care and criminal justice systems, reaching a common understanding of how data are currently accessed and used to support decision-making at each point in the system, and identifying gaps in available data needed to effectively provide and coordinate care. Having gone through this process, the collaboration was able to identify several actionable next steps, and also deepen their collective understanding of the shared challenge. Read more in this CHP bright spot and project profile.
3. Have you determined the frame of reference for your work?
To achieve your desired project outcomes, you have a variety of different approaches to consider. Are you working on a health care intervention, a quality improvement initiative, a public health effort, a research project, or something else? Regardless of framing, each comes with a different set of regulatory and other requirements to navigate. Before committing, consider the benefits and drawbacks of each approach in terms of what they afford for data access, utility, and protections for use. Also, consider how these approaches could change over time in order to achieve your broader community health improvement objectives.
Example: Cleveland Healthy Home Data Collaborative
The BUILD 1.0 Cleveland Healthy Home Data Collaborative is building a healthy housing system that enables physicians, public health officials, and the public to easily access collaborative, useful information to address disparities—with a focus on asthma and lead poisoning. Because their program was defined as a research project rather than a public health initiative, it was not eligible for a public health exemption under HIPAA. Their biggest challenge from the health system has been moving through the IRB approval process, which took longer than anticipated. The Healthy Housing Program had to revise the MOU between MetroHealth and Environmental Health Watch to satisfy the requirements of the IRB. Read more in this Practical Playbook success story and BUILD project profile.
4. Have you engaged partners in clear and deliberate dialogue about why specific data are needed to achieve program objectives, and how the data assets they manage contribute?
Even among partners with strong collaborative history and trust, data sharing negotiations can be challenging and sometimes slow or even stall local health improvement efforts. One strategy for navigating this process is to ensure that all partners have a clear and shared understanding of why each specific data source, type, and element is needed to achieve core objectives, and to identify the narrowest set of data that can allow for their achievement. This, coupled with a similarly detailed discussion of how the data will be used, and under what specific conditions, helps to convey why each partner’s involvement is mission critical.
Example: Allegheny Data Sharing Alliance for Health
The Allegheny Data Sharing Alliance for Health is working to create a connected data warehouse that combines data from multiple sectors to create a more complete picture of the factors impacting the cardiovascular health of residents. To figure out how to make the data valuable to all sectors involved, the Allegheny County Health Department worked with each partner to determine the data that is most useful in helping them build on their existing goals. Convening partners around the table in discussions about data is critical to the sustainability of the alliance, as these other sectors play a vital role in developing and implementing interventions to address cardiovascular disease. Read more in this DASH spotlight article and project profile.
5. Are you planning for the future as you act in the present?
In working toward design and development of robust data infrastructure to support community health, it’s important to maintain focus on immediate goals, while also considering potential next steps; up-front consideration of whether the data systems built today can be repurposed and extended later to satisfy longer-term objectives may require a bit more time and thoughtfulness at the outset, but can save time and resources in the long run; it can also support bolder action and greater progress. Many local data sharing collaborations start with a very specific objective, and frame it in terms of a narrow use case (e.g., care coordination). This focus may be essential for early stages, but leadership should always be thinking about—and considering whether the infrastructure under development will support expansion into new use cases and other ways to generate value for community partners. Taking time at the beginning, to plan for sustainable governance structures, and revisiting plans throughout to mitigate “technical debt,” can help to build flexible capacity and maximize the range of opportunities for impact and sustained revenue.
Example: Harris County BUILD Health Partnership
In the early stages of the BUILD 1.0 Harris County BUILD Health Partnership, the collaboration came together to develop a sustainability plan for their community-supported food system in Pasadena, TX. From the start, the partnership looked at how to map the impacts of potential governance structures and funding streams and outlined recommendations to ensure sustainability. As a result of this planning, the collaboration took steps to strengthen their governance structure, establish new partnerships that could advance their work, form a food policy council, and consider new funding streams for the project. Read more in BUILD’s Keys to Collaboration brief and this project profile.
About the Author
Alison Rein
is a Senior Director for Evidence Generation and Translation at AcademyHealth, where she directs several projects that investigate how new sources of data and expanded stakeholder engagement are helping to transform health, care and research. Ms. Rein led the Community Health Peer (CHP) Learning Program, a partnership with ONC to establish a national peer learning collaborative for 15 competitively awarded communities to address specified population health management challenges through increased sharing and use of electronic data.