By Natasha Goburdhun
Vice President, Connected Communities of Care at PCCI
Throughout human history storytelling has been a powerful tool to convey important messages to audiences. Data driven storytelling combines hard data to support an engaging narrative for an audience. Like any good story, data driven storytelling needs to answer three key questions:
- Which audience do you want to communicate your story to? For ACH sites this could be community members, partners, or potential funders
- What is the message that you want to tell your audience? ACH sites may be trying to convey to neighborhood leaders how an intervention will help their community, or they may be trying to build a value proposition to present to a Medicaid Managed Care payer.
- What data can you collect to help you support the message you’re trying to tell? Depending on the story that sites could use publicly available data like Community Health Needs Assessments and census data or may need to demonstrate impact by gathering data on changes in healthcare utilization and costs or disease prevalence.
In the context of the ACH model, data driven storytelling impacts just about every key part of the process from planning (Community Engagement, Aligned Vision and Goals) to performance improvement (Continuous Learning) to demonstrating impact (Financial Sustainability, Business Case/ ROI, and Policy and Systems Change). Recently our six TACHI sites gathered to discuss how data driven storytelling can assist them throughout their planning and implementation process.
Start Small: Use Data to Help Prioritize Activities
When transitioning from planning to execution, sites are often unsure how to take the first step towards implementing their interventions to impact the larger issues that communities are struggling with such as food insecurity, homelessness, earning a living wage, and health equity. In many communities more than one of these issues is a priority for improving the health and wellness of the most vulnerable populations, which makes it even harder to know where to start.
Using data to help inform a prioritization process can be helpful in building a phased approach to solving a specific community need. To begin, teams should focus on identifying quick wins associated with improving the current state of an identified need by using an intervention that can be implemented in a short timeframe and gather initial results. This gives teams time to pilot programmatic and operational workflows and test their ability to collect data accurately and consistently. Choosing a more immediate intervention may also offer the ability to demonstrate initial results of the intervention.
A good example of this type of phased approach can be seen from a health system that whose goal was to impact health inequities in vulnerable populations by reducing health disparities in this population. This was part of a larger Diversity, Equity and Inclusion strategy for the organization. But which health disparities would they focus on first?
Using publicly available and internal data, and some third-party tools, they created a heat maps that helped them identify care gaps in non-white patient populations. They used a scoring rubric to prioritize among the identified populations and then complemented with additional data including social determinants of health, organizational information on patient engagement and information gathered from the community.
Based on these analyses they narrowed their focus to disparities in cancer screening rates within minority patient populations, who they identified as less likely to use the system’s primary patient engagement online tool to schedule appointments. What they discovered is that the barriers that make it harder for minority communities to access healthcare services also made it harder for community members to use the online engagement tool. They are now working to increase the completeness of race, ethnicity and language data within their systems, doing outreach to increase the screening appointments made for members of the minority communities, and getting direct patient feedback to help them improve their interventions going forward.
Build a Strong Data and Technology Foundation
Much like trying to solve for health equity, trying to solve all technology and data issues in Year 1 of an implementation is not realistic. While a long-term goal of ACH sites may be to integrate health and social data via a Community Information Exchange (CIE) and demonstrate population level improvements, it takes time, people, and financial resources to do so. As implementation starts, it may be better to identify your access to existing data tools that are less complex and less resource intensive but still allow you to collect, analyze and report on key metrics at an individual/person level (see Figure 1).
Figure 1: Data Infrastructure Continuum
To support the phased implementation process, start by building a data and technology strategy designed to create a strong foundation of data sharing, reporting and operational workflows across all partners that supports the creation of a CIE over time. By taking this approach, sites can start to test their capabilities, build the relationships and trust they need to share data across partners, and pilot their reporting and analytics capabilities.
The AHC model is more than an experiment in improving community health disparities. It is about changing the way healthcare and community partners work to develop sustainable changes that support underserved communities to improve their whole person health. These types of system-level changes take time. But taking a first step, starting small, and building a strong foundation with continuous performance improvement can ensure a team’s long-term success.