This article is part three of a six-part blog series with Dr. Brad Jones.
By understanding social determinants of health and factoring them into the equation when making data-driven decisions, organizations can realize a return on their investment in a few different ways. In addition to serving their target populations more effectively, they also increase their cost-effectiveness. By delivering programs and product cost-effectively, the bottom line is positively impacted through increased margins.
Geo-culturally Sensitive Assessments
As part of the ACA, non-profit hospitals must do a community health needs assessment once every three years in order to maintain their non-profit status. Doing these assessments in a geo-culturally sensitive way will enhance those assessments and promote development of implementation plans in ways that enable them to better serve their target populations. For example, examining claims and prevalence data at sub-county levels such as zip codes and census tracts and then augmenting that data with social determinants of health-related information can enable organizations to understand where “hotspots of needs” and “hotspots of assets” are and, as a result, make cost-effective, service-maximizing decisions.
For instance, Southwest Georgia is very socio-culturally similar across several counties. Let’s consider Albany and Thomasville because they’re somewhat close in proximity, but are far enough apart to have their own separate civic identities. As the smaller of the two cities, does it make sense for Thomasville-area non-profit hospitals to do their own assessment, or should they pool their resources with non-profit hospitals in and around Albany, Tifton, and Valdosta and do a larger, more comprehensive assessment for the entire region? If the answer to this question is the latter, this larger assessment could then be segmented into smaller sub-assessments categorized by the four anchor “regional hubs” of Albany, Thomasville, Tifton, and Valdosta that could be then utilized by organizations in each of those areas for the purpose of satisfying the ACA requirement.
This type of analysis and approach is where I think we can really help rural-serving organizations in particular. We can take a comprehensive look at the data, see where the geographically-based socio-cultural similarities are, and identify prospective partners that may not otherwise consider themselves as prospective partners. We can then convene cross-sector partners across these geo-culturally similar rural regions for several purposes including, but not limited to, conducting community health needs assessments, applying for major grants, engaging the private sector from across a wider region as prospective partners, and promoting health and wellness across a larger population base defined more by geo-cultural similarities as opposed to geo-political boundaries.
State-level Medicaid administrators, CEOs of rural-serving organizations, Community Health Center/FQHC CEOs, and administrators for state departments of health typically face the same major problem: limited financial resources. Consequently, designing and implementing programs in a cost-effective way is often mission-critical for these types of organizations. With our tools and analytical capabilities, we can help organizations identify and understand the relevant data sets and strategically apply those data analytics along with recent findings from the literature. Since cost-effectiveness is a major priority for these organizations, we can take a thorough and comprehensive look at social determinants of health data, claims data, prevalence data, etc., and ask questions such as, “Where are the greatest needs? What are our assets? Where can we get the most ‘bang for our buck’?” We aim to analyze data from as small of a geographic unit possible (e.g., county, zip-code, census tract) in order to understand where efforts and resources should be targeted most specifically to promote maximum cost-effectiveness and service.
[Look for part three of this six-part blog series next week!]