Geospatial analysis is helping improve community health outcomes
One of the tenets of UVA Health’s mission is community and health equity. The health and well-being of people in its community, especially those who are challenged by social determinants of health, can be improved through community-based care.
With that in mind, UVA Health and its partners have launched WellAWARE, a program that provides community health services to targeted neighborhoods in Central Virginia.
The UVA Health Data Science team identified and prioritized neighborhoods in need through geospatial analysis, which integrated SDOH proxies with medical record data and provided operational support to the program.
By reducing barriers to healthy living and connecting people with primary care, one of WellAWARE’s goals is to increase the use of primary care services to reduce emergency department visits and the hospitalizations of low-acuity patients.
Christian Wernz, Senior Data Scientist, UVA Health System, who will address how to improve community outcomes using geospatial analysis at HIMSS22, explained UVA Health Community Health Worker-based programs are pilots at this point.
“We are tracking the outcomes but there is insufficient data to gauge the effectiveness of the programs,” he said. “The preliminary work we did is to understand the community needs and focus the initial program resources on the most vulnerable neighborhoods.”
He noted the medical record information can be used at a population level to understand the prevalence of chronic disease and health needs.
Aggregating this data allows community health leaders to measure the level of needs within a community.
“These leaders are always faced with the problem of where to focus limited resources,” Wernz said. “Having an accurate picture of the health problems and needs plays a huge role in developing the best solution for the communities.”
While health systems play a crucial role, they are not the complete solution to improving community health: There is a significant role for collaboration with nonprofits in community health.
“We did not limit ourselves to medical data. In caring for patients, the social determinants of health play a significant role in improving a patient’s health,” Wernz said. “Combining the data on health with the social and economic needs of the community creates an understanding that allows the collaboration of medical systems and nonprofits to work together to improve the community. This is achieved by limited data sharing and interactive feedback between the entities.”
The UVA Health Data Science Team has created a system that geocodes addresses within their enterprise data warehouse, allowing the team to aggregate information based on location.
This lets them answer questions like, “how many of our patients in a given census area have diabetes?” Or what area has the highest prevalence of hypertension? And then, do these areas correspond and by how much?
“Because different neighborhoods require different types of interventions, i.e. a rural versus an urban area, we use different measures of the different regions to assure valid and useful comparisons,” Wernz said.
Many of these data elements needed to characterize a region are available within the American Community Survey information, which UVA integrates and updates within their EDW.
“We can also add the Area Deprivation Index, which is hosted by the University of Wisconsin, as a component of our analysis,” he added. “These data are often used to risk adjust and improve analysis.”
Nathan Eddy is a healthcare and technology freelancer based in Berlin.
Email the writer: [email protected]
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