Geospatial Analytics Helps Improve Community Health Outcomes
One of the tenets of UVA Health’s mission is community and health equity. The health and well-being of members of his community, especially those who face social determinants of health, can be improved through community-based care.
With that in mind, UVA Health and its partners 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 to primary care, one of WellAWARE’s goals is to increase utilization of primary care services to reduce emergency room visits and hospitalizations for low acuity patients. .
Christian Wernz, Senior Data Scientist, UVA Health System, who will be discussing how to improve community outcomes using geospatial analytics at HIMSS22, explained that UVA Health Community Health Worker-based programs are pilot projects at this stage.
“We track outcomes, but the data is insufficient to assess program effectiveness,” he said. “The preliminary work we’ve done is to understand the needs of the community and focus initial program resources on the most vulnerable neighborhoods.”
He noted that information from medical records can be used at the population level to understand chronic disease prevalence and health needs.
Aggregating this data allows community health officials to gauge the level of need within a community.
“These leaders are always faced with the problem of where to focus limited resources,” Wernz said. “Having an accurate picture of health issues and needs plays a huge role in crafting the best solution for communities.”
Although health systems play a crucial role, they are not the complete solution to improving community health: collaboration with nonprofit organizations plays an important role in community health.
“We didn’t limit ourselves to medical data. In patient care, the social determinants of health play an important role in improving a patient’s health,” Wernz said. “Combining health data with the social and economic needs of the community creates an understanding that enables the collaboration of medical systems and nonprofit organizations to work together to improve the community. This is achieved by limited data sharing and interactive feedback between entities.
The UVA Health Data Science team created a system that geocodes addresses within their corporate data warehouse, allowing the team to aggregate information based on location.
This allows them to answer questions such as “how many of our patients in a given census area have diabetes?” Or which region 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. rural versus urban, we use different measures from different regions to ensure valid and useful comparisons,” Wernz said.
Many of these data elements needed to characterize a region are available in information from the American Community Survey, which the AVU integrates and updates in its 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. “This data is often used to adjust risk and improve analysis.”
Nathan Eddy is a health and tech freelancer based in Berlin.
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