EHR + Geography = Population Health Management
By Katie Wike, contributing writer
Duke Medicine is combining the data of EHRs with geography information to create a program which can predict patient diagnoses.
Duke University Medicine is using geographical information to turn electronic health records (EHRs) into population health predictors. By integrating its EHR data with its geographic information system, Duke can enable clinicians to predict patients' diagnoses.
According to Health Data Management, Sohayla Pruitt was hired by Duke to run this project; she has a master’s degree in geographic information systems, or GIS. “I thought, wow, if we could automate some of this, pre select some of the data, preprocess a lot and then sort of wait for an event to happen, we could pass it through our models, let them plow through thousands of geospatial variables and [let the system] tell us the actual statistical significance,” Pruitt says. “Then, once you know how geography is influencing events and what they have in common, you can project that to other places where you should be paying attention because they have similar probability.”
iHealth Beat explains that the system works by using an automated geocoding system to verify addresses with a U.S. Postal Service database. These addresses are then passed through a commercial mapping database to geocode them. Finally, the system imports all U.S. Census Bureau data with a block group ID. This results in an assessment of socioeconomic indicators for each group of patients.
“When we visually map a population and a health issue, we want to give an understanding about why something is happening in a neighborhood,” says Pruitt. “Are there certain socioeconomic factors that are contributing? Do they not have access to certain things? Do they have too much access to certain things like fast food restaurants?”
Duke is working to develop a proof of concept and algorithms that would map locations and patients. They are also working on a system to track food-borne illnesses.
“It’s easy to visualize or just say, ‘Oh, this person lives in a low income neighborhood with lots of fast food restaurants.’ You could probably do that very quickly,” Pruitt says. ”But the only way to really understand the statistical significance of what’s going on and where else it’s happening or going to happen is through infrastructure development, by pre-downloading that data, prepping and pre-relating that data to every address and every EHR.”