image: Dr. Nate Apathy, professor of health policy and management at University of Maryland School of Public Health.
Credit: UMD
It’s a common “medical” complaint: You switch to a new doctor and find that electronic health records from your old clinic or from urgent care or specialist visits have not moved with you – leading to a déjà vu of filling out the same forms you’ve filled out a hundred times before. But beyond the form frustration, what does it mean for your health when your providers aren’t seeing your complete medical history? Now, thanks to a $1.4 million NIH grant, co-led by the University of Maryland School of Public Health (SPH), researchers are working to answer that question.
“It’s been a point of frustration for decades that digitized health records don’t move with you when you go from doctor to doctor. There are lots of layers that introduce friction to the use of this outside data,” said Dr. Nate Apathy, a professor of health policy and management at UMD SPH. “There are still a lot of important outstanding questions about how the use of outside records changes doctors’ decision-making, and furthermore, how those gaps in records could impact a patient’s health.”
Apathy and Dr. A Jay Holmgren of University of California, San Francisco’s Medical School, will examine how doctors make clinical decisions with the data at hand, how their decisions change when they see all the information including “outside data” and what impact that change ultimately has on a person’s health. “Outside data” refers to any health records that originate from outside of a primary health provider's institution (e.g., an urgent care facility or non-affiliated hospital).
The rate at which doctors look at “outside data” to make clinical decisions is, in general, surprisingly low, Apathy adds. In an outpatient setting, on average less than 30% look at outside data. For many doctors, it’s more like 5-10%. That low usage is due to a variety of factors, including that the data may not be relevant to the patient’s health concern, Apathy notes.
Over the next four years, researchers will examine data from two academic health systems: UCSF Health and the University of Maryland Medical System. The IT systems of both institutions were recently updated to seamlessly include “outside data” (as permitted by patients) in patient electronic health records, comparing if and how clinicians used this outside data for clinical decisions before and after the system upgrade.
Using machine learning and AI tools, the researchers plan to create different patient “phenotypes,” which are profiles of observable traits like “male, over 50 years old with newly diagnosed hypertension who hasn’t been seen in the last 6 months.” These phenotypes will help flag relevant outside records for a given patient so doctors can proactively look at the relevant data for their decision making.
“When health data travels seamlessly between institutions, there is immense promise to drive down health care costs and reduce health care use and duplicative paperwork, all of which can improve patient health and satisfaction,” Apathy said.
“We hope this research and the open-source tools we will create from it will contribute to improved decision-making and health outcomes.”
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This award is supported National Institutes of Health (NIH) grant 1R01LM014770-01.