Machine learning predicts patients in need of advanced depression care

Using data from a statewide health information exchange, researchers have created machine learning algorithms that are able to identify patients who need advanced treatment for depression.

According to Regenstrief Institute and Indiana University researchers, identifying cases of depression that require advanced care can be challenging for primary care physicians.

However, they contend that their models—which leverage diagnostic, behavioral and demographic data, as well as past visit history from an HIE—can help PCPs predict which patients may be more at risk for adverse events from depression.

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Regenstrief Institute

Researchers created models for the entire patient population at Eskenazi Health, the public safety net healthcare system for Marion County, Indiana, as well as several different high-risk patient populations.

“This study demonstrates the ability to automate screening for patients in need of advanced care for depression across an overall patient population or various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors and past visit history,” conclude researchers in a recent article published in the Journal of Medical Internet Research. “Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound healthcare services.”

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“Primary care doctors often have limited time, and identifying patients with more severe forms of depression can be challenging and time consuming,” observes Shaun Grannis, MD, co-author of the study and director of the Clem McDonald Center for Biomedical Informatics at Regenstrief Institute. “Our model helps them help their patients more efficiently and improve quality of care simultaneously,”

“Our approach is also well suited to leverage increasing health information technology adoption and interoperability to enable preventive care and improve access to wraparound health services,” adds Grannis, who is also the Clem McDonald Professor of Biomedical Informatics at Indiana University School of Medicine.

Going forward, he and his colleagues are working to integrate social determinants of health data into their models.

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