Northwestern Medicine Builds Machine-Learning Model To Identify Patients With Heart Failure
Scientists and physicians create a custom tool within the Epic electronic health record to preemptively find patients with heart failure, expediting assessment and treatment.
One of the greatest strengths of healthcare software as ubiquitous as Epic’s electronic health record (EHR) is that it can amass so much data. In fact, the company estimates that hospitals using its software hold the medical records of more than half of all patients in the U.S. But with such a high volume of information held in one system, a common question facing today’s healthcare experts is how that data can be more efficiently leveraged to advance patient care.
For a team of scientists and physicians at Northwestern Medicine Bluhm Cardiovascular Institute, the answer to this question involved a form of artificial intelligence. In the fall of 2020, they began creating a custom machine-learning model within the Epic EHR that could sort through millions of patient records to make predictions about whether a patient could have heart failure, which currently impacts more than 6 million people in the United States.*