AI in Rhapsody EMPI Improves Patient Matching

Drew Ivan, Chief Strategy Officer and Chief Architect at Rhapsody, a global leader in digital health enablement, describes in this interview their recent addition of machine learning to their Enterprise Master Patient Index (EMPI) tool.

Patient matching is hard work and there are risks both to making an incorrect match and to maintaining separate records for the same patient. An incorrect match runs the risk of a HIPAA violation where patient information is shared incorrectly and if the patient information is stored in two records that can lead to care issues where the clinician doesn’t have access to the patient’s full record.

Ivan explains that Rhapsody reached the limits of what conventional algorithms could do, and developed a form of AI leveraging machine learning (ML) in their new Autopilot tool to reproduce the decisions human staff would make. With Autopilot, for instance, an organization could process a large batch of new enrollees overnight.

Rhapsody EMPI can also manage identifiers from different organizations. It can query an outside source for additional information—such as a previous address or alternate patient name—and provides visibility into who merged records and when.  This ability to track data lineage and the consistent accuracy of AI matching reduce the risk of both non-compliance in person data management and poor patient outcomes due to incomplete records.

We also talk about how EMPI can be leveraged by health tech companies and care providers to ultimately improve the care that’s being provided.  Ivan also shares when it makes sense to look at an outsourced EMPI rather than a homegrown solution or no EMPI at all.  Check out the video to learn more about the value of identity management and how AI is taking it to the next level.

Learn more about Rhapsody: https://rhapsody.health/

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