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Machine Learning in Healthcare Takes Another Step

Analysis  |  By  
   November 29, 2016

Get ready for the next wave of predictive analytics, capable of identifying future admissions and health plan disenrollments.

Machine learning is not new to healthcare, and we have IBM's Watson technology to thank for that.

Until recently, many of the machine learning applications talked about for healthcare had been used to teach computing systems enough to be able to suggest a diagnosis on a specific disease.

IBM took things further. It essentially sent Watson to medical school.

IBM had Watson ingest large amounts of medical literature to learn everything physicians are taught about patients' conditions, and then taught it to make diagnoses.

This is how Watson won at Jeopardy.

But a Harvard professor who leads a startup supplying machine learning technology to Senior Whole Health, a Medicaid managed care organization active in New York state and Massachusetts, says that machine learning will eventually power all technologies we know today as predictive analytics and population health.

Leonard D'Avolio is that professor, and his background in healthcare makes him someone to watch on this front. His startup, Cyft, specializes in creating proactive care models with all available data from EHRs, unstructured notes, pharmacy info, and more to identify and better treat patients who will be soon experiencing some kind of trauma or risk of readmission.

D'Avolio's background includes collaboration with Atul Gawande, MD, at Ariadne Labs, an innovation lab startup where Gawande serves as executive director.

"I came up as a researcher and so I knew from trying to solve medical data problems that more than 50% of what is considered clinically relevant is unstructured free text in the medical record," D'Avolio says.

Scott Mace is the former senior technology editor for HealthLeaders Media. He is now the senior editor, custom content at H3.Group.

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