How UNC Health Care Uses Natural Language Processing to Reduce Readmissions

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UNC Health Care in Chapel Hill, N.C., has begun to use natural language processing technology to turn a growing amount of unstructured data into information that can help the system reduce readmissions.

More than 80 percent of a healthcare organization's data is unstructured, including physician notes, registration forms, discharge summaries and other nonstandardized electronic forms, which complicates traditional data analysis.

So says IBM, the provider of UNC Health Care's Smarter Care solution, which uses a Watson-like natural language processing technology to interpret written notes and other documents and provide actionable information to the health system. Natural language processing finds red flags in unstructured data to help identify at-risk patients and allow the hospital to intervene. The technology can also put patients' medical information into a simplified format to be given to the patient, fostering patient engagement.

"Using conventional methods, too many patients fall through the cracks," Carlton Moore, MD, an associate professor at the University of North Carolina School of Medicine in Chapel Hill, told Baseline Magazine. "A lot of unstructured data in medical records and other places is lost. Natural-language processing can spot things that people miss."

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