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RESOURCE: How natural language processing can help EHRs

Consider natural language processing (NLP), a technology that can produce readable summaries of chunks of text. Basic examples of NLP include social media, newspaper articles, and, as the Parliament of Canada and the European Union have done, translating governmental proceedings into all official languages. But this is just the tip of the iceberg. NLP can do much, much more, including deciphering doctors’ notes and other unstructured information generated during patient visits. NLP can take EHRs to an entirely different level.

While turning unstructured data into something useful may not get your juices flowing, many people feel passionately about the sub­ject. Count among them tech-savvy doctors like Jaan Sidorov and Kevin Pho, the web’s top social media influencer in health care and medicine according to Klout. In an article on KevinMD (Pho’s site), Sidorov cites statistics that an astonishing 80 percent of clinical docu­mentation existing in health care today is unstructured. Yet that infor­mation is largely ignored

via How natural language processing can help EHRs.

Brian S McGowan, PhD

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Brian is a research scientist and educational technologist. He helped transform Pfizer’s Medical Education Group and previously served in educational leadership roles at HealthAnswers, Inc.; Acumentis, LLC.; Cephalon; and Wyeth. He taught graduate medical education programs at Arcadia University for 10 years. Dr. McGowan recently authored the book "#socialQI: Simple Solutions for Improving Your Healthcare" and has been invited to speak internationally on the subject of information flow, technology, and learning in healthcare.

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