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
Written by Brian S McGowan, PhD
Dr. McGowan has served in leadership positions in numerous medical educational organizations and commercial supporters and is a Fellow of the Alliance (FACEhp). He founded the Outcomes Standardization Project, launched and hosted the Alliance Podcast, and most recently launched and hosts the JCEHP Emerging Best Practices in CPD podcast. In 2012 he Co-Founded ArcheMedX, Inc, a healthcare informatics and e-learning company to apply his research in practice.
