Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Sep 1, 2017

Abstract

OBJECTIVE: Widespread application of clinical natural language processing (NLP) systems requires taking existing NLP systems and adapting them to diverse and heterogeneous settings. We describe the challenges faced and lessons learned in adapting an existing NLP system for measuring colonoscopy quality.

Authors

  • David S Carrell
    Group Health Research Institute, Seattle, WA, 98101, USA.
  • Robert E Schoen
    Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine and Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
  • Daniel A Leffler
    Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Michele Morris
    Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Sherri Rose
    Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.
  • Andrew Baer
    Kaiser Permanente of Washington Health Research Institute (formerly Group Health Research Institute), Seattle, WA, USA.
  • Seth D Crockett
    Division of Gastroenterology and Hepatology, University of North Carolina School of Medicine, Chapel Hill, NC, USA.
  • Rebecca A Gourevitch
    Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
  • Katie M Dean
    Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
  • Ateev Mehrotra
    Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.

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