Tracking medical students' clinical experiences using natural language processing

  • Authors:

  • Joshua C. Denny;Lisa Bastarache;Elizabeth Ann Sastre;Anderson Spickard, III

  • Affiliations:

  • Department of Biomedical Informatics, Vanderbilt University Medical Center, Eskind Biomedical Library, Room 442, 2209 Garland Ave., Nashville, TN 37232, USA and Division of General Internal Medici ...;Department of Biomedical Informatics, Vanderbilt University Medical Center, Eskind Biomedical Library, Room 442, 2209 Garland Ave., Nashville, TN 37232, USA;Division of General Internal Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA;Department of Biomedical Informatics, Vanderbilt University Medical Center, Eskind Biomedical Library, Room 442, 2209 Garland Ave., Nashville, TN 37232, USA and Division of General Internal Medici ...

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2009

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Abstract

Graduate medical students must demonstrate competency in clinical skills. Current tracking methods rely either on manual efforts or on simple electronic entry to record clinical experience. We evaluated automated methods to locate 10 institution-defined core clinical problems from three medical students' clinical notes (n=290). Each note was processed with section header identification algorithms and the KnowledgeMap concept identifier to locate Unified Medical Language System (UMLS) concepts. The best performing automated search strategies accurately classified documents containing primary discussions to the core clinical problems with area under receiver operator characteristic curve of 0.90-0.94. Recall and precision for UMLS concept identification was 0.91 and 0.92, respectively. Of the individual note section, concepts found within the chief complaint, history of present illness, and assessment and plan were the strongest predictors of relevance. This automated method of tracking can provide detailed, pertinent reports of clinical experience that does not require additional work from medical trainees. The coupling of section header identification and concept identification holds promise for other natural language processing tasks, such as clinical research or phenotype identification.