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Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record

Author

Listed:
  • Joshua C. Denny
  • Neesha N. Choma
  • Josh F. Peterson
  • Randolph A. Miller
  • Lisa Bastarache
  • Ming Li
  • Neeraja B. Peterson

Abstract

Background . Difficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates. Objective . To use Electronic Health Record (EHR) data to identify patients with prior CRC testing. Design . A clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests. Setting . Large academic medical center. Patients . 200 patients ≥50 years old who had completed ≥2 non-acute primary care visits within a 1-year period. Measures . Recall and precision of the NLP system, billing records, and human chart review were compared to a reference standard of human review of all available information sources. Results . For identification of all CRC tests, recall and precision were as follows: NLP system (recall 93%, precision 94%), chart review (74%, 98%), and billing records review (44%, 83%). Recall and precision for identification of patients in need of screening were: NLP system (recall 95%, precision 88%), chart review (99%, 82%), and billing records (99%, 67%). Limitations . Small sample size and requirement for a robust EHR. Conclusions . Applying NLP to EHR records detected more CRC tests than either manual chart review or billing records review alone. NLP had better precision but marginally lower recall to identify patients who were due for CRC screening than billing record review.

Suggested Citation

  • Joshua C. Denny & Neesha N. Choma & Josh F. Peterson & Randolph A. Miller & Lisa Bastarache & Ming Li & Neeraja B. Peterson, 2012. "Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record," Medical Decision Making, , vol. 32(1), pages 188-197, January.
    • Handle: RePEc:sae:medema:v:32:y:2012:i:1:p:188-197
    DOI: 10.1177/0272989X11400418
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    References listed on IDEAS

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    1. Montano, D.E. & Phillips, W.R., 1995. "Cancer screening by primary care physicians: A comparison of rates obtained from physician self-report, patient survey, and chart audit," American Journal of Public Health, American Public Health Association, vol. 85(6), pages 795-800.
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    Cited by:

    1. Sebastian Gehrmann & Franck Dernoncourt & Yeran Li & Eric T Carlson & Joy T Wu & Jonathan Welt & John Foote Jr. & Edward T Moseley & David W Grant & Patrick D Tyler & Leo A Celi, 2018. "Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-19, February.

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