Using Natural Language Processing to Extract Abnormal Results From Cancer Screening Reports.

Journal: Journal of patient safety
Published Date: Sep 1, 2017

Abstract

OBJECTIVES: Numerous studies show that follow-up of abnormal cancer screening results, such as mammography and Papanicolaou (Pap) smears, is frequently not performed in a timely manner. A contributing factor is that abnormal results may go unrecognized because they are buried in free-text documents in electronic medical records (EMRs), and, as a result, patients are lost to follow-up. By identifying abnormal results from free-text reports in EMRs and generating alerts to clinicians, natural language processing (NLP) technology has the potential for improving patient care. The goal of the current study was to evaluate the performance of NLP software for extracting abnormal results from free-text mammography and Pap smear reports stored in an EMR.

Authors

  • Carlton R Moore
    From the *Division of General Medicine and Clinical Epidemiology, Department of Medicine, School of Medicine, †The North Carolina Translational and Clinical Sciences Center, and ‡Department of family Medicine, School of Medicine, University of North Carolina, Chapel Hill, North Carolina.
  • Ashraf Farrag
  • Evan Ashkin

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