Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification.

Journal: Journal of medical Internet research
Published Date: Jan 28, 2022

Abstract

BACKGROUND: Electronic health records (EHRs) are a rich source of longitudinal patient data. However, missing information due to clinical care that predated the implementation of EHR system(s) or care that occurred at different medical institutions impedes complete ascertainment of a patient's medical history.

Authors

  • Sungrim Moon
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Luke A Carlson
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.
  • Ethan D Moser
    Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
  • Bhavani Singh Agnikula Kshatriya
    Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
  • Carin Y Smith
    Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
  • Walter A Rocca
    Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
  • Liliana Gazzuola Rocca
    Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
  • Suzette J Bielinski
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA. bielinski.suzette@mayo.edu.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Nicholas B Larson
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.

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