Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Total Hip Arthroplasty.

Journal: The Journal of bone and joint surgery. American volume
PMID: 31567670

Abstract

BACKGROUND: Manual chart review is labor-intensive and requires specialized knowledge possessed by highly trained medical professionals. Natural language processing (NLP) tools are distinctive in their ability to extract critical information from raw text in electronic health records (EHRs). As a proof of concept for the potential application of this technology, we examined the ability of NLP to correctly identify common elements described by surgeons in operative notes for total hip arthroplasty (THA).

Authors

  • Cody C Wyles
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
  • Meagan E Tibbo
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
  • Sunyang Fu
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Walter K Kremers
    Departments of Orthopedic Surgery (C.C.W., M.E.T., D.J.B., D.G.L., and H.M.-K.) and Health Sciences Research (S.F., Y.W., S.S., W.K.K., and H.M.-K.), Mayo Clinic, Rochester, Minnesota.
  • Daniel J Berry
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
  • David G Lewallen
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
  • Hilal Maradit-Kremers
    Departments of Orthopedic Surgery (C.C.W., M.E.T., D.J.B., D.G.L., and H.M.-K.) and Health Sciences Research (S.F., Y.W., S.S., W.K.K., and H.M.-K.), Mayo Clinic, Rochester, Minnesota.

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