Use of natural language processing method to identify regional anesthesia from clinical notes.

Journal: Regional anesthesia and pain medicine
PMID: 38580338

Abstract

INTRODUCTION: Accurate data capture is integral for research and quality improvement efforts. Unfortunately, limited guidance for defining and documenting regional anesthesia has resulted in wide variation in documentation practices, even within individual hospitals, which can lead to missing and inaccurate data. This cross-sectional study sought to evaluate the performance of a natural language processing (NLP)-based algorithm developed to identify regional anesthesia within unstructured clinical notes.

Authors

  • Laura A Graham
    Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, California, USA lagraham@stanford.edu.
  • Samantha S Illarmo
    VA Palo Alto Health Care System, Palo Alto, California, USA.
  • Sherry M Wren
    Division of General Surgery, Department of Surgery, Stanford University, Stanford, California.
  • Michelle C Odden
    Department of Health Research and Policy, Stanford University, California.
  • Seshadri C Mudumbai
    Anesthesiology and Perioperative Care Service, Veterans Affairs Palo Alto Health Care System, Palo Alto, California.

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