Identification of Preanesthetic History Elements by a Natural Language Processing Engine.

Journal: Anesthesia and analgesia
Published Date: Jul 15, 2022

Abstract

BACKGROUND: Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes. We hypothesize that the NLP pipeline would identify a significant portion of pertinent history captured by a perioperative provider.

Authors

  • Harrison S Suh
    From the School of Medicine, University of California, San Diego, La Jolla, California.
  • Jeffrey L Tully
    Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, California.
  • Minhthy N Meineke
    Departments of Anesthesiology.
  • Ruth S Waterman
    Department of Anesthesiology, University of California, San Diego, La Jolla, CA, USA.
  • Rodney A Gabriel
    Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.

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