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Use of natural language processing method to identify regional anesthesia from clinical notes
  1. Laura A Graham1,
  2. Samantha S Illarmo2,
  3. Sherry M Wren3,4,
  4. Michelle C Odden2,5 and
  5. Seshadri C Mudumbai6,7
  1. 1Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, California, USA
  2. 2VA Palo Alto Health Care System, Palo Alto, California, USA
  3. 3Department of General Surgery, VA Palo Alto Health Care System, Palo Alto, California, USA
  4. 4Department of Surgery, Stanford University School of Medicine, Stanford, California, USA
  5. 5Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA
  6. 6Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
  7. 7Anesthesiology and Perioperative Care Service, VA Palo Alto Health Care System, Palo Alto, California, USA
  1. Correspondence to Dr Laura A Graham, Health Economics Resource Center, VA Palo Alto Health Care System, Menlo Park, California, USA; lagraham{at}stanford.edu

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.

Methods We obtained postoperative clinical notes for all patients undergoing elective non-cardiac surgery with general anesthesia at one of six Veterans Health Administration hospitals in California between January 1, 2017, and December 31, 2022. After developing and executing our algorithm, we compared our results to a frequently used referent, the Corporate Data Warehouse structured data, to assess the completeness and accuracy of the currently available data. Measures of agreement included sensitivity, positive predictive value, false negative rate, and accuracy.

Results We identified 27,713 procedures, of which 9310 (33.6%) received regional anesthesia. 96.6% of all referent regional anesthesia cases were identified in the clinic notes with a very low false negative rate and good accuracy (false negative rate=0.8%, accuracy=82.5%). Surprisingly, the clinic notes documented more than two times the number of regional anesthesia cases that were documented in the referent (algorithm n=9154 vs referent n=4606).

Discussion While our algorithm identified nearly all regional anesthesia cases from the referent, it also identified more than two times as many regional anesthesia cases as the referent, raising concerns about the accuracy and completeness of regional anesthesia documentation in administrative and clinical databases. We found that NLP was a promising alternative for identifying clinical information when existing databases lack complete documentation.

  • REGIONAL ANESTHESIA
  • Methods
  • Epidemiology

https://doi.org/10.1136/rapm-2024-105340

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    Footnotes

    • Twitter @SMudumbaiDoc

    • Contributors LG and SI acquired funding and data for the analysis. LG developed the framework and the code, analyzed the data, and wrote the manuscript. SI, SCM, MCO, and SMW aided in interpreting the results and critically reviewed the manuscript. All authors discussed the results and commented on the manuscript.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.