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Charting the course: natural language processing unveils regional anesthesia procedures in clinical records – an infographic
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  1. Ryan S D'Souza1,
  2. Eric S Schwenk2 and
  3. Laura A Graham3
  1. 1Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, USA
  2. 2Department of Anesthesiology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, USA
  3. 3VA Palo Alto Health Care System, Palo Alto, USA
  1. Correspondence to Dr Ryan S D'Souza, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, USA; DSouza.Ryan{at}mayo.edu

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Summary

Documentation practices for regional anesthesia vary widely due to limited guidance, which can result in missing and inaccurate data capture. This study developed a natural language processing (NLP) algorithm to identify regional anesthesia in unstructured clinical notes, and compared results to the current referent for regional anesthesia research data (Corporate Data Warehouse structured data).1 The analysis included postoperative notes from elective non-cardiac surgeries at one of six Veterans Health Administration hospitals in California between January 1, 2017 and December 31, 2022. Results showed that the algorithm identified 96.6% of the regional anesthesia cases recorded in the referent, with a low false negative rate of 0.8% and an accuracy of 82.5%. Notably, the NLP algorithm found more than twice as many regional anesthesia cases as the referent, indicating that there might be issues with the accuracy and completeness of documentation in research databases. This suggests that NLP is a promising tool for improving the completeness and accuracy of clinical documentation.

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Footnotes

  • Contributors RSD and ESS was involved in infographic planning, creation, and revision. LAG was involved in infographic creation and revision.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests RSD is an Associate Editor of Regional Anesthesia and Pain Medicine. RSD receives investigator-initiated research grant funding paid to his institution from Nevro Corp and Saol Therapeutics.

  • Provenance and peer review Commissioned; internally peer reviewed.