{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T12:26:40Z","timestamp":1784723200028,"version":"3.55.0"},"reference-count":27,"publisher":"BMJ","issue":"4","content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Reg Anesth Pain Med"],"accepted":{"date-parts":[[2023,6,24]]},"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Large language models have been gaining tremendous popularity since the introduction of ChatGPT in late 2022. Perioperative pain providers should leverage natural language processing (NLP) technology and explore pertinent use cases to improve patient care. One example is tracking persistent postoperative opioid use after surgery. Since much of the relevant data may be \u2018hidden\u2019 within unstructured clinical text, NLP models may prove to be advantageous. The primary objective of this proof-of-concept study was to demonstrate the ability of an NLP engine to review clinical notes and accurately identify patients who had persistent postoperative opioid use after major spine surgery.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Clinical documents from all patients that underwent major spine surgery during July 2015\u2013August 2021 were extracted from the electronic health record. The primary outcome was persistent postoperative opioid use, defined as continued use of opioids greater than or equal to 3\u2009months after surgery. This outcome was ascertained via manual clinician review from outpatient spine surgery follow-up notes. An NLP engine was applied to these notes to ascertain the presence of persistent opioid use\u2014this was then compared with results from clinician manual review.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The final study sample consisted of 965 patients, in which 705 (73.1%) were determined to have persistent opioid use following surgery. The NLP engine correctly determined the patients\u2019 opioid use status in 92.9% of cases, in which it correctly identified persistent opioid use in 95.6% of cases and no persistent opioid use in 86.1% of cases.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>Access to unstructured data within the perioperative history can contextualize patients\u2019 opioid use and provide further insight into the opioid crisis, while at the same time improve care directly at the patient level. While these goals are in reach, future work is needed to evaluate how to best implement NLP within different healthcare systems for use in clinical decision support.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/rapm-2023-104629","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T01:25:18Z","timestamp":1688693118000},"page":"241-247","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":15,"title":["Tracking persistent postoperative opioid use: a proof-of-concept study demonstrating a use case for natural language processing"],"prefix":"10.1136","volume":"49","author":[{"given":"Eri C","family":"Seng","sequence":"first","affiliation":[{"name":"Division of Perioperative Informatics, Department of Anesthesiology, University of California San Diego, La Jolla, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5132-4297","authenticated-orcid":false,"given":"Soraya","family":"Mehdipour","sequence":"additional","affiliation":[{"name":"Division of Perioperative Informatics, Department of Anesthesiology, University of California San Diego, La Jolla, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sierra","family":"Simpson","sequence":"additional","affiliation":[{"name":"Division of Perioperative Informatics, Department of Anesthesiology, University of California San Diego, La Jolla, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4443-0021","authenticated-orcid":false,"given":"Rodney A","family":"Gabriel","sequence":"additional","affiliation":[{"name":"Division of Perioperative Informatics, Department of Anesthesiology, University of California San Diego, La Jolla, California, USA"},{"name":"Division of Regional Anesthesia, Department of Anesthesiology, University of California, San Diego, La Jolla, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"2025101110320992000_49.4.241.1","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/s41591-023-02289-5","article-title":"Will Chatgpt transform Healthcare","volume":"29","year":"2023","journal-title":"Nat Med"},{"key":"2025101110320992000_49.4.241.2","unstructured":"CDC\/NHS . 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