{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T12:50:03Z","timestamp":1792759803809,"version":"4.5.0"},"reference-count":17,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T00:00:00Z","timestamp":1679097600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Global Spine Journal"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Retrospective cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Billing and coding-related administrative tasks are a major source of healthcare expenditure in the United States. We aim to show that a second-iteration Natural Language Processing (NLP) machine learning algorithm, XLNet, can automate the generation of CPT codes from operative notes in ACDF, PCDF, and CDA procedures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We collected 922 operative notes from patients who underwent ACDF, PCDF, or CDA from 2015 to 2020 and included CPT codes generated by the billing code department. We trained XLNet, a generalized autoregressive pretraining method, on this dataset and tested its performance by calculating AUROC and AUPRC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The performance of the model approached human accuracy. Trial 1 (ACDF) achieved an AUROC of .82 (range: .48-.93), an AUPRC of .81 (range: .45-.97), and class-by-class accuracy of 77% (range: 34%-91%); trial 2 (PCDF) achieved an AUROC of .83 (.44-.94), an AUPRC of .70 (.45-.96), and class-by-class accuracy of 71% (42%-93%); trial 3 (ACDF and CDA) achieved an AUROC of .95 (.68-.99), an AUPRC of .91 (.56-.98), and class-by-class accuracy of 87% (63%-99%); trial 4 (ACDF, PCDF, CDA) achieved an AUROC of .95 (.76-.99), an AUPRC of .84 (.49-.99), and class-by-class accuracy of 88% (70%-99%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We show that the XLNet model can be successfully applied to orthopedic surgeon\u2019s operative notes to generate CPT billing codes. As NLP models as a whole continue to improve, billing can be greatly augmented with artificial intelligence assisted generation of CPT billing codes which will help minimize error and promote standardization in the process.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/21925682231164935","type":"journal-article","created":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T03:39:21Z","timestamp":1679110761000},"page":"2022-2030","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":33,"title":["Can a Novel Natural Language Processing Model and Artificial Intelligence Automatically Generate Billing Codes From Spine Surgical Operative Notes?"],"prefix":"10.1177","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8823-720X","authenticated-orcid":false,"given":"Bashar","family":"Zaidat","sequence":"first","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4544-3262","authenticated-orcid":false,"given":"Justin","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varun","family":"Arvind","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0736-3245","authenticated-orcid":false,"given":"Eric A.","family":"Geng","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0308-0512","authenticated-orcid":false,"given":"Akiro H.","family":"Duey","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6572-585X","authenticated-orcid":false,"given":"Calista","family":"Dominy","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0718-3423","authenticated-orcid":false,"given":"Kiehyun D.","family":"Riew","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, Weill Cornell Medical Center- Och Spine Hospital, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel K.","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun S.","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"bibr1-21925682231164935","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2021.17318"},{"key":"bibr2-21925682231164935","doi-asserted-by":"publisher","DOI":"10.15265\/IY-2014-0020"},{"key":"bibr3-21925682231164935","doi-asserted-by":"publisher","DOI":"10.1177\/21925682211062831"},{"key":"bibr4-21925682231164935","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1906.08237"},{"key":"bibr5-21925682231164935","volume-title":"Natural Language Processing With Python","author":"Bird 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