Can a Novel Natural Language Processing Model and Artificial Intelligence Automatically Generate Billing Codes From Spine Surgical Operative Notes?

Autor:	Zaidat B; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Tang J; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Arvind V; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Geng EA; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Cho B; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Duey AH; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Dominy C; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Riew KD; Department of Neurological Surgery, Weill Cornell Medical Center- Och Spine Hospital, New York, NY, USA., Cho SK; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Kim JS; Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Jazyk:	angličtina
Zdroj:	Global spine journal [Global Spine J] 2024 Sep; Vol. 14 (7), pp. 2022-2030. Date of Electronic Publication: 2023 Mar 18.
DOI:	10.1177/21925682231164935
Abstrakt:	Study Design: Retrospective cohort. Objective: 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. Methods: 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. Results: 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%). Conclusions: We show that the XLNet model can be successfully applied to orthopedic surgeon's 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. Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JK, Stryker: Paid consultant, SK, FAAOS; AAOS: Board or committee member; American Orthopaedic Association: Board or committee member; AOSpine North America: Board or committee member; Cervical Spine Research Society: Board or committee member; Globus Medical: IP royalties; North American Spine Society: Board or committee member; Scoliosis Research Society: Board or committee member; Stryker: Paid consultant; KR, FAAOS; America: Stock or stock Options; AOSpine: Board or committee member; AxioMed: Stock or stock Options; Benvenue: Stock or stock Options; Biomet: IP royalties; Paid presenter or speaker; Clinics in orthopedics: Editorial or governing board; European Spine Journal: Editorial or governing board; Expanding Orthopedics, PSD: Stock or stock options; Global Spine Journal: Editorial or governing board; HAPPE Spine: Unpaid consultant; Medtronic: Paid presenter or speaker; Neurosurgery: Editorial or governing board; North American Spine Society: Board or committee member; Nuvasive: Paid consultant; Paid presenter or speaker; Paradigm Spine: Stock or stock Options; Spinal Kinetics: Stock or stock Options; Spine: Editorial or governing board; Spine Surgery Today: Editorial or governing board; Spineology: Stock or stock Options; Vertiflex: Stock or stock Options. The following individuals have no conflicts of interest or sources of support that require acknowledgement: BZ, JT, VA, BC, AD, EG, CD.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu