ArticleGlobal spine journal2023
Can Natural Language Processing and Artificial Intelligence Automate The Generation of Billing Codes From Operative Note Dictations?
Article in Global spine journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining the Role of Large Language Models in Orthopedics: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Artificial Intelligence-Assisted Current Procedural Terminology Coding in Pediatric Orthopaedic Surgery: Promise or Premature?Journal of the Pediatric Orthopaedic Society of North America · 2026Article
- Natural Language Processing for Automated Classification of Cleft and Craniofacial Procedures From Operative Notes: Model Development and Feasibility Study.JMIR medical informatics · 2026Observational
- Real-time automated billing for tobacco treatment: performance evaluation of the CigStopper machine learning framework.JAMIA open · 2026Article
- LLM-augmented semantic embeddings enable Cross-Lingual mapping of medical procedure terms.Scientific reports · 2026Article
- Online survey assessing US primary care physicians' attitudes toward AI use in clinical administrative tasks.BMJ health & care informatics · 2026Article
- Artificial intelligence-assisted medical coding and DRG management: current applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
- Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Review
- Current trends and future prospects of language models and processing systems in spine surgery - a scoping review.Neurosurgical review · 2025Article
- Learning health system strategies in the AI era.npj health systems · 2025Review
- Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning.World journal of otorhinolaryngology - head and neck surgery · 2025Article
- Article
- Artificially Intelligent Billing in Spine Surgery: An Analysis of a Large Language Model.Global spine journal · 2025Article
- Artificial Intelligence as a Tool for Creating Patient Visit Summary: A Scoping Review and Guide to Implementation in an Erectile Dysfunction Clinic.Current urology reports · 2024Article
- Automating surgical procedure extraction for society of surgeons adult cardiac surgery registry using pretrained language models.JAMIA open · 2024Article
- Can a Novel Natural Language Processing Model and Artificial Intelligence Automatically Generate Billing Codes From Spine Surgical Operative Notes?Global spine journal · 2024Article
- Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics.BMJ health & care informatics · 2024Article
- Orthopaedic surgeons display a positive outlook towards artificial intelligence: A survey among members of the AGA Society for Arthroscopy and Joint Surgery.Journal of experimental orthopaedics · 2024Article
- Current Applications of Artificial Intelligence in Billing Practices and Clinical Plastic Surgery.Plastic and reconstructive surgery. Global open · 2024Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designRetrospective Cohort Study.
objectivesUsing natural language processing (NLP) in combination with machine learning on standard operative notes may allow for efficient billing, maximization of collections, and minimization of coder error. This study was conducted as a pilot study to determine if a machine learning algorithm can accurately identify billing Current Procedural Terminology (CPT) codes on patient operative notes.
methodsThis was a retrospective analysis of operative notes from patients who underwent elective spine surgery by a single senior surgeon from 9/2015 to 1/2020. Algorithm performance was measured by performing receiver operating characteristic (ROC) analysis, calculating the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC). A deep learning NLP algorithm and a Random Forest algorithm were both trained and tested on operative notes to predict CPT codes. CPT codes generated by the billing department were compared to those generated by our model.
resultsThe random forest machine learning model had an AUC of .94 and an AUPRC of .85. The deep learning model had a final AUC of .72 and an AUPRC of .44. The random forest model had a weighted average, class-by-class accuracy of 87%. The LSTM deep learning model had a weighted average, class-by-class accuracy 0f 59%.
conclusionsCombining natural language processing with machine learning is a valid approach for automatic generation of CPT billing codes. The random forest machine learning model outperformed the LSTM deep learning model in this case. These models can be used by orthopedic or neurosurgery departments to allow for efficient billing.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.