Revamping medical coding with AI: a systematic review of interdisciplinary applications and perspectives for plastic surgery

Abstract

Aim: This review evaluates the use of artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) technologies for enhancing current procedural terminology (CPT) coding accuracy and efficiency in plastic and reconstructive surgery and related disciplines to define a precedent for future implementation.

Methods: A systematic search of PubMed, Scopus, and Web of Science Core Collection was performed to identify studies that leveraged artificially intelligent technologies in coding related to surgical procedures commonly managed by plastic and reconstructive surgeons.

Results: 11 peer-reviewed articles, which encompassed more than 1,000 CPT codes across numerous surgical subspecialties with overlap in plastic and reconstructive surgery and model systems, were included. The key findings highlight that AI-driven models demonstrate high sensitivity, specificity, area under the receiver operating curve (AUROC), and accuracy. While performance metrics varied considerably depending on the specific AI model employed, these systems were found to be effective assistive technologies for medical coding. Studies underscored the advantages of integration, maximizing billing workflow and reducing administrative workload. However, studies of AI performance for billing and medical coding within plastic surgery settings specifically were sparse. Notably, these investigations emphasized the need to tailor models for targeted suitability.

Conclusions: This review highlights the potential of AI technologies to improve CPT coding by enabling time and resource management and ultimately combatting the mounting presence of surgeon burnout. The sparsity of plastic surgery-specific literature on this emerging topic and untested promise in the specialty calls for intentional plastic surgeon-driven initiatives in the development of such applications.

Keywords

CPT / artificial intelligence / machine learning / natural language processing / surgery / billing / reimbursement

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