<< Back to the abstract archive
Quality Assessment and Coding Implications of Hand Trauma Operative Reports
Ermina Lee, BS; Tiffany Shi, PhD; Victoria Lee, BS; Kelly Spiller, MD; Maleeh Effendi, MD; Ryan Gobble, MD; Ann R. Schwentker, MD
University of Cincinnati
2025-01-21
Presenter: Ermina Lee
Affidavit:
Ermina, Tiffany, Victoria, and Dr. Schwentker contributed to data acquisition. All co-authors contributed to project design and approved of the abstract.
Director Name: Ann R. Schwentker
Author Category: Medical Student
Presentation Category: Clinical
Abstract Category: Hand
Background:
Medical billing in hand trauma is error-prone, requiring anatomical understanding for billers to assign unbundled Current Procedural Terminology (CPT) codes. Surgeons may aid in this process by authoring detailed yet concise notes. This study assesses operative report quality and the ability to support coding.
Methods:
20 hand trauma operations were identified from retrospective review of 2018-2023. Operative reports were evaluated using an expert hand surgeon's adaptation of the Structured Assessment Format for Evaluating Operative Reports (SAFE-OR). The corresponding billing data was compared to the surgeon's assigned CPTs. Relative Value Units (RVUs) were assigned using the Centers for Medicare and Medicaid Services Physician Fee Schedule.
Results:
Report authors were authored evenly between attendings, fellows, and senior residents (p=0.69) with an average of 1.5 days passing between the operation and when the note was signed. Overall SAFE-OR scores were high (92.5%) and no significant scoring differences (p=0.16) were seen between author types. Despite this, 29.4% (15/51) of billed CPT codes were not supported by documentation and 19 CPT codes were omitted despite procedural description. Additionally, 6/20 (30%) of operative notes had anatomical inconsistencies or issues with technique description. A lack of documented defect size was consistent, resulting in improper CPT code assignments.
Conclusions:
Hand trauma medical billing is error-prone even with high-quality operative reports. Quality improvement can identify consistent discrepancies between documentation and billing. These learnings can standardize documentation templates for improved billing accuracy and refine artificial intelligence models, which have long been employed by insurers to review claims.