Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study.

Abstract

Background: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial costs. While protocols exist to reduce wrong-site surgery, underreporting and inconsistent documentation continue to contribute to its persistence. Machine learning (ML) models, which have shown success in detecting medication errors, may offer a solution by identifying unusual procedure-diagnosis combinations. This study investigated whether an ML approach can effectively adapt to detect surgical errors.

Objective: This study aimed to evaluate the transferability and effectiveness of an ML-based model for detecting inconsistencies within surgical documentation, particularly focusing on laterality discrepancies.

Methods: We used claims data from the Centers for Medicare and Medicaid Services Limited Data Set (CMS-LDS) from 2017 to 2020, focusing on surgical procedures with documented laterality. We developed an adapted Association Outlier Pattern (AOP) ML model to identify uncommon procedure-diagnosis combinations, specifically targeting discrepancies in laterality. The model was trained on data from 2017 to 2019 and tested on 2020 orthopedic procedures, using ICD-10-PCS (International Classification of Diseases, Tenth Revision, Procedure Coding System) codes to distinguish body part and laterality. Test cases were classified based on alignment between procedural and diagnostic laterality, with 2 key subgroups (right-left and left-right mismatches) identified for evaluation. Model performance was assessed by comparing precision-recall curves and accuracy against rule-based methods.

Results: The findings here included 346,382 claims, of which 2170 claims demonstrated with significant laterality discrepancies between procedures and diagnoses. Among patients with left-side procedures and right-side diagnoses (603/1106), 54.5% were confirmed as errors after clinical review. For right-side procedures with left-side diagnoses (541/1064), 50.8% were classified as errors. The AOP model identified 697 and 655 potentially unusual combinations in the left-right and right-left subgroups, respectively, with over 80% of these cases confirmed as errors following clinical review. Most confirmed errors involved discrepancies in laterality for the same body part, while nonerror cases typically involved general diagnoses without specified laterality.

Conclusions: This investigation showed that the AOP model effectively detects inconsistencies between surgical procedures and diagnoses using CMS-LDS data. The AOP model outperformed traditional rule-based methods, offering higher accuracy in identifying errors. Moreover, the model's transferability from medication-disease associations to procedure-diagnosis verification highlights its broad applicability. By improving the precision of identifying laterality discrepancies, the AOP model can reduce surgical errors, particularly in orthopedic care. These findings suggest that the model enhances patient safety and has the potential to improve clinical decision-making and outcomes.