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

Background:
Wrong-site surgery (WSS) remains a critical but preventable medical error, often resulting in severe patient harm and substantial financial costs. While protocols exist to reduce WSS, 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 research explores whether the ML approach used for medication error can be adapted to detect surgical errors.

Objective:
We aim to assess the model's transferability and validate its effectiveness in identifying inconsistencies in surgical documentation.

Methods:
We used claims data from the Centers for Medicare & 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 2017–2019 and tested on 2020 orthopedic procedures, using ICD-10-PCS codes to distinguish body part and laterality. Test cases were classified based on alignment between procedural and diagnostic laterality, with two key subgroups (right-left and left-right) 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, focusing on two key subgroups with significant laterality discrepancies between procedures and diagnoses. Among patients with left-side procedures and right-side diagnoses (n=1,106), 54.5% were confirmed as errors after clinical review, while 50.8% of right-side procedures with left-side diagnoses (n=1,064) were also deemed 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 non-error 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.