Identifying colon and open reduction of fracture surgical site infections using a partially automated electronic algorithm

Background: Algorithms leveraging electronic data may reduce manual review burden for surgical site infection (SSI) surveillance with little to no reduction in sensitivity. We developed an algorithm to identify colon and open reduction of fracture (FX) SSIs to reduce manual chart review.

Methods: A retrospective cohort of colon and FX procedures and associated SSIs was constructed. Potential SSIs were identified by positive microbiologic cultures or administrative data for diagnosis or treatment of wound infection. Sensitivity and specificity of the algorithm were assessed. The number of charts needing review to identify 1 SSI, and the potential time-savings from the algorithm, were calculated.

Results: Four hundred seventy-three colon (SSI rate = 7%) and 1081 FX (SSI rate = 3%) procedures were identified. The algorithm was 91% and 97% sensitive and 76% and 93% specific for colon and FX procedures, respectively. Overall, chart review would have been reduced by 24.3 hours per 100 procedures, decreasing the number of charts to review to identify 1 SSI from 23.9 for manual review to 3.9 with the algorithm.

Conclusions: The algorithm identified SSIs with excellent sensitivity and specificity, resulting in substantial reductions in manual chart review. This algorithm could be tailored and applied to other hospitals.