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Identifying colon and open reduction of fracture surgical site infections using a partially automated electronic
Bryan C Knepper1, Heather Young2, Sara M Reese1
1Department of Patient Safety and Quality, Denver Health Medical Center, Denver, CO.
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.
Insights
An algorithm effectively identifies surgical site infections (SSIs) for colon and fracture procedures, significantly reducing manual chart review time and improving surveillance efficiency.
Area of Science:
- Medical informatics
- Infectious disease surveillance
- Surgical outcomes research
Background:
- Manual review of surgical site infection (SSI) data is time-consuming.
- Electronic health records offer potential for automated SSI surveillance.
- Developing algorithms can streamline SSI detection and reduce workload.
Purpose of the Study:
- To create and validate an algorithm for identifying colon and open reduction of fracture (FX) SSIs.
- To assess the algorithm's sensitivity and specificity compared to manual review.
- To quantify the potential time savings and reduction in manual chart review.
Main Methods:
- Retrospective cohort study of colon and FX procedures.
- Algorithm development using positive microbiologic cultures and administrative data.
- Calculation of algorithm sensitivity, specificity, and impact on chart review workload.
Main Results:
- The algorithm demonstrated high sensitivity (91% for colon, 97% for FX) and specificity (76% for colon, 93% for FX).
- Implementation reduced charts needing review per SSI from 23.9 to 3.9.
- An estimated 24.3 hours of manual review time saved per 100 procedures.
Conclusions:
- The developed algorithm accurately identifies SSIs with high sensitivity and specificity.
- This tool significantly reduces the burden of manual chart review for SSI surveillance.
- The algorithm is adaptable for use in other healthcare settings.
