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Objective: Automated surveillance of healthcare-associated infections reduces workload and improves standardization, but it has not yet been adopted widely. In this study, we assessed the performance and feasibility of an easy implementable framework to develop algorithms for semiautomated surveillance of deep incisional and organ-space surgical site infections (SSIs) after orthopedic, cardiac, and colon surgeries., Design: Retrospective cohort study in multiple countries., Methods: European hospitals were recruited and selected based on the availability of manual SSI surveillance data from 2012 onward (reference standard) and on the ability to extract relevant data from electronic health records. A questionnaire on local manual surveillance and clinical practices was administered to participating hospitals, and the information collected was used to pre-emptively design semiautomated surveillance algorithms standardized for multiple hospitals and for center-specific application. Algorithm sensitivity, positive predictive value, and reduction of manual charts requiring review were calculated. Reasons for misclassification were explored using discrepancy analyses., Results: The study included 3 hospitals, in the Netherlands, France, and Spain. Classification algorithms were developed to indicate procedures with a high probability of SSI. Components concerned microbiology, prolonged length of stay or readmission, and reinterventions. Antibiotics and radiology ordering were optional. In total, 4,770 orthopedic procedures, 5,047 cardiac procedures, and 3,906 colon procedures were analyzed. Across hospitals, standardized algorithm sensitivity ranged between 82% and 100% for orthopedic surgery, between 67% and 100% for cardiac surgery, and between 84% and 100% for colon surgery, with 72%-98% workload reduction. Center-specific algorithms had lower sensitivity., Conclusions: Using this framework, algorithms for semiautomated surveillance of SSI can be successfully developed. The high performance of standardized algorithms holds promise for large-scale standardization.

Purpose of Review: This review describes recent advances in the field of automated surveillance of healthcare-associated infections (HAIs), with a focus on data sources and the development of semiautomated or fully automated algorithms., Recent Findings: The availability of high-quality data in electronic health records and a well-designed information technology (IT) infrastructure to access these data are indispensable for successful implementation of automated HAI surveillance. Previous studies have demonstrated that reliance on stand-alone administrative data is generally unsuited as sole case-finding strategy. Recent attempts to combine multiple administrative and clinical data sources in algorithms yielded more reliable results. Current surveillance practices are mostly limited to single healthcare facilities, but future linkage of multiple databases in a network may allow interfacility surveillance. Although prior surveillance algorithms were often straightforward decision trees based on structured data, recent studies have used a wide variety of techniques for case-finding, including logistic regression and various machine learning methods. In the future, natural language processing may enable the use of unstructured narrative data., Summary: Developments in healthcare IT are rapidly changing the landscape of HAI surveillance. The electronic availability and incorporation of routine care data in surveillance algorithms enhances the reliability, efficiency and standardization of surveillance practices.

Manual surveillance of surgical site infections (SSIs) after total hip or knee arthroplasty is time-consuming and prone to error. Semiautomated surveillance based on routine care data extracted from electronic health records can retrospectively identify deep SSIs and substantially reduce workload while maintaining 100% sensitivity. Infect Control Hosp Epidemiol 2017;38:732-735.