Developing a Surgical Site Infection Surveillance System Based on Hospital Unstructured Clinical Notes and Text Mining

Background: Electronic surveillance using clinical and administrative data from multiple sources has been reported as a tool for surveillance of surgical site infections (SSIs), but experiences are limited. In this study, we aimed to assess the accuracy of a text-searching algorithm to detect SSIs in children based on the application of regular expressions of unstructured clinical notes collected through different information systems. Methods: We developed an information system data warehouse that integrates data provided by electronic health and administrative records for patients who underwent surgical procedures in index weeks when active SSIs surveillances was conducted. To capture whether the patient developed an SSI, we developed a customized application to analyze clinical notes and code descriptions applying a pattern-matching algorithm based on regular expressions. We described the SSI cases detected by the active surveillance and the text-searching algorithm. To assess the accuracy in identifying the SSIs through the two methods, we adopted a reference standard that calculated the total number of SSIs as those detected by active surveillance plus those derived by the text-searching algorithm that was missed by active surveillance. Results: Compared with the total number of SSIs used as a reference standard, both methods had a specificity of 100%, a positive predictive value of 100%, and a negative predictive value >99.5%. Sensitivity was 70% for the text-mining algorithm and 60% for the active surveillance. Accuracy was >99% with both methods. The kappa value was 0.46. Conclusions: Compared with conventional surveillance of SSIs, a text-searching algorithm is a valid tool for case finding that has the potential to reduce drastically the workload of conventional surveillance, which involved direct contact with all families.