A digital trigger-based model for epidemiological surveillance of healthcare-associated infections in a multidisciplinary hospital: development and implementation

Abstract

Aim . To develop and implement a digital trigger-based system for HAI surveillance integrated into a hospital information system of a multidisciplinary hospital and to evaluate its effectiveness. Materials and Methods . A retrospective single-center observational study was conducted in a multidisciplinary hospital (83 beds, including 11 intensive care unit beds). The analysis was based on data obtained from the operation of a digital epidemiological surveillance system during the period from January 1, 2025, to December 31, 2025. The development and implementation of the digital surveillance system had been completed before the beginning of the study period. The system was based on trigger-driven identification of potential healthcare-associated infection (HAI) cases using objective events recorded in the hospital information system, namely positive microbiological test results and the occurrence of surgical procedures. When a trigger event was detected, an electronic task was automatically generated for the attending physician to complete a standardized structured checklist without free-text entry. Case classification was performed according to standard case definitions using clinical, laboratory, and temporal criteria. To ensure completeness of case registration, the system incorporated mandatory confirmation of entered data, task completion monitoring, and selective case verifi cation by the hospital epidemiologist. Results . During the study period, 2,037 surgical procedures were performed. Device utilization metrics were: 2,969 central line-days, 2,321 urinary catheter-days, and 1,207 ventilator-days. HAI rates were: central line-associated bloodstream in fections (CLABSI) – 4.3 per 1,000 central line-days; catheter-associ ated urinary tract infections (CAUTI) – 2.1 per 1,000 catheter-days; ventilator-associated pneumonia (VAP) – 4.1 per 1,000 ventilator-days; surgical site infections (SSI) – 0.8 per 100 procedures. A total of 394 trigger events were registered, of which 43 cases (11.0 %) were clas sified as HAIs. All generated tasks were completed. In 10% of cases, data clarification was required during verification. No additional HAI cases not captured by the system were identified. Conclusion . The dig ital trigger-based surveillance system integrated into the hospital information system ensures standardized HAI detection and high case as certainment completeness. The use of automated algorithms, structured checklists, and multi-level control improves reproducibility of epidemi ological indicators and can be considered an effective tool for organizing epidemiological surveillance.