An Evaluation of an Automated Hospital Outbreak Detection System (WHONET-SaTScan) Versus Standard Outbreak Detection Approach

Abstract

Background. A recent survey on current hospital outbreak detection systems (ODS) shows hospitals rely on a manual review of empirical rules of no more than 9 pre-identified organisms to identify potential outbreaks.1 We use a novel software package, WHONET-SaTScan (WS), to facilitate an automated outbreak detection system via space/time to improve standard outbreak detection approach (SODA). Methods. We established WS along with a database to detect, store, and link electronic health record (EHR) data and record results frominvestigations/interventions of clusters to create an automated ODS. We conducted surveillance for 490 bacterial/fungal organisms along with antibiotic resistance phenotypes for 11 organisms. For patients identified as part of a cluster, the following information was extracted from the EHR: possible environmental organism, shared unit, room, medical providers, and antibiotic susceptibility patterns. These data were used to stratify clusters into 2 risk groups (high versus low). Using the CDC's guidelines for evaluating a surveillance system, we assessed system attributes (i.e. simplicity, flexibility, sensitivity, timeliness, and usefulness).2 Results. During an 8-month time period (1 September 2015-30 April 2016), 146 clusters were detected, 39 of which were considered high-risk clusters. Of these 39 clusters, 16 organisms were identified, with S. aureus (21%), S. maltophilia (15%), and K. pneumoniae (13%) representing the majority (figure). Three transmission events were determined to have likely occurred based on demographic factors and review of disinfection practices. Only 1 of these transmission events was identified by an astute clinician. WS did not miss any clusters identified by SODA. The median number of days for WS to detect a cluster was 16. Conclusion. WS is a relatively simple ODS that offers flexibility in creating the parameters (e.g. outbreak window, significance threshold, baseline data) and can identify clusters in space/time. The output is simple to navigate; however, the process to implement software and link with EHR data required additional IT skills. The system has high sensitivity, albeit low specificity. The median time to detection was 16 days; however, many clusters were detected within 3 days. WS is a useful addition to a robust infection control program. (Figure Presented) .