Gastrointestinal disease outbreak detection using multiple data streams from electronic medical records

Background: Passive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect in electronic medical records (EMRs) clinical and laboratory data that could be exploited for surveillance.

Methods: Zip code-specific daily episode counts in 2009 were generated for 22 data streams from Kaiser Permanente Northern California, a large integrated health care delivery system. Data streams included outpatient and inpatient visits and diagnoses; antidiarrheal medication dispensings; orders for stool cultures; and tests positive for 6 GI pathogens. Prospective surveillance with daily univariate and multivariate analyses was mimicked using the space-time permutation scan statistic, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters.

Results: Twenty-nine potential outbreaks were identified, mostly from outpatient GI diagnoses. Potential outbreaks included a cluster of 18 stool cultures ordered over 5 days in 1 zip code and a cluster in 3 zip codes over 9 days, in which at least 5 of 6 Salmonella cases had the same rare serotype: Thompson.

Conclusions: GI disease-related data streams can be generated from EMRs and used to identify potential outbreaks. This process can supplement traditional GI disease outbreak reports to health departments (HDs), which frequently consist of outbreaks in well-defined settings (e.g., day care centers, restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included outpatient diagnoses, orders for stool cultures, and microbiology test results. Healthcare systems could submit cluster information to HDs along with isolates, to prioritize further laboratory testing and improve outbreak detection.