Implementation and evaluation of an automated surveillance system to detect hospital outbreak

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Abstract

Background:

The timely identification of a cluster is a critical requirement for infection prevention and control (IPC) departments because these events may represent transmission of pathogens within the health care setting. Given the issues with manual review of hospital infections, a surveillance system to detect clusters in health care settings must use automated data capture, validated statistical methods, and include all significant pathogens, antimicrobial susceptibility patterns, patient care locations, and health care teams.

Methods:

We describe the use of SaTScan statistical software to identify clusters, WHONET software to manage microbiology laboratory data, and electronic health record data to create a comprehensive outbreak detection system in our hospital. We also evaluated the system using the Centers for Disease Control and Prevention's guidelines.

Results:

During an 8-month surveillance time period, 168 clusters were detected, 45 of which met criteria for investigation, and 6 were considered transmission events. The system was felt to be flexible, timely, accepted by the department and hospital, useful, and sensitive, but it required significant resources and has a low positive predictive value.

Conclusions:

WHONET-SaTScan is a useful addition to a robust IPC program. Although the resources required were significant, this prospective, real-time cluster detection surveillance system represents an improvement over historical methods. We detected several episodes of transmission which would have eluded us previously, and allowed us to focus infection prevention efforts and improve patient safety.

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