Novel Visualization of Infections in Intensive Care Units.

BACKGROUND

Accurate and timely surveillance and diagnosis of healthcare-facility onset infection (HO-CDI) is vital to controlling infections within the hospital, but there are limited tools to assist with timely outbreak investigations.

OBJECTIVES

To integrate spatiotemporal factors with HO-CDI cases and develop a map-based dashboard to support infection preventionists (IPs) in performing surveillance and outbreak investigations for HO-CDI.

METHODS

Clinical laboratory results and Admit-Transfer-Discharge data for admitted patients over two years were extracted from the Information Warehouse of a large academic medical center and processed according to Center for Disease Control (CDC) National Healthcare Safety Network (NHSN) definitions to classify infection (CDI) cases by onset date. Results were validated against the internal infection surveillance database maintained by IPs in Clinical Epidemiology of this Academic Medical Center (AMC). Hospital floor plans were combined with HO-CDI case data, to create a dashboard of intensive care units. Usability testing was performed with a think-aloud session and a survey.

RESULTS

The simple classification algorithm identified all 265 HO-CDI cases from 1/1/15-11/30/15 with a positive predictive value (PPV) of 96.3%. When applied to data from 2014, the PPV was 94.6% All users "strongly agreed" that the dashboard would be a positive addition to Clinical Epidemiology and would enable them to present Hospital Acquired Infection (HAI) information to others more efficiently.

CONCLUSIONS

The CDI dashboard demonstrates the feasibility of mapping clinical data to hospital patient care units for more efficient surveillance and potential outbreak investigations.