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DeLisle S (VA Maryland Health Care System), Ma Z
(Johns Hopkins Hospital), South B
(VA Salt Lake City Health Care System), Smith G
(Univ. of Pennsylvania), Loftus S
(VA Maryland HCS), Samore M
(VA Salt Lake City HCS), Perl TM
(Johns Hopkins Hospital)
Objectives:
Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition. This work uses a mathematical model of a plausible influenza epidemic to test how the statistical characteristics of single-case detection algorithms (CDAs) impact the real-world performance of a VA-based disease surveillance system.
Methods:
A manual electronic medical record (EMR) review of 15,377 outpatient encounters at the Veterans Administration health system (VA) uncovered individuals with influenza-like illness (ILI). These cases served to develop CDAs that utilized various logical arrangements of structured EMR data and automated analyses of the free-text computerized clinical notes. We applied 18 of the most successful CDAs to EMR data extracts and generated 18 distinct real-world substrate datasets of daily ILI encounters at the Baltimore VA. We then injected an age-structured, spatiotemporal modeled epidemic into each of these background datasets, and simulated prospective outbreak detection with either scanning (SatScan) or CUSUM statistical approaches.
Results:
The most desirable CDAs were those that coupled single-case detection sensitivity above 75% with high specificity or positive predictive value. Compared to ICD-9-only CDAs in use by current national surveillance systems, the best EMR-based CDAs shortened outbreak detection delay by 3 days and decreased the false-alert rate ~15-fold.
Implications:
Case detection methods that take advantage of information from the full EMR can lower both the delay and the workload required to find an influenza epidemic in the community.
Impacts:
Automated, high-performance EMR-based recognition of diseases or events requires methodical development, but offers novel and powerful avenues toward system-wide improvements in health care quality and safety.
