Lizewski Rhonda

The Armed Forces Health Surveillance Center (AFHSC) supports the development of new analytical tools to improve alerting in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) disease-monitoring application used by the Department of Defense (DoD). Developers at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) have added an analytic capability to alert the user when corroborating evidence exists across syndromic and clinical data streams including laboratory tests and filled prescription records. In addition, AFHSC epidemiologists have guided the addition of data streams related to case severity for monitoring of events expected to require expanded medical resources. Evaluation of the multi-level fusion capability for both accuracy and utility is a challenge that requires feedback from the user community before implementation and deployment so that changes to the design can be made to save both time and money. The current effort describes the design and results of a large evaluation exercise.

Objective

To evaluate, prior to launch, a surveillance system upgrade allowing analytical combination of weighted clinical and syndromic evidence with multiple severity indicators.

Data streams related to case severity have been added to the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), a disease-monitoring application used by the Department of Defense (DoD), as an additional analytic capability to alert the user when indications for events requiring expanded medical resources exist in clinical data streams. Commonly used indicators are admission and death, but fatalities are rare and many DoD clinics lack admitting capability, so we sought to derive additional severity indicators from outpatient records. This abstract describes the technical details and the thought process behind two novel derived indicators: Sick-in-Quarters (SIQ) and Escalating Care.

Objective

To evaluate new severity indicators that mimic a public health professional or clinician's judgment in determining the severity of a public health event when detected by a surveillance system.