20783 Evaluating the Impact of Electronic Disease Surveillance Systems On Local Health Department Work Processes

Monday, August 31, 2009: 10:30 AM
Hanover E
Deepthi Rajeev, MS, MSc , Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
Rui Zeller , Division of Epidemiology, University of Utah, Salt Lake City, UT
Andrea Price, LPN , Salt Lake Valley Health Department, Salt Lake County, Salt Lake City, UT
Jon Reid, MBA , Clinical Epidemiology, University of Utah, Salt Lake City, UT
Catherine Staes, BSN, MPH, PhD , Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
Ilene Risk, MPA , Salt Lake Valley Health Department, Salt Lake County, Salt Lake City, UT
Two new electronic systems that enhance communicable disease surveillance in Utah are currently being developed and implemented. First, the Utah Department of Health (UDOH) and local collaborators implemented the Utah-National Electronic Disease Surveillance System (UT-NEDSS) in January 2009; ongoing enhancements are underway.  This PHIN compliant system allows all Utah health departments to have a shared view of reportable cases.  Second, the Real Time-Clinical Electronic Notifiable Disease Reporting System (RT-CEND) is a collaborative effort between UDOH, Intermountain Healthcare, and the University of Utah to electronically transmit case reports from Intermountain Healthcare to UDOH using HL7 v2.5.

Objective: Investigators with the Utah Center of Excellence in Public Health Informatics aim to identify appropriate metrics to evaluate the impact of UT-NEDSS and RT-CEND and implement an ongoing monitoring system that does not interfere with workflow.

Methods: To identify metrics, we conducted an observation study of the work processes at the Salt Lake Valley Health Department (SLVHD) and conducted interviews with epidemiologists and other personnel.  We defined and collected baseline data using observations and existing surveillance data.

Results: We documented the major processes at a local health department when a case report is received and investigated. We selected workflow and surveillance metrics that can be operationalized, including:  Number (%) of out-of county cases received, Total number of unique (non-duplicate) cases received, Number of auxiliary databases (and number of common data fields) needed to manage workflow, and time to complete an investigation. We are currently gathering baseline data.

Conclusion: We have documented work processes involved with managing a case report at a local health department. We have identified appropriate metrics to evaluate the impact of electronic surveillance systems on their work flow and will report on progress with the evaluation as systems are implemented and enhanced.

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