Broad-based electronic health information exchange (HIE), in which patients’ clinical data follow them between care delivery settings, is expected to produce large quality gains and cost savings. Although these benefits are assumed to result from reducing redundant care, there is limited supporting empirical evidence.
To evaluate whether HIE adoption is associated with decreases in repeat imaging in emergency departments (EDs).
ED discharge data from the State Emergency Department Databases for California and Florida for 2007–2010 were merged with Health Information Management Systems Society data that report hospital HIE participation.
Using regression with ED fixed effects and trends, we performed a retrospective analysis of the impact of HIE participation on repeat imaging, comparing 37 EDs that initiated HIE participation during the study period to 410 EDs that did not participate in HIE during the same period. Within 3 common types of imaging tests [computed tomography (CT), ultrasound, and chest x-ray), we defined a repeat image for a given patient as the same study in the same body region performed within 30 days at unaffiliated EDs.
In our sample there were 20,139 repeat CTs (representing 14.7% of those cases with CT in the index visit), 13,060 repeat ultrasounds (20.7% of ultrasound cases), and 29,703 repeat chest x-rays (19.5% of x-ray cases). HIE was associated with reduced probability of repeat ED imaging in all 3 modalities: −8.7 percentage points for CT [95% confidence interval (CI): −14.7, −2.7], −9.1 percentage points for ultrasound (95% CI: −17.2, −1.1), and −13.0 percentage points for chest x-ray (95% CI: −18.3, −7.7), reflecting reductions of 44%–67% relative to sample means.
HIE was associated with reduced repeat imaging in EDs. This study is among the first to find empirical support for this anticipated benefit of HIE.
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*Mathematica Policy Research
†School of Information
‡School of Public Health, Health Management and Policy
§Department of Emergency Medicine
∥Center for Healthcare Outcomes and Policy (CHOP), University of Michigan, Ann Arbor, MI
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Supported by the STIET doctoral training program and the Health Services Organization and Policy doctoral training program, both at the University of Michigan, Ann Arbor, MI. Mathematical Policy Research also provided funding to support preparation of the manuscript.
An earlier version of this study was presented during a poster session at Academy Health’s Annual Research Meeting in June 2012. The Institutional Review Board of the University of Michigan evaluated this study and classified it under not regulated status.
The authors declare no conflict of interest.
Reprints: Eric J. Lammers, PhD, Mathematical Policy Research, 220 East Huron Street, Suite 300, Ann Arbor, MI 48104. E-mail: email@example.com.