1. The Impact of Health IT Adoption: Are We Measuring the Right Outcomes?
Tiago K. Colicchio1, MBA, MS, Guilherme Del Fiol1, MD, PhD, Watson A. Bowes III1,2 MD, MS, Julio C. Facelli1, PhD, Debra L. Scammon3, PhD, Scott P. Narus1,2, PhD
1Biomedical Informatics, University of Utah, Salt Lake City, UT; 2Medical Informatics, Intermountain Healthcare, Salt Lake City, UT ; 3David Eccles School of Business, University of Utah, Salt Lake City, UT
Introduction
Although Electronic Health Record (EHR) systems adoption has increased in the U.S., our understanding of how they impact care outcomes is still limited.
A contributing factor to this gap is the use of a narrow set of study-specific measurements.
We investigated if measures reported in prior studies provide a comprehensive coverage of processes likely impacted by health IT interventions.
Methods
We interviewed Intermountain Healthcare leaders to collect suggestions of relevant measures for assessing health IT interventions.
We combined Intermountain’s suggestions with the most commonly reported measures from the literature [1].
The combined list was included in two online surveys sent to informatics experts who suggested additional relevant measures (Figure 1).
Conclusion
Intermountain leaders and national informaticists suggested several measures that have not been reported in the literature.
Most suggestions assess productivity and safety care processes, which deserve more attention in future evaluations.
We used measures from our inventory in a pilot evaluation of a large commercial EHR implementation and propose a replicable methodology for real-time monitoring of health IT adoption [3].
References
Colicchio TK, Facelli JC, Del Fiol G, Scammon DL, Bowes WA, Narus SP. Health information technology adoption: Understanding research protocols and outcome measurements for IT interventions in health care. Journal of Biomedical Informatics Oct;63:33–44.
Colicchio TK, Del Fiol G, Scammon DL, Bowes WA, Facelli JC, Narus SP. Development and classification of a robust inventory of near real-time outcome measurements for assessing information technology interventions in health care. Journal of Biomedical Informatics Sep;73;62–75.
Colicchio TK, Del Fiol G, Stoddard JG, Narus SP. Evaluation of a systematic methodology to detect in near real-time performance changes during electronic health record system implementations: a longitudinal study. AMIA Annu Symp Proc (Accepted).
Results
Intermountain leaders suggested 54 measures that were combined with 37 measures from the literature.
Eleven additional measures were suggested by 112 informatics experts, producing a final inventory of 102 measures classified into 16 types of measurements (Figure 2) [2].
Acknowledgements
This study was supported by Intermountain Healthcare. JCF has been partially funded by NIH Award Number 1ULTR
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