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First published February 28, 2007 as JAMIA PrePrint; doi:10.1197/jamia.M2327
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J Am Med Inform Assoc. 2007;14:329-339. DOI 10.1197/jamia.M2327.
© 2007 American Medical Informatics Association


Research paper

Crossing the Evidence Chasm: Building Evidence Bridges from Process Changes to Clinical Outcomes

David C. Kendrick, MD, MPHa,c,d, Davis Bu, MD, MAa,c,d, Eric Pan, MD, MSca,c,d and Blackford Middleton, MD, MPH, MSca,b,c,d,*

a Center for Information Technology Leadership, Boston, MA
b Clinical Informatics Research & Development, Boston, MA
c Partners HealthCare System, Department of General Internal Medicine, Boston, MA
d Brigham and Women’s Hospital, Harvard Medical School, Boston, MA.

* Correspondence and reprints: Blackford Middleton, MD, MPH, MSc, Center for Information Technology Leadership, Partners HealthCare System, 93 Worcester Street, Second Floor, Wellesley, MA 02481 (Email: bmiddleton{at}citl.org).

Received for publication: 11/13/06; accepted for publication: 02/08/07.

Objective: Although demand for information about the effectiveness and efficiency of health care information technology grows, large-scale resource-intensive randomized controlled trials of health care information technology remain impractical. New methods are needed to translate more commonly available clinical process measures into potential impact on clinical outcomes.

Design: The authors propose a method for building mathematical models based on published evidence that provides an evidence bridge between process changes and resulting clinical outcomes. This method combines tools from systematic review, influence diagramming, and health care simulations.

Measurements: The authors apply this method to create an evidence bridge between retinopathy screening rates and incidence of blindness in diabetic patients.

Results: The resulting model uses changes in eye examination rates and other evidence-based population parameters to generate clinical outcomes and costs in a Markov model.

Conclusion: This method may serve as an alternative to more expensive study designs and provide useful estimates of the impact of health care information technology on clinical outcomes through changes in clinical process measures.







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Copyright © 2007 by the American Medical Informatics Association.