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First published February 28, 2007 as JAMIA PrePrint; doi:10.1197/jamia.M2327
Journal of the American Medical Informatics Association 2007;14(3):329-339
© 2007 American Medical Informatics Association

A more recent version of this article appeared on May 1, 2007
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Submitted on November 13, 2006
Accepted on February 8, 2007

Crossing the Evidence Chasm: Building evidence bridges from process changes to clinical outcomes

David C. Kendrick MD, MPH1, Davis Bu MD, MA1, Eric Pan MD, MSc1, and Blackford Middleton MD, MPH, MSc2*

Affiliation of the authors: 1 Center for Information Technology Leadership (CITL), Wellesley, MA; Department of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA ; 2 Center for Information Technology Leadership (CITL), Wellesley, MA; Clinical Informatics Research and Development, Boston, MA; Partners HealthCare System Department of General Internal Medicine, Brigham and Women’s Hospital, Boston, MA; Harvard Medical School, Boston, MA

* To whom correspondence should be addressed.

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

Design We 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 healthcare simulations.

Measurements We apply this method to create an evidence bridge between retinopathy screening rates and incidence of blindness in diabetics.

Results The resulting model uses changes in eye exam 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 HIT on clinical outcomes through changes in clinical process measures.






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