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Electronic Medical Record-Assisted Design of a Cluster-Randomized Trial to Improve Diabetes Care and Outcomes

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Abstract

Background

Electronic medical records (EMRs) have the potential to facilitate the design of large cluster-randomized trials (CRTs).

Objective

To describe the design of a CRT of clinical decision support to improve diabetes care and outcomes.

Methods

In the Diabetes Improvement Group-Intervention Trial (DIG-IT), we identified and balanced preassignment characteristics of 12,675 diabetic patients cared for by 147 physicians in 24 practices of 2 systems using the same vendor’s EMR. EMR-facilitated disease management was system A’s experimental intervention; system B interventions involved patient empowerment, with or without disease management. For our sample, we: (1) identified characteristics associated with response to interventions or outcomes; (2) summarized feasible partitions of 10 system A practices (2 groups) and 14 system B practices (3 groups) using intra-cluster correlation coefficients (ICCs) and standardized differences; (3) selected (blinded) partitions to effectively balance the characteristics; and (4) randomly assigned groups of practices to interventions.

Results

In System A, 4,306 patients, were assigned to 2 groups of practices; 8,369 patients in system B were assigned to 3 groups of practices. Nearly all baseline outcome variables and covariates were well-balanced, including several not included in the initial design. DIG-IT’s balance was superior to alternative partitions based on volume, geography or demographics alone.

Conclusions

EMRs facilitated rigorous CRT design by identifying large numbers of patients with diabetes and enabling fair comparisons through preassignment balancing of practice sites. Our methods can be replicated in other settings and for other conditions, enhancing the power of other translational investigations.

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Acknowledgments

An earlier version of this work was presented at the Society for Medical Decision Making Annual Meeting in Boston, October 2006. This work is supported in part by grant no. R01 HS15123 from the Agency for Healthcare Research and Quality. The authors wish to thank Mary Jo Roach, Ph.D., and Mark Votruba, Ph.D. for their substantial contributions to DIG-IT.

Conflicts of Interest

Dr. Love has received an honorarium for a short course given to Amgen, Inc. Dr. Einstadter has served as a consultant to Medical Mutual of Ohio. Dr. Dawson has served as a consultant to Gerson Lehrman Group. None of these activities are related to the research described in this report.

Author information

Authors and Affiliations

  1. Center for Health Care Research and Policy, Case Western Reserve University-MetroHealth Medical Center, Cleveland, OH, USA

    Thomas E. Love PhD, Randall D. Cebul MD, Douglas Einstadter MD, MPH, Scott S. Husak BS & Neal V. Dawson MD

  2. Department of Medicine, School of Medicine, Case Western Reserve University-MetroHealth Medical Center, Cleveland, OH, USA

    Thomas E. Love PhD, Randall D. Cebul MD, Douglas Einstadter MD, MPH, Peter J. Greco MD & Neal V. Dawson MD

  3. Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Randall D. Cebul MD, Douglas Einstadter MD, MPH & Neal V. Dawson MD

  4. Information Technology Division and Department of General Internal Medicine, Medicine Institute, Cleveland Clinic Foundation, Cleveland, OH, USA

    Anil K. Jain MD

  5. University Hospitals, Cleveland, OH, USA

    Holly Miller MD, MBA

  6. Cleveland Clinic Foundation, Cleveland, OH, USA

    C. Martin Harris MD, MBA

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  1. Thomas E. Love PhD
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Correspondence to Thomas E. Love PhD.

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Love, T.E., Cebul, R.D., Einstadter, D. et al. Electronic Medical Record-Assisted Design of a Cluster-Randomized Trial to Improve Diabetes Care and Outcomes. J GEN INTERN MED 23, 383–391 (2008). https://doi.org/10.1007/s11606-007-0454-3

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