Electronic medical record-assisted design of a cluster-randomized trial to improve diabetes care and outcomes.
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.
Love
Thomas E
TE
Center for Health Care Research and Policy, Case Western Reserve University-MetroHealth Medical Center, Cleveland, OH 44109-1998, USA. Thomas.Love@case.edu
Cebul
Randall D
RD
Einstadter
Douglas
D
Jain
Anil K
AK
Miller
Holly
H
Harris
C Martin
CM
Greco
Peter J
PJ
Husak
Scott S
SS
Dawson
Neal V
NV
D000368:Aged; D000555:Ambulatory Care Information Systems; D016000:Cluster Analysis; D048909:Diabetes Complications / Q000517:prevention & control*; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy*; D005260:Female; D006123:Group Practice; D006801:Humans; D008297:Male; D050316:Medical Order Entry Systems; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D009820:Ohio; D010821:Physicians, Family; D010818:Practice Patterns, Physicians'; D011320:Primary Health Care; D011785:Quality Assurance, Health Care; D012107:Research Design; D016896:Treatment Outcome