Love Thomas E, Cebul Randall D, Einstadter Douglas, Jain Anil K, Miller Holly, Harris C Martin, Greco Peter J, Husak Scott S, Dawson Neal V
Center for Health Care Research and Policy, Case Western Reserve University-MetroHealth Medical Center, Cleveland, OH 44109-1998, USA.
J Gen Intern Med. 2008 Apr;23(4):383-91. doi: 10.1007/s11606-007-0454-3.
Electronic medical records (EMRs) have the potential to facilitate the design of large cluster-randomized trials (CRTs).
To describe the design of a CRT of clinical decision support to improve diabetes care and outcomes.
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.
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.
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.
电子病历（EMR）有潜力促进大型整群随机试验（CRT）的设计。
描述一项用于改善糖尿病护理及结局的临床决策支持整群随机试验的设计。
在糖尿病改善组干预试验（DIG-IT）中，我们利用同一供应商的电子病历，识别并平衡了2个系统中24家医疗机构的147名医生所护理的12675名糖尿病患者的预分配特征。电子病历辅助的疾病管理是系统A的实验性干预措施；系统B的干预措施包括患者赋权，有或没有疾病管理。对于我们的样本，我们：（1）识别与干预反应或结局相关的特征；（2）使用组内相关系数（ICC）和标准化差异总结10家系统A医疗机构（2组）和14家系统B医疗机构（3组）的可行分组；（3）选择（盲法）分组以有效平衡这些特征；（4）将医疗机构组随机分配至干预措施。
在系统A中，4306名患者被分配至2组医疗机构；系统B中的8369名患者被分配至3组医疗机构。几乎所有基线结局变量和协变量都得到了很好的平衡，包括一些最初设计中未纳入的变量。DIG-IT的平衡优于仅基于数量、地理位置或人口统计学的其他分组。
电子病历通过识别大量糖尿病患者并通过医疗机构预分配平衡实现公平比较，促进了严谨的整群随机试验设计。我们的方法可在其他环境和针对其他病症中复制，增强其他转化研究的效力。
