Assessing the impact of clinically relevant quality improvement activities (QIA) is important to managed care organizations; yet, turnover in enrollment can reduce the data available for analyses, thus decreasing the chance that a difference post-QIA will be detected. The Combined Quality Improvement Ratio (CQuIR) uses matching of patients into pairs pre- and post-QIA to systematically and validly increase the data included in the analysis for evaluation of the QIA. Using a paired cohort study design, 456 pairs of patients with diabetes were identified using the Health Plan Employer Data Information Set (HEDIS) specifications. Patients having retinal examinations were identified pre- and post-QIA. The change in retinal examination rates was analyzed and results compared using repeated pairs (RP), matched pairs (MP), and combined pairs (CP). The CQuIR methodology (which uses CP = RP + MP) resulted in an increase in sample size (n = 456 [CP] versus n = 156 [RP] and n = 300 [MP]) and consequently an increase in power (0.92 [CP] versus 0.38 [RP] and 0.82 [MP]) and a decrease in the confidence interval range (0.97 [CP] versus 2.06 [RP] and 1.14 [MP]). The CQuIR uses a statistically valid approach to increase the data available for the evaluation of QIAs.
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