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The ability of observational studies to draw conclusions on causal relationships between covariates and outcomes can be improved by incorporating randomly matched controls using the propensity scoring method. This procedure controls for pre-program differences between the enrolled and non-enrolled groups by reducing each participant’s set of covariates into a single score, which makes it feasible to match on what are essentially multiple variables simultaneously. This paper introduces this concept using the first year results of a congestive heart failure (CHF) disease management (DM) program as an example.This study employed a case-control pre-post study design with controls randomly matched to patients based on the propensity score. There were 94 patients with CHF enrolled in a DM program for at least 1 year (cases), who were matched to 94 patients with CHF drawn from a health plan’s CHF population (controls). Independent variables that estimated the propensity score were pre-program: hospital admissions, emergency department (ED) visits, total costs, and risk level. Baseline (1 year prior to program commencement) and 1-year outcome variables were compared for the two groups.The results indicated that, at post-program, program participants had significantly lower hospitalization rates (p = 0.005), ED visit rates (p = 0.048), and total costs (p = 0.003) than their matched controls drawn from the CHF population.Because of its simplicity and utility, propensity scoring should be considered as an alternative procedure for use with current non-experimental designs in evaluating DM program effectiveness.