Using population-based automated pharmacy data, patterns of use of selected prescription medications during a 1 year time period identified by a consensus judgement process were used to construct a measure of chronic disease status (Chronic Disease Score). This score was evaluated in terms of its stability over time and its association with other health status measures. In a pilot test sample of high utilizers of ambulatory health care well known to their physicians (n = 219), Chronic Disease Score (CDS) was correlated with physician ratings of physical disease severity (r = 0.57). In a second random sample of patients (n = 722), its correlation with physician-rated disease severity was 0.46. In a total population analysis (n = 122,911), it was found to predict hospitalization and mortality in the following year after controlling for age, gender and health care visits. In a population sample (n = 790), CDS showed high year to year stability (r = 0.74). Based on health survey data, CDS showed a moderate association with self rated health status and self reported disability. Unlike self-rated health status and health care utilization, CDS was not associated with depression or anxiety. We conclude that scoring automated pharmacy data can provide a stable measure of chronic disease status that, after controlling for health care utilization, is associated with physician-rated disease severity, patient-rated health status, and predicts subsequent mortality and hospitalization rates. Specific methods of scoring automated pharmacy data to measure global chronic disease status may require adaptation to local prescribing practices. Scoring might be improved by empirical estimation of weighting factors to optimize prediction of mortality and other health status measures.