OBJECTIVES: The purpose of this research is to develop three econometric models [i.e., cost model (model #1); the occurrence of hospitalization or ER event model (model #2); time to hospitalization or ER event model (model #3)] that can be used to identify high-risk patients and to evaluate whether risk models are valid based on claims data from the California Medicaid (MediCal) diabetic patients. METHODS: A retrospective study was conducted by using claims data from January 1995 to December 2000. Dependent variables were total healthcare cost, the occurrence of event, and time to event. Event included hospitalization or ER visits. Historical data including demographic factors, healthcare cost and utilization, type of drugs, increasing dose, adding drugs, and changing drugs, follow-up services based on diabetic guidelines (e.g., office visit, lab tests, and self glucose monitoring), medication compliance, complications, and comorbidity were used as independent variables. The generalized estimating equation and the fixed effect partial likelihood methods were used in a longitudinal data set and a cross-sectional data set with repeatable events, respectively. The split sample validation method was applied to validate the models. RESULTS: The results show that if high-risk patients were identified by high healthcare costs, model #1 was the most appropriate to use since it yielded the highest percentage of correct predictions. Likewise, if high-risk patients were defined as patient who had the occurrence of hospitalization or ER event, model #2 was the most suitable to apply. Similarly, if high-risk patients were indicated by shorter time to hospitalization or ER event, model #3 was the most proper to utilize. Moreover, three models were valid. CONCLUSIONS: The choice of method depends on how high-risk is defined by researchers or policy makers. Identification of high-risk patients with diabetes could mean healthcare providers and health plans could intervene to improve patient management.