OBJECTIVE: We developed an Internet-based simulation model to determine the long-term health and economic outcomes associated with type 1 and type 2 diabetes and the effects of different treatment policies. The model performs real-time simulations to account for insulin therapy, new devices and delivery systems, oral hypoglycemic medications, screening and treatment strategies for micro- and end-stage complications and multi-factorial interventions. METHODS: The model is based on a series of inter-dependent, linked sub-models that simulate diabetes-related complications (cardiovascular disease, retinopathy, hypoglycemia, ketoacidosis, nephropathy, neuropathy, and non-specific mortality). Each sub-model isa Markov-based model using time, state and diabetes type dependent probabilities derived from published sources. Cohorts can be defined in terms of age, gender, baseline risk factors and pre-existing complications. First- and 2nd-order Monte Carlo simulation using tracker variables confers memory within the model, and accounts for uncertainty in multiple input parameters. Cohort definitions, economic and clinical data in the disease management module can be edited by the user, allowing for the inclusion of recently available data, country-, HMO- or provider-specific perspectives as well as hypothetical analyses. A program is currently collecting Thai epidemiology and cost data for future analyses. Time horizon can be varied from 1-year to patient lifetimes. Clinical and economic outcomes calculated by the model include life expectancy, quality-adjusted life expectancy, incidence, prevalence and time to onset of diabetes-related complications, direct and indirect medical costs and incremental cost-effectiveness ratios. The CORE Diabetes Model has been peer-reviewed, thoroughly validated and documented. It is widely used to generate health economic outcomes for submission to decision-makers globally. CONCLUSIONS: The CORE Diabetes Model allows results obtained from short-term trials to be extrapolated to long-term outcomes. Diabetes management strategies can be compared in different patient populations in a variety of clinical settings, allowing investigations geared towards improving the quality of care for diabetes patients.