OBJECTIVES: In the last fifteen years, models built to appraise health technologies have grown in complexity to accurately reflect the natural history of disease and calculate costs and benefits accordingly. The advantage of using patient level simulation models (PSM) over cohort models (CM) is that CM may become unwieldy with thousands of branches. This usually happens if the patient characteristics influence the risks of complications and these vary over time, or if there are many comorbidities to take into account and the risk of each complication is time dependent. In this case, a typical Markov cohort may be unsuitable. However a PSM can take a long time to run in order to produce results. Aim of this research is to present a case study where a PSM is simplified with a Markov structure and to compare the results of the two models. METHODS: We describe how a PSM for diabetes may be adapted to a CM, stating all the limitations. RESULTS: Provided that the PSM gives similar answers to the CM, the CM may be used as a surrogate for conducting complex sensitivity analysis (e.g. 3 way analysis or tornado diagrams). The advantage is that this model would produce results “instantaneously”. CONCLUSIONS: The adaptation of a PSM model to a cohort model may be a desirable feature if the model is required by a non technical audience. This is the case for “due diligence” models that are increasingly required by investors to assess the value of assets that a pharmaceutical company considers to buy or sell. Further research is needed to have a powerful test to assess whether the difference in results between a simplified CM model and the PSM are statistically significant.