We propose reimbursement schemes based on patient classification systems (PCSs) that include adjustments for length of stay (LOS) and exceptional costs and are designed to minimize undesirable effects of economic incentives. In addition, a statistical approach to compare the schemes and the underlying PCSs is proposed, where costs and LOSs for two successive years are used. The first year data provides estimates of the class cost means and the next year's reimbursements which are compared with the second year's costs. This method focuses on the predictive power of a PCS and differs from the usual retrospective analyses based on the proportion of explained variance for single year data. The approach is applied to discharge data of Swiss hospitals where stays are grouped according to five PCSs: All Patient Diagnosis-Related Groups (AP-DRGs), All Patient Refined Diagnosis-Related Groups (APR-DRGs), International Refined Diagnosis-Related Groups (IR-DRGs), Australian Refined Diagnosis-Related Groups (AR-DRGs), and SQLape. When adjusting for LOS and outliers, these systems do not differ substantially in their ability to predict cost of stay. Therefore, increasing the number of classes does not necessarily improve cost predictions. However, the payment of a fixed amount per diem (not exceeding the marginal cost) and correcting the reimbursements for exceptional costs substantially reduces the average discrepancy between costs and reimbursements.