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The objective of this study was to compare costs data by diagnosis related group (DRG) between Belgium and Switzerland. Our hypotheses were that differences between countries can probably be explained by methodological differences in cost calculations, by differences in medical practices and by differences in cost structures within the two countries.Classifications of DRG used in the two countries differ (AP-DRGs version 1.7 in Switzerland and APR-DRGs version 15.0 in Belgium). The first step of this study was to transform Belgian summaries into Swiss AP-DRGs. Belgian and Swiss data were calculated with a clinical costing methodology (full costing). Belgian and Swiss costs were converted into US$ PPP (purchasing power parity) in order to neutralize differences in purchasing power between countries.The results of this study showed higher costs in Switzerland despite standardization of cost data according to PPP. The difference is not explained by the case-mix index because this was similar for inliers between the two countries. The length of stay (LOS) was also quite similar for inliers between the two countries. The case-mix index was, however, higher for high outliers in Belgium, as reflected in a higher LOS for these patients. Higher costs in Switzerland are thus probably explained mainly by the higher number of agency staff by service in this country or because of differences in medical practices.It is possible to make international comparisons but only if there is standardization of the case-mix between countries and only if comparable accountancy methodologies are used. Harmonization of DRGs groups, nomenclature and accountancy is thus required.

Objectives of this article are to evaluate the possibility to create a CW scale by pathology on the basis of cost data from Belgian hospitals, to compare several methodologies to create this CW scale, and to evaluate the financial impact of a modification of the financing system on hospitals’ income. CW scales were elaborated according to various methodologies in order to isolate the scale allowing the most adequate financing system, i.e. approaching the real costs as much as possible. Twelve scales were created. They vary according to the type of data used, according to DRGs and severities of illness included within the scale, and according to the variable used in order to isolate outliers. For a similar case-mix, Hospitals H2 and H5 would see their financing increased through a prospective system based on the selected CW scale (No. 6). This modification would generate a reduction in financing going from −1 to −9% according to hospitals. The cost database created made it possible to create a CW scale according to a technique which could constitute the first step of a PPS if advantages of a such financing system were established. In the Belgian context, it would be probably judicious to envisage regional databases allowing diversified methodological approaches whose results would be confronted, discussed, and coordinated at the federal level.