DATA ENVELOPMENT ANALYS – EFFICIENCY ANALYSIS ON 17 MIDDLE-SIZED HOSPITALS IN SWEDEN

Abstract: Rising costs of healthcare in most OECD-countries have contributed to a quest for research into the field of healthcare costs and efficiency, something that has not been free of controversies. As a result of that, healthcare financers as well as providers have become more inclined to measure performance and compare themselves to others with the same responsibilities. In Sweden, the Association of Local Authorities and Regions, SKL, has since 2006 measured and compared the costs and production of entities in the healthcare sector in what is called “Öppna Jämförelser”, Open comparisons (SKL.se/oppnajamforelser). However, most of the comparisons have been between county councils. In this thesis, the entities compared are hospitals. The technique used is Data Envelopment Analysis (DEA), in its different revised forms. The entities, i.e. hospitals are seen as Decision-making units (DMUs) that are compared in order to find out which hospitals that are efficient in relation to other hospitals and which ones that have potential to increase their efficiency levels. One of the aims of the thesis is to find whether the technique is robust and reliable. A revised version of DEA, namely Multiple-criteria DEA (MCDEA), is also used and compared with the classical one. As hospitals in Sweden all are financed by county councils, their sizes and patient bases differ depending on how many people live in the region or sub-regional area and how many hospitals are active there. Some county councils that had not reported complete data on hospital level to Swedish Association of Local Authorities and Regions (SKL) have no hospitals represented in this study. Of the 17 hospitals included, results show that the smallest ones face increasing returns to scale while the bigger ones face either constant or decreasing returns to scale. Spearman’s rank correlation tests show correlations between the efficiency ranks of the hospitals and their ranking orders in some other usually used indicators used in healthcare, such as length of stay, patient satisfaction rate, overcrowding and mean DRG-point. Compared to the classical DEA, MCDEA performs much better and easing the efficiency score limit of 1.00 shows that the efficient hospitals get different scores higher than 1.00 and are thus discernible and rankable. It is concluded that DEA is a reliable and robust technique and the revised version, MCDEA, is better than the classical one.