© 1995 Faculty of Public Health Medicine of the Royal Colleges of Physicians of the United Kingdom
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Differences in hospital casemix, and the relationship between casemix and hospital costs
Neil Söderlund, Research Fellow
Ruairidh Milne, Consultant in Public Health Medicine
Alastair Gray, Research Associate
James Raftery, Health Economist
Department of Public Health and Primary Care, University of Oxford Gibson Building, Radcliffe Infirmary, Oxford OX2 6HE
Department of Public Health, Oxford Regional Health Authority
Centre for Socio-Legal Studies, University of Oxford
National Casemix Office, NHS Management Executive Winchester
Address correspondence to Dr N. Söderlund
BACKGROUND: The aim of the study was to examine the relationship between hospital costs and casemix, and after adjustment for casemix differences, between cost and institutional size, number of specialties, occupancy and teaching status.
METHODS: A retrospective analysis of all admissions to nine acute-care NHS hospitals in the Oxford region during the 1991–1992 financial year was undertaken. All episodes were assigned to a diagnosis-related group (DRG) and a cost weight assigned accordingly. Costs per finished consultant episode, before and after adjustment for casemix differences, were analysed at the hospital and specialty level.
RESULTS: Casemix differences were significant, and accounted for approximately 77 per cent of the difference in costs between providers. Costs per casemix-adjusted episode were not significantly associated with differences in hospital size, scope, occupancy levels or teaching status, but sample size was insufficient to investigate these relationships adequately. Specialty costs were poorly correlated with specialty casemix. This was probably due to poor apportionment of specialty costs in hospital accounting returns.
CONCLUSIONS: Casemix differences need to be taken into account when comparing providers for the purposes of contracting, as unadjusted unit costs may be misleading. Although the methods used may currently be applied to most NHS hospitals, widespread use would be greatly facilitated by the development of indigenous cost weights and better routine hospital data coding and collection.
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