Time for a new budget allocation model for hospital care in Stockholm?

• Published in: Health care management science 2011-03, Vol.14 (1), p.36-55
• Main Authors: Andersson, Per-Åke, Bruce, Daniel, Walander, Anders, Viberg, Inga
• Format: Article
• Language: English
• Subjects: Adult; Age Factors; Aged; Budgeting; Budgets; Budgets - statistics & numerical data; Business and Management; Computer Simulation; Consumption; Costly diagnosis groups; Econometrics; Female; Health Administration; Health care expenditures; Health care industry; Health Care Rationing - economics; Health Informatics; Hospice care; Hospital Costs - statistics & numerical data; Hospitalization; Hospitals; Humans; International; Male; Management; Matrix model; Medicin och hälsovetenskap; Middle Aged; Models, Economic; Need-based allocation; Operations research; Operations Research/Decision Theory; Per capita; Population; Sex Factors; Socioeconomic Factors; Specialist care; State Medicine - economics; Stockholm; Studies; Sweden; Variables
• ISSN: 1386-9620; 1572-9389
• DOI: 10.1007/s10729-010-9140-9

In Stockholm County Council (SLL), budgets for hospital care have been allocated to geographically responsible authorities for a long time. This hospital care includes all publicly financed specialist care, also privately owned hospitals, except private practitioner care. The old needs-index model, a 6D capitation matrix based on demography and socio-economy, was generated on linked individual data for 1994–96. In this paper the power of the old allocation model is evaluated by the use of new data for 2006. The analysis shows that most of the socioeconomic variables have lost their descriptive power in 10 years. Using a methodical search we also find an improved need-based allocation model for hospital care using the new data for 2006. By focusing on costly diagnoses, where the descriptive power has increased between 1996 and 2006, and by using some new socioeconomic variables, and by relying on birth and death prognoses, we are able to generate a matrix model with much higher coefficients-of-determinations in 1 year predictions. In addition, a more careful modelling of multi-morbidity, part-of-the-year inhabitants, episode definition and cost transformation is developed. The area-level cost residuals of registered versus predicted costs show stable signs over the years, indicating unexplained systematics. For the reduction of the residuals, accepting proven inpatient diagnoses but not the full costs, a mixed capitation/fee-for-service strategy is discussed. Once equivalent (e.g. full-year) observations are determined, the link between background and consumption is not on individual-level but on cell-level, as in current resource allocation studies in the United Kingdom.