The Analysis of Efficiency Among a Small Number of Organisations: How Inferences Can Be Improved By Exploiting Patient-Level Data
Posted: 26 Jun 2007
Date Written: June 18, 2007
Those responsible for monitoring and managing the performance of health care organisations face the common problem that the relationship between observed performance and effort is difficult to establish. A solution is to compare the performance of multiple organisations, but this requires a sufficient number of comparators. Faced with a small sample, it may be possible to exploit other information sources. Multilevel regression models are applied to analyse the performance of six Danish vascular departments in 2004 using a patient-level dataset. We compare inferences drawn from using summary patient data with those from applying a multilevel model, and explore the sensitivity of these inferences to various specifications. Treatment costs are higher for smokers, older patients, patients with cerebrovascular and pulmonal diseases and for those subject to acute hospitalization and with longer lengths of stay. Costs are lower for patients who are having follow-up surgery and for patients who receive some form of home care, suggesting that there may be some substitution of care input between vascular departments and other care providers.
We estimate the relative efficiency of each department. The construction of confidence intervals allows the seven departments to be sorted into three groups, containing the least and most efficient departments and those that fall between these extremes. Conclusions about relative efficiency are robust to model specification, choice of estimator and hold at the 95% confidence level.
Keywords: efficiency, stochastic frontier analysis, multilevel models, panel data
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