Background: Data envelopment analysis (DEA) and the Malmquist index are frequently used in the hospital sector to measure efficiency. However, very few works are published for Italian hospitals, despite the fact that efficiency was the main driver guiding healthcare reform in the 1990s. Objectives: The objective of this study is derive technical efficiency and change in productivity of the Local Health Trust (LHT) in directly managed Italian hospitals. We will also explore whether the complexity of treated hospitals cases influences technical efficiency. Methods/approaches: The DEA technique and DEA-Malmquist index are used to derive technical efficiency, and changes in productivity and efficiency, for directly managed hospitals in Italy's public healthcare system. To control for the influence of the complexity of the treated cases on the technical efficiency, two DEA input models are examined. One of these models, weighs outputs with a case mix index (CMI) as a measure of the complexity of hospital treatment. Results: The results show that efficiency in the model not adjusted is on average 79,52% compared to 81,55 % efficiency in Model B (output adjusted with CMI), in efficiency level. In mean complexity of treatment, as measured with CMI, influence technical efficiency, as indicate in Table 5 and 6. Statistics tests reveal differences in the efficiency score distribution for Model A and Model B (adjusted). The influence of complexity of treatment on technical efficiency analysis, has hospital individual relevance. The Malmquist index reveals productivity improvement for 7 out of the 8 periods measured. Technical efficiency change is positive (improvement) between 2000 and 2005, and fall in 2006 and 2007. Technological change is positive in 1999-2000, 2000-2001, 2002-2003, 2005-2006, 2006-2007. Scale efficiency improves in 2000-2001, 2001-2002,2003-2004, 2004-2005. Practical implications: Between 1999 and 2007, for the sample, improved productivity was examined and attributed to an input reduction of the same output. This could mean that the reforms that took place in the 1990s were successful and that this direction is worth further pursuit. In light of these findings, one must make policy recommendations with caution, despite the fact, the complexity of treatments influence technical efficiency, hence the proportional reduction of the inputs vector.
Complexity of Treatment, and Changes in Efficiency and Productivity for Directly Managed Italian Hospitals
PINTO, CLAUDIO
2013-01-01
Abstract
Background: Data envelopment analysis (DEA) and the Malmquist index are frequently used in the hospital sector to measure efficiency. However, very few works are published for Italian hospitals, despite the fact that efficiency was the main driver guiding healthcare reform in the 1990s. Objectives: The objective of this study is derive technical efficiency and change in productivity of the Local Health Trust (LHT) in directly managed Italian hospitals. We will also explore whether the complexity of treated hospitals cases influences technical efficiency. Methods/approaches: The DEA technique and DEA-Malmquist index are used to derive technical efficiency, and changes in productivity and efficiency, for directly managed hospitals in Italy's public healthcare system. To control for the influence of the complexity of the treated cases on the technical efficiency, two DEA input models are examined. One of these models, weighs outputs with a case mix index (CMI) as a measure of the complexity of hospital treatment. Results: The results show that efficiency in the model not adjusted is on average 79,52% compared to 81,55 % efficiency in Model B (output adjusted with CMI), in efficiency level. In mean complexity of treatment, as measured with CMI, influence technical efficiency, as indicate in Table 5 and 6. Statistics tests reveal differences in the efficiency score distribution for Model A and Model B (adjusted). The influence of complexity of treatment on technical efficiency analysis, has hospital individual relevance. The Malmquist index reveals productivity improvement for 7 out of the 8 periods measured. Technical efficiency change is positive (improvement) between 2000 and 2005, and fall in 2006 and 2007. Technological change is positive in 1999-2000, 2000-2001, 2002-2003, 2005-2006, 2006-2007. Scale efficiency improves in 2000-2001, 2001-2002,2003-2004, 2004-2005. Practical implications: Between 1999 and 2007, for the sample, improved productivity was examined and attributed to an input reduction of the same output. This could mean that the reforms that took place in the 1990s were successful and that this direction is worth further pursuit. In light of these findings, one must make policy recommendations with caution, despite the fact, the complexity of treatments influence technical efficiency, hence the proportional reduction of the inputs vector.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
