Measuring Efficiency in Health Care健康卫生中的效率评估：分析技术与保健方针
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作者： Rowena Jacobs著
出 版 社：
出版时间： 2006-6-1字数：版次： 1页数： 243印刷时间： 2006/06/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9780521851442包装： 精装编辑推荐
The pursuit of efficiency has become a central objective of policymakers within most health systems. This book examines the strengths and limitations of two analytic techniques - data envelopment analysis and stochastic frontier analysis - widely advocated as means of measuring the comparative efficiency of health care organizations.
内容简介
With the healthcare sector accounting for a sizeable proportion of national expenditures, the pursuit of efficiency has become a central objective of policymakers within most health systems. However, the analysis and measurement of efficiency is a complex undertaking, not least due to the multiple objectives of health care organizations and the many gaps in information systems. In response to this complexity, research in organizational efficiency analysis has flourished. This book examines some of the most important techniques currently available to measure the efficiency of systems and organizations, including data envelopment analysis and stochastic frontier analysis, and also presents some promising new methodological approaches. Such techniques offer the prospect of many new and fruitful insights into health care performance. Nevertheless, they also pose many practical and methodological challenges. This is a timely critical assessment of the strengths and limitations of efficiency analysis applied to health and health care.
作者简介
Rowena Jacobs is a Research Fellow at the Centre for Health Economics, University of York.
目录
List of figures
List of tables
Preface
Acknowledgements
List of abbreviations
1 Efficiency in health care
1.1 Introduction
1.2 The demand for efficiency analysis in health care
1.3 Organisational efficiency
1.4 Analytic efficiency measurement techniques
1.5 Experience with efficiency analysis in health care
1.6 This book
2 The components of an efficiency model
2.1 Introduction
2.2 Unit of analysis
2.3 What are outputs in health care?
2.3.1 Health outcomes
2.3.2 Health care activities
2.4 Valuing health care outputs
2.5 Specifying inputs
2.5.1 Labour inputs
2.5.2 Capital inputs
2.5.3 Summary
2.6 Environmental constraints
2.7 Practical challenges
2.8 Conclusions
3 Stochastic frontier analysis of cross-sectional data
3.1 Introduction
3.2 Considerations in stochastic frontier analysis
3.2.1 Whether to estimate a production or a cost function
3.2.2 Whether to transform variables
3.2.3 Whether to estimate a total or an average function
3.2.4 Which explanatory variables to include
3.2.5 How to model the residual
3.2.6 How to extract the efficiency estimates
3.3 Application to acute hospitals in England
3.4 Conclusions
4 Stochastic frontier analysis of panel data
4.1 Introduction
4.2 Time-invariant efficiency
4.2.1 Empirical application
4.3 Time-varying efficiency
4.3.1 Empirical application
4.4 Unobserved heterogeneity
4.4.1 Empirical application
4.5 Summary and sensitivity analysis
4.6 Conclusions
5 Data envelopment analysis
5.1 Introduction
5.2 The DEA methodology
5.2.1 Input-oriented efficiency
5.2.2 Output-oriented efficiency
5.2.3 DEA formulation
5.3 Considerations in data envelopment analysis
5.3.1 Whether to assume constant or variable returns to scale
5.3.2 Whether to assume an input or an output orientation
5.3.3 Whether to apply weight restrictions
5.3.4 Dealing with ‘slacks’
5.3.5 Model specification and judging the quality of a DEA model
5.3.6 How to adjust for environmental factors
5.4 Application to acute hospitals in England
5.4.1 The methods and data
5.4.2 Model specifications
5.4.3 Results
5.5 Conclusions
6 The Malmquist index
6.1 Introduction
6.2 The Malmquist methodology
6.2.1 A graphical illustration
6.2.2 The general form of the Malmquist index
6.3 Considerations in using the Malmquist index
6.4 Previous literature on the Malmquist index in health care
6.5 Application to acute hospitals in England
6.5.1 The methods and data
6.5.2 Model specifications
6.5.3 Results
6.6 Conclusions
7 A comparison of SFA and DEA
7.1 Introduction
7.2 Why SFA and DEA produce different efficiency estimates
7.3 Other differences between SFA and DEA
7.4 Comparison of different methodologies
7.4.1 The methods and data
7.4.2 Model specifications
7.4.3 Results
7.5 Conclusions
8 Unresolved issues and challenges in efficiency measurement
8.1 Introduction
8.2 Output weights
8.3 Modelling the production process
8.4 Environmental constraints
8.5 Dynamic effects
8.6 Conclusions
9 Some alternative approaches to measuring performance
9.1 Introduction
9.2 Multilevel modelling
9.3 Generalised statistical modelling
9.3.1 Illustrative example
9.4 Seemingly unrelated regression (SUR) in a multilevel context
9.4.1 Illustrative example
9.5 Conclusions
10 Conclusions
10.1 Introduction
10.2 Output weights
10.3 Partitioning unexplained variation
10.4 Unresolved technical issues
10.5 For policy makers and regulators
Appendix: Data description
References
Author index
Subject index
