| 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. |
| Rowena Jacobs is a Research Fellow at the Centre for Health Economics, University of York.
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| 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 |
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