Disinvestment for re-allocation: A process to identify priorities in healthcare
Introduction
Resource scarcity and increasing demand for services require health systems to cope with difficult choices within constrained budgets. A range of concerns, ranging from ethical principles such as “accountability for reasonableness” through to economic goals of increasing productivity argue for a thoughtful approach that targets reductions as opposed to across-the-board cuts.
The typical health system approach of deriving budgets based on historical spending or political pressures can lead to sub-optimal use of limited resources [1]. Economic approaches can help decision makers by providing a systematic and explicit way to set evidence-based priorities [2], [3] even if they are not the sole consideration [4], [5].
In the process of resource re-allocation, different countries have followed varying approaches for setting priorities at national level [6]. Since 1970s many countries have adopted the Program budgeting and marginal analysis (PBMA) in the health sector [4], [7]. PBMA has been developed as an attempt to rationalize the incremental budgeting approach, based on applications of opportunity cost and marginal analysis [8]. PBMA can be deployed at the micro-level (i.e. specific service areas or treatments) but also at the meso-level (Health Authorities) and the macro-level (Regional Health Systems or National Health Systems) [9]. Other budgeting and re-allocation techniques have used Health Technology Assessment techniques to guide disinvestment decisions in ineffective treatments (e.g. guidance on disinvestment from NICE) [10], [11].
This paper describes a study carried out in the Regional Health System of Tuscany, Italy. Using 2007 performance data, the study measures the impact that performance improvement could have on the amount of resources that Health Authorities (HAs) and the Regional Health System could save and re-allocate to other services. This exercise highlights the role that benchmarking best practices can play in disinvestment decisions. For this study, “disinvestment” in healthcare describes the processes of withdrawing health resources from existing healthcare practices, procedures, technologies or pharmaceuticals that are deemed to deliver little or no health gain relative to their cost, and thus, no efficient allocation or health resources [10]. This means that disinvestment includes service reductions due to inappropriateness and savings achieved through better efficiency identified through benchmarking (e.g. lower cost for the same output).
Performance benchmarking is a common improvement method in hospitals in Anglo-American countries. Despite the debate that has developed in UK National Health Service on how best practices were disseminated and used as models for emulation, benchmarking is still considered a highly desirable policy instrument [12]. Johnston [13] in an OECD report suggests that benchmarking is useful for improving performance, particularly improvements in efficiency and that it may provide a valuable way of reconciling rising demands for healthcare with limits on public financing. In this sense benchmarking could be useful as a guide to disinvestment because it identifies where to free resources by improving performance indicators with an impact on resource utilization.
Section snippets
Background
The use of benchmarking as a managerial tool may be very useful in the Italian health sector where there is the widespread belief that costs cannot be reduced [14]. This is especially true within the Health Authorities (HAs) that have typically not competed with each other or used inter-regional benchmarking to guide improvement efforts. During the evaluation of annual Regional budgets, Health Authorities typically argue that they cannot reduce their spending due to already constrained budgets
Methodology
In line with other European healthcare performance reports [13], the Tuscan PES shows substantial variability of performance across HAs [22]. Based on these differences, the research team created an exploratory simulation based on 2007 performance data to quantify the amount of resources that could be saved.
Construction of the simulation followed four steps: (1) the identification of indicators to be analyzed, (2) the identification of the minimum and maximum improvement that can be required to
Results
Table 4 shows that at the regional level, from 2 to 7% of the 2007 €6.1 million healthcare budget could be re-allocated if all the institutions achieve the regional average or the best practice. These estimates are derived from the sum of all the inefficiencies in the 11 indicators analyzed, considering the two hypotheses.
It is important to emphasize that not all of these savings would be monetized immediately. Some would need to realized over a longer period of time and some would be
Limitations and further developments
There are a number of limits on the results, most notably, limits resulting from the use of means and benchmarks as guides to achievable benchmarks and problems with overlap and attribution of savings to specific interventions. This means that the results presented here may be optimistic estimates of possible savings. In some cases, best performance (Hypothesis 2) is defined on the basis of the best performer instead of a national or regional target. This could lead to some problems such as the
Conclusions
This benchmarking process changed the traditional pattern of complaints around the budgeting and resource allocation processes in Tuscany. The results of this study were shown to all CEOs in a meeting with the Regional leadership. It worked as a warning to all CEOs who argued that their HA had reached the maximum level of efficiency and that they needed more money to achieve required performance improvements. This study has also had a significant impact on the use of the performance evaluation
Acknowledgments
The authors wish to thank the researchers from Management and Health Laboratory for their help in the development of this study and the top managers of Tuscan Health System. The authors also want to thank the colleagues of the European Health Policy Group for the useful discussion and the reviewers of the paper, especially Prof. Adalsteinn Brown for his excellent advice during the review process of the paper.
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