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Assessing productivity in Australian health services delivery: Some experimental estimates Owen Gabbitas and Christopher Jeffs Productivity Commission.

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Presentation on theme: "Assessing productivity in Australian health services delivery: Some experimental estimates Owen Gabbitas and Christopher Jeffs Productivity Commission."— Presentation transcript:

1 Assessing productivity in Australian health services delivery: Some experimental estimates Owen Gabbitas and Christopher Jeffs Productivity Commission 17 December 2007 PRELIMINARY WORKING PAPER: NOT FOR QUOTATION WITHOUT PRIOR CLEARANCE FROM THE CORRESPONDING AUTHOR, OWEN GABBITAS

2 2 Outline of presentation n Setting the scene n Conceptual framework for the delivery of health services n What is productivity? n Quality is an important aspect of healthcare n State variation in average public hospital costs n Stochastic frontier analysis of state & territory public hospital systems n Summary

3 3 Setting the scene n The Commission gave an undertaking in Australia’s Health Workforce to pursue further work in the area of productivity measurement in health services delivery n Our paper explores the availability and suitability of Australian health data for use in productivity analysis n It looks at productivity at 3 levels in the health system  health and community services (the health system in aggregate)  public hospitals (the health service provider level)  diagnostic categories related to hip replacement surgery (the procedural level) n Focus today on public hospitals

4 4 Conceptual framework

5 5 What is productivity? n Units of output per unit of input  Concerned with physical units  Does not take into account input or output prices  Expressed in levels or, more commonly, growth rates n Related to technical efficiency  Extent to which inputs can be reduced while producing the same output (input-augmenting)  Extent to which output can be increased from existing inputs (output-augmenting) n Productivity focus is on measurement n Policy focus is on efficiency and effectiveness

6 6 Quality is important n Quality is multi-dimensional  Quantity and quality of life (mortality & morbidity) n Quality may vary over time (inter-temporal nature) (eg survival rates) n Indicators may also reflect other factors (attribution) (eg lifestyle) n Choice of counterfactual?  Before and after treatment  What would otherwise have occurred n Choice of appropriate quality measures to use?  Composite measure based on indicators How to weight different metrics & time periods?  Overarching measures (eg life expectancy)? n Can be incorporated into productivity analysis in various ways  Through use of quality-adjusted output  As a separate output in its own right  Using the resulting health outcomes instead of outputs n Seldom done in practice due to the absence of suitable summary measures

7 7 Considerable variation between treatments and jurisdictions

8 8 Stochastic frontier analysis of state & territory public hospital systems n Unlike DEA, SFA allow for measurement error, not just inefficiency n The model estimated contains  1 Output (casemix-adjusted separations per jurisdiction)  3 Inputs (labour (FTE), real capital services, real medical supplies) n Estimated in Stata using maximum likelihood n Data from Australian Institute of Health & Welfare; Report on Government Service Provision; Australian Bureau of Statistics n All variables expressed per 1000 residents – no adjustment for demographics n Covers the period: to n Alternative models  Quality adjusted output (Casemix-adjusted separations adjusted by an index of life- expectancy at birth by state)  Time invariant, Time variant

9 9 Public hospitals: implied productivity gap by state

10 10 Public hospitals: implied productivity gap by state

11 11 Summary n Experimental results suggest that there could be scope for productivity improvement in Australian public hospital systems  (Analysis suggest that this could be in the order of 10%)  Wide variation across jurisdictions n However, caution needed  Based on (sometimes dated) historical information  Quality of data is less than ideal  Do not isolate the effects of policy choices (eg achievement of equity goals) from efficiency and other influences  Examination of the industry in situ, not ‘forward looking’ — do not fully take account of the potential for change  Unable to control for all relevant institutional and operating factors

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