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Assessing productivity in Australian health services delivery: Some experimental estimates

This preliminary working paper explores the availability and suitability of Australian health data for productivity analysis in the delivery of health services, with a focus on public hospitals. It discusses the conceptual framework for productivity, the importance of quality in healthcare, state variation in average public hospital costs, and presents stochastic frontier analysis of state and territory public hospital systems. The paper concludes that there is potential for productivity improvement in Australian public hospital systems, but caution is needed due to data limitations and the inability to fully control for all relevant factors.

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Assessing productivity in Australian health services delivery: Some experimental estimates

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  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 (ogabbitas@pc.gov.au)

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

  3. Setting the scene • The Commission gave an undertaking in Australia’s Health Workforce to pursue further work in the area of productivity measurement in health services delivery • Our paper explores the availability and suitability of Australian health data for use in productivity analysis • 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) • Focus today on public hospitals

  4. Conceptual framework

  5. What is productivity? • 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 • 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) • Productivity focus is on measurement • Policy focus is on efficiency and effectiveness

  6. Quality is important • Quality is multi-dimensional • Quantity and quality of life (mortality & morbidity) • Quality may vary over time (inter-temporal nature) (eg survival rates) • Indicators may also reflect other factors (attribution) (eg lifestyle) • Choice of counterfactual? • Before and after treatment • What would otherwise have occurred • Choice of appropriate quality measures to use? • Composite measure based on indicators • How to weight different metrics & time periods? • Overarching measures (eg life expectancy)? • 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 • Seldom done in practice due to the absence of suitable summary measures

  7. Considerable variation between treatments and jurisdictions

  8. Stochastic frontier analysis of state & territory public hospital systems • Unlike DEA, SFA allow for measurement error, not just inefficiency • The model estimated contains • 1 Output (casemix-adjusted separations per jurisdiction) • 3 Inputs (labour (FTE), real capital services, real medical supplies) • Estimated in Stata using maximum likelihood • Data from Australian Institute of Health & Welfare; Report on Government Service Provision; Australian Bureau of Statistics • All variables expressed per 1000 residents – no adjustment for demographics • Covers the period: 1996-97 to 2004-05 • Alternative models • Quality adjusted output (Casemix-adjusted separations adjusted by an index of life-expectancy at birth by state) • Time invariant, Time variant

  9. Public hospitals: implied productivity gap by state

  10. Public hospitals: implied productivity gap by state

  11. Summary • 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 • 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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