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Assessing productivity in Australian health services delivery: Some experimental estimates

Assessing productivity in Australian health services delivery: Some experimental estimates. Owen Gabbitas and Christopher Jeffs Productivity Commission 17 December 2007.

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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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