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Assessing Efficiency Gains in Public Health Sector

Explore methods, data, and analysis for enhancing efficiency in healthcare. Evaluate outcomes, inputs, and institutional settings to identify areas for improvement and benchmark OECD countries. No universal solution, but tailored recommendations for enhanced value.

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Assessing Efficiency Gains in Public Health Sector

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  1. Assessing the potential for efficiency gains in the public health sector Christophe André, OECD EconomicsDepartment Presentation to the Fagerberg Committee Friday 5 November 2010

  2. Context and approach • Economic Policy Committee, Working Party No. 1 on Macroeconomic and Structural Policy Analysis (Joumard et al., OECD Economics Department Working Paper No. 769) • Definition of outcomes and inputs • Methodology: efficiency frontier (DEA and panel regressions) • Information on institutional settings gathered through questionnaires

  3. Efficiency in health care is difficult to measure and analyse • No obvious definition of health care outcomes and inputs • A large variety of actors (hospitals, outpatient physicians, drug companies, etc.) • Cross-country data on health care outcomes are imperfect • Mix of public and private spending • Data on institutions are largely missing

  4. Choosing the level of analysis • System level • Disease level • Sub-sector level Reference: Häkkinen and Joumard, OECD Economics Department Working Paper No. 554.

  5. Measuring health care outcomes • Raw mortality/longevity indicators (life expectancy, premature mortality, infant mortality…) • Longevity indicators adjusted for morbidity/disability (HALE, DFLE) • Amenable mortality • Other health-related indicators (equity, public satisfaction)

  6. Identifying the determinants of health status (“inputs”) • Health care resources per capita (spending on health care, number of health practitioners…) • Lifestyle factors (diet, tobacco, alcohol…) • Socio-economic factors (GDP per capita, education, pollution…)

  7. Deriving efficiency indicators • Econometric approach: the unexplained component from panel regressions is assumed to reflect mainly health system efficiency • Data Envelopment Analysis (DEA): construction of an efficiency frontier for a given year using a non-parametric optimisation technique

  8. Econometric approach • Estimation of a production function; panel data regressions on a panel of 23 OECD countries over the period 1980-2003 Reference: Joumard et al. (2008), OECD Economics Department Working Paper No. 627.

  9. Panel regressions: unexplained differences in life expectancy(Health care resources measured in monetary terms) Years

  10. DEA: efficiency frontier and measurement of inefficiency

  11. DEA: output-oriented efficiency scores (spending efficiency)

  12. DEA and panel regression results (spending efficiency)

  13. DEA sensitivity analysis

  14. Characterising health care systems • Identify the institutional features which most differentiate OECD countries • Assess empirically how the various institutional features are combined across OECD countries (health care systems) • Assess whether one health care system provides better value for money

  15. Cluster analysis: identifying country groups

  16. Efficiency across and within groups

  17. Norway: Policy and institutions Group 6: Hungary, Ireland, Italy, New Zealand, Norway, Poland, United Kingdom.

  18. Norway: Efficiency and quality Group 6: Hungary, Ireland, Italy, New Zealand, Norway, Poland, United Kingdom.

  19. Norway: Activity and consumption Group 6: Hungary, Ireland, Italy, New Zealand, Norway, Poland, United Kingdom.

  20. Conclusions • Benchmarking OECD countries is possible; single number efficiency estimates are not perfect but reasonably robust • Groups of countries sharing broadly similar institutions can be identified • No big-bang reform and no one-size-fits-all recommendations • Areas for efficiency improvements can be identified

  21. Thankyou !

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