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Measuring public sector productivity: the case of NHS Trusts

Measuring public sector productivity: the case of NHS Trusts. Mary O’Mahony, (NIESR, University of Birmingham and AIM) Philip Stevens (NIESR and Ministry of Economic Development, New Zealand) Lucy Stokes (NIESR). This work was funded by the ESRC Public Services

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Measuring public sector productivity: the case of NHS Trusts

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  1. Measuring public sector productivity: the case of NHS Trusts Mary O’Mahony, (NIESR, University of Birmingham and AIM) Philip Stevens (NIESR and Ministry of Economic Development, New Zealand) Lucy Stokes (NIESR) This work was funded by the ESRC Public Services Programme grant number RES-153-25-0044

  2. Purpose • Consider productivity performance of NHS Trusts in England • Compare star ratings with alternative performance measures • Examine the context in which the health sector operates

  3. Defining productivity • Output per unit of input • Focus on labour productivity • Output indices - Cost weighted output index (CWOI) - CWOI adjusted for survival • Index of labour input - Full-time equivalents, by occupational group, weighted using data on earnings (alternative measures also considered)

  4. Data • 7 year time period: 1997/98 – 2003/04 • NHS Acute Trusts in England - complications in matching Trusts over time due to mergers etc. • Hospital Episode Statistics (HES) provided data on finished consultant episodes (FCEs), used to construct provider spells. HES also provides data on waiting times and mortality rates. • Reference Cost database provided data on unit costs.

  5. Data • NHS Workforce Census formed principal source for data on labour input (numbers employed), plus NHS Staff Earnings Survey and Trust Financial Returns (TFR). • TFR also provided data on expenditure on non-NHS staff (agency staff) • Data on intermediate and capital inputs also available e.g. on clinical supplies (including drugs) and use of information technology.

  6. Nature of the health service • Two ways of describing services delivered by health sector • Accident: Positive probability of contracting major disease, requiring insurance • Repair: With age more routine treatments required to enhance quality of life • Context matters • What types of treatment • For whom • Structure of population, other linked services provided in local areas

  7. Types of TreatmentShare of activity & expenditure by HRG, 2003/04: F - digestive N - maternity E – cardiac H -Musculo-skeletal

  8. Who is treated: Share of total activity and costs accounted for by the over 65s, 1997/98-2003/04

  9. Share of total activity and costs accounted for by the over 75s, 1997/98-2003/04

  10. Most NHS Trusts increasing activity on over 65s. HRG E, Cardiac: % of Trusts with increasing share of activity and expenditure on over 65s

  11. HRG H, Musculoskeletal: % of Trusts with increasing share of activity and expenditure on over 65s

  12. NHS Trust labour productivity levels, 2003

  13. Labour productivity Proportion of Trusts within 50% and 80% of Trust with the highest level of labour productivity

  14. What determines star ratings? • Many indicators employed, combined in complex ways • Most important seems to be waiting times • Average waiting times have been declining but much less so if weighted by unit costs • Implies much of the reduction occurs in low cost conditions • Possibility for gaming – Trusts could achieve higher Star ratings if they reduced focus on more older patients

  15. Is there a relationship between productivity and star ratings?Survival-adjusted CWOI per unit of labour input Survival-adjusted CWOI per unit of labour input

  16. What determines differences in labour productivityBackground variables • Considered the impact of background variables on relative levels of productivity across NHS Trusts. • Used data at the Local Authority level on income deprivation and net expenditure on social services for older people. • Using information on LA of residence in HES, matched data on background variables at the LA level with each individual record in the HES data. • Mean value then calculated by averaging across all individual records for each Trust.

  17. Regression results Dependent variable = log (output) *** significant at 99% level, ** significant at 95% level, *significant at 90% level

  18. Conclusions • The use of quantitative measures can be useful in informing relative evaluations of service providers • For public policy makers • And useful benchmark for managers • Context matters • Cannot look at hospitals in isolation • Productivity measures should be used in conjunction with other measures of performance • But should question accuracy of measures that give high awards to service providers that produce few outputs using many inputs

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