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The role of GP’s compensation schemes in diabetes care: evidence from panel data

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  • E. Iezzi
  • M. Lippi Bruni
  • C. Ugolini

Abstract

The design of incentive schemes that improve quality of care is a central issue for the healthcare sector. Nowadays we observe many pay-for-performance programs, where payment is contingent on meeting indicators of provider effort, but also other alternative strategies have been introduced, for example programs rewarding physicians for participation in diseases management plans. Although it has been recognised that incentive-based remuneration schemes can have an impact on GP behaviour, there is still weak empirical evidence on the extent to which such programs influence health outcomes. We investigate the impact of financial incentives in Regional and Local Health Authority contracts for primary care in the Italian Region Emilia Romagna for the years 2003-05. We focus on avoidable hospitalisations (Ambulatory Care Sensitive Conditions) for patients affected by type 2 diabetes mellitus, for which the assumption of responsibility and the adoption of clinical guidelines are specifically rewarded. We estimate a panel count data model using a Negative Binomial distribution to test the hypothesis that, other things equal, patients under the responsibility of GPs receiving a higher share of their income through these programs are less likely to experience avoidable hospitalisations. Our findings support the hypothesis that financial transfers may contribute to improve quality of care, even when they are not based on the ex-post verification of performances.

Suggested Citation

  • E. Iezzi & M. Lippi Bruni & C. Ugolini, 2011. "The role of GP’s compensation schemes in diabetes care: evidence from panel data," Working Papers wp766, Dipartimento Scienze Economiche, Universita' di Bologna.
  • Handle: RePEc:bol:bodewp:wp766
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    References listed on IDEAS

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    Cited by:

    1. Brekke, Kurt R. & Holmås, Tor Helge & Monstad, Karin & Straume, Odd Rune, 2015. "Socioeconomic Status and Physicians’ Treatment Decisions," Discussion Paper Series in Economics 12/2015, Norwegian School of Economics, Department of Economics.
    2. Lippi Bruni, Matteo & Mammi, Irene & Ugolini, Cristina, 2016. "Does the extension of primary care practice opening hours reduce the use of emergency services?," Journal of Health Economics, Elsevier, vol. 50(C), pages 144-155.
    3. Kongstad, L.P. & Mellace, G. & Olsen, K.R., 2016. "Can the use of Electronic Health Records in General Practice reduce hospitalizations for diabetes patients? Evidence from a natural experiment," Health, Econometrics and Data Group (HEDG) Working Papers 16/25, HEDG, c/o Department of Economics, University of York.
    4. Fiorentini, Gianluca & Lippi Bruni, Matteo & Ugolini, Cristina, 2013. "GPs and hospital expenditures. Should we keep expenditure containment programs alive?," Social Science & Medicine, Elsevier, vol. 82(C), pages 10-20.
    5. Matteo Lippi Bruni & Irene Mammi, 2017. "Spatial effects in hospital expenditures: A district level analysis," Health Economics, John Wiley & Sons, Ltd., vol. 26, pages 63-77, September.
    6. repec:nip:nipewp:05/2015 is not listed on IDEAS
    7. Donatini A. & Fiorentini G. & Lippi Bruni M. & Mammi I. & Ugolini C., 2014. "Dealing with minor illnesses: the link between primary care characteristics and First Aid Clinics’ attendances," Working Papers wp925, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. Luiz Flavio Andrade & Thomas Rapp & Christine Sevilla-Dedieu, 2016. "Exploring the determinants of endocrinologist visits by patients with diabetes," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(9), pages 1173-1184, December.

    More about this item

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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