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Physicians’ incentives to adopt personalised medicine: Experimental evidence

Author

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  • David Bardey

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CEDE - Los Andes University)

  • Samuel Kembou

    (UNIL - Université de Lausanne = University of Lausanne)

  • Bruno Ventelou

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

We study physicians' incentives to use personalised medicine techniques, replicating the physician's trade-offs under the option of personalised medicine information. In a laboratory experiment conducted in two French Universities, prospective physicians played a real-effort game. We vary both the information structure (free access versus paid access to personalised medicine information) and the payment scheme (pay-for-performance (P4P), capitation (CAP) and fee-for-service (FFS)), implementing a within-subject design. Our results are threefold: (i) Compared to FFS and CAP, the P4P scheme strongly and positively impacts the decision to adopt personalised medicine. (ii) Although expected to dominate the other schemes, P4P is not always efficient in transforming free access to personalised medicine into higher quality of care. (iii) When it has to be paid for and after controlling for self-selection, personalised medicine is positively associated with quality, suggesting that subjects tend to make better use of information that comes at a cost. We find this effect to be stronger for males than for females prospective physicians. Quantification of our results however suggests that this positive impact is not strong enough to justify generalising the payment for personalised medicine access. Finally, we develop a theoretical model that includes in its set-up a commitment device component, which is the mechanism that we inferred from the data of the experiment. Our model replicates the principal results of the experiment, reinforcing the interpretation that the higher quality provided by subjects who bought personalised medicine can be interpreted as a commitment device effect.

Suggested Citation

  • David Bardey & Samuel Kembou & Bruno Ventelou, 2021. "Physicians’ incentives to adopt personalised medicine: Experimental evidence," Post-Print hal-03420688, HAL.
  • Handle: RePEc:hal:journl:hal-03420688
    DOI: 10.1016/j.jebo.2021.07.037
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-03420688v1
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    Cited by:

    1. Massimo Finocchiaro Castro & Calogero Guccio & Domenica Romeo, 2024. "Looking inside the lab: a systematic literature review of economic experiments in health service provision," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(7), pages 1177-1204, September.
    2. David Bardey & Philippe de Donder & Vera Zaporozhets, 2024. "Economic Incentives to Develop and to Use Diagnostic Tests - A Literature Review," Working Papers hal-04472497, HAL.
    3. Finocchiaro Castro, Massimo & Guccio, Calogero & Romeo, Domenica, 2022. "A systematic literature review of 10 years of behavioral research on health services," EconStor Preprints 266248, ZBW - Leibniz Information Centre for Economics.
    4. Kurt R. Brekke & Dag Morten Dalen & Odd Rune Straume, 2024. "Competing with precision: incentives for developing predictive biomarker tests," Scandinavian Journal of Economics, Wiley Blackwell, vol. 126(1), pages 60-97, January.
    5. Michel Mougeot & Florence Naegelen, 2022. "Incentives to implement personalized medicine under second‐best pricing," Health Economics, John Wiley & Sons, Ltd., vol. 31(11), pages 2411-2424, November.

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    Keywords

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    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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