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When Technological Advance Meets Physician Learning in Drug Prescribing

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  • Domenico Depalo
  • Jay Bhattacharya
  • Vincenzo Atella
  • Federico Belotti

Abstract

The support for scientific investigation in biomedicine depends in part on the adoption of new knowledge into medical practice. We investigate how a technological advance, in the form of a large and influential 2010 randomized controlled study, changed physician practice in statin (a medication used to manage high cholesterol levels) prescribing. We analyze data representative of the Italian population for the period 2003-2014. Our analysis accounts for possible non-random sorting of patients into treatment. We show that both doctors and patients responded promptly to this technological shock, changing the mix of patients who received therapy, drug dosing, and frequency of testing for side effects, as well as patient adherence to therapy. The results show that investments in scientific knowledge can rapidly diffuse into practice in professions where continuing education is the norm.

Suggested Citation

  • Domenico Depalo & Jay Bhattacharya & Vincenzo Atella & Federico Belotti, 2019. "When Technological Advance Meets Physician Learning in Drug Prescribing," NBER Working Papers 26202, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26202
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    1. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    2. Eleonora Fichera & James Banks & Luigi Siciliani & Matt Sutton, 2017. "Does Patient Health Behaviour respond to Doctor’s Effort?," Department of Economics Working Papers 62/17, University of Bath, Department of Economics.
    3. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    4. Vincenzo Atella & Joanna Kopinska, 2014. "The impact of cost-sharing schemes on drug compliance in Italy: evidence based on quantile regression," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 59(2), pages 329-339, April.
    5. Vincenzo Atella & Federico Belotti & Domenico Depalo, 2017. "Drug therapy adherence and health outcomes in the presence of physician and patient unobserved heterogeneity," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 106-126, September.
    6. Philip Oreopoulos, 2006. "Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter," American Economic Review, American Economic Association, vol. 96(1), pages 152-175, March.
    7. Scott, Anthony & Vick, Sandra, 1999. "Patients, Doctors and Contracts: An Application of Principal-Agent Theory to the Doctor-Patient Relationship," Scottish Journal of Political Economy, Scottish Economic Society, vol. 46(2), pages 111-134, May.
    8. Atella, Vincenzo & Conti, Valentina, 2014. "The effect of age and time to death on primary care costs: The Italian experience," Social Science & Medicine, Elsevier, vol. 114(C), pages 10-17.
    9. Jay Bhattacharya & Azeem M. Shaikh & Edward Vytlacil, 2008. "Treatment Effect Bounds under Monotonicity Assumptions: An Application to Swan-Ganz Catheterization," American Economic Review, American Economic Association, vol. 98(2), pages 351-356, May.
    10. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    11. Anthony Scott & Sandra Vick, 1999. "Patients, Doctors and Contracts: An Application of Principal‐Agent Theory to the Doctor‐Patient Relationship," Scottish Journal of Political Economy, Scottish Economic Society, vol. 46(2), pages 111-134, May.
    12. Propper, Carol & Avdic, Daniel & von Hinke Kessler Scholder, Stephanie & Lagerqvist, Bo & Vikström, Johan, 2019. "Information shocks and provider responsiveness: evidence from interventional cardiology," CEPR Discussion Papers 13627, C.E.P.R. Discussion Papers.
    13. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    14. Vincenzo Atella & Francesco D’Amico, 2015. "Who is responsible for your health: is it you, your doctor or the new technologies?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(8), pages 835-846, November.
    15. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    16. Janet M. Currie & W. Bentley MacLeod, 2018. "Understanding Doctor Decision Making: The Case of Depression," NBER Working Papers 24955, National Bureau of Economic Research, Inc.
    17. Atella, Vincenzo & Belotti, Federico & Bojke, Chris & Castelli, Adriana & Grašič, Katja & Kopinska, Joanna & Piano Mortari, Andrea & Street, Andrew, 2019. "How health policy shapes healthcare sector productivity? Evidence from Italy and UK," Health Policy, Elsevier, vol. 123(1), pages 27-36.
    18. repec:wly:hlthec:v:26:y:2017:i::p:106-126 is not listed on IDEAS
    19. Fichera, Eleonora & Banks, James & Siciliani, Luigi & Sutton, Matt, 2018. "Does patient health behaviour respond to doctor effort?," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 225-251.
    20. Gafni, Amiram & Charles, Cathy & Whelan, Tim, 1998. "The physician-patient encounter: The physician as a perfect agent for the patient versus the informed treatment decision-making model," Social Science & Medicine, Elsevier, vol. 47(3), pages 347-354, August.
    21. Richard Blundell & Lorraine Dearden & Barbara Sianesi, 2005. "Evaluating the effect of education on earnings: models, methods and results from the National Child Development Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 473-512, July.
    22. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 555-574.
    23. Dyfrig A. Hughes & Adrian Bagust & Alan Haycox & Tom Walley, 2001. "The impact of non‐compliance on the cost‐effectiveness of pharmaceuticals: a review of the literature," Health Economics, John Wiley & Sons, Ltd., vol. 10(7), pages 601-615, October.
    24. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
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    Cited by:

    1. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
    2. Avdic, Daniel & Blankart, Katharina, 2021. "A Hard Look at “Soft” Cost‐control Measures in Healthcare Organizations: Evidence from Preferred Drug Policies in Germany," CINCH Working Paper Series (since 2020) 74978, Duisburg-Essen University Library, DuEPublico.
    3. Avdic, Daniel & Blankart, Katharina, 2021. "A Hard Look at “Soft” Cost‐control Measures in Healthcare Organizations: Evidence from Preferred Drug Policies in Germany," CINCH Working Paper Series (since 2020) 74978, Duisburg-Essen University Library, DuEPublico.

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    More about this item

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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