IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/26202.html

When Technological Advance Meets Physician Learning in Drug Prescribing

Author

Listed:
  • 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
    Note: EH
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w26202.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. repec:wly:hlthec:v:26:y:2017:i::p:106-126 is not listed on IDEAS
    4. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    10. 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.
    11. 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.
    12. repec:bla:scotjp:v:46:y:1999:i:2:p:111-34 is not listed on IDEAS
    13. 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.
    14. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Avdic & Katharina E. Blankart, 2021. "A hard look at soft cost-control measures in healthcare organizations: Evidence from preferred drug policies in Germany," Papers 2021-07, Centre for Health Economics, Monash University.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    3. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    4. Patrick Kline & Christopher R. Walters, 2019. "On Heckits, LATE, and Numerical Equivalence," Econometrica, Econometric Society, vol. 87(2), pages 677-696, March.
    5. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    6. Atella, Vincenzo & Belotti, Federico & Giaccherini, Matilde & Medea, Gerardo & Nicolucci, Antonio & Sbraccia, Paolo & Mortari, Andrea Piano, 2024. "Lifetime costs of overweight and obesity in Italy," Economics & Human Biology, Elsevier, vol. 53(C).
    7. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    8. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2015. "Going Beyond LATE: Bounding Average Treatment Effects of Job Corps Training," IZA Discussion Papers 9511, Institute of Labor Economics (IZA).
    9. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    10. Nobel Prize Committee, 2021. "Answering causal questions using observational data," Nobel Prize in Economics documents 2021-2, Nobel Prize Committee.
    11. Mogstad, Magne & Torgovitsky, Alexander & Walters, Christopher R., 2024. "Policy evaluation with multiple instrumental variables," Journal of Econometrics, Elsevier, vol. 243(1).
    12. Stefan Boes, 2013. "Nonparametric analysis of treatment effects in ordered response models," Empirical Economics, Springer, vol. 44(1), pages 81-109, February.
    13. Wang, Xintong & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2025. "The effects of Vietnam-era military service on the long-term health of veterans: A bounds analysis," Journal of Health Economics, Elsevier, vol. 101(C).
    14. Federico Belotti & Joanna Kopinska & Alessandro Palma & Andrea Piano Mortari, 2022. "Health status and the Great Recession. Evidence from electronic health records," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1770-1799, August.
    15. Ogundari, Kolawole, 2021. "A systematic review of statistical methods for estimating an education production function," MPRA Paper 105283, University Library of Munich, Germany.
    16. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    17. Ismael Mourifie & Yuanyuan Wan, 2015. "(Partially) Identifying potential outcome distributions in triangular systems," Working Papers tecipa-532, University of Toronto, Department of Economics.
    18. Denni Tommasi & Arthur Lewbel & Rossella Calvi, 2017. "LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India," Working Papers ECARES ECARES 2017-27, ULB -- Universite Libre de Bruxelles.
    19. Lina Zhang & David T. Frazier & D.S. Poskitt & Xueyan Zhao, 2025. "Decomposing identification gains and evaluating instrument identification power for partially identified average treatment effects," Econometric Reviews, Taylor & Francis Journals, vol. 44(7), pages 915-938, August.
    20. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:26202. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.