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Fixing Misallocation with Guidelines: Awareness vs. Adherence

Author

Listed:
  • Jason Abaluck
  • Leila Agha
  • David C. Chan Jr
  • Daniel Singer
  • Diana Zhu

Abstract

Expert decisions often deviate from evidence-based guidelines. If experts are unaware of guidelines, dissemination may improve outcomes. If experts are aware of guidelines but continue to deviate, promoting stricter adherence has ambiguous effects on outcomes depending on whether experts have information not in guidelines. We study guidelines for anticoagulant use to prevent strokes among atrial fibrillation patients. By text-mining physician notes, we identify when physicians start using guidelines. After mentioning guidelines, physicians become more guideline-concordant, but adherence remains far from perfect. To evaluate whether nonadherence reflects physicians’ superior information, we combine observational data on treatment choices with machine learning estimates of heterogeneous treatment effects from eight randomized trials. Most departures from guidelines are not justified by measurable treatment effect heterogeneity. Promoting stricter adherence to guidelines could prevent 24% more strokes, producing much larger gains than broader guideline awareness.

Suggested Citation

  • Jason Abaluck & Leila Agha & David C. Chan Jr & Daniel Singer & Diana Zhu, 2020. "Fixing Misallocation with Guidelines: Awareness vs. Adherence," NBER Working Papers 27467, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:27467
    Note: AG EH LS PE PR
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    Cited by:

    1. Amy Finkelstein & Petra Persson & Maria Polyakova & Jesse M. Shapiro, 2022. "A Taste of Their Own Medicine: Guideline Adherence and Access to Expertise," American Economic Review: Insights, American Economic Association, vol. 4(4), pages 507-526, December.
    2. Dahlstrand Rudin, Amanda, 2022. "Defying distance? The provision of services in the digital age," LSE Research Online Documents on Economics 118042, London School of Economics and Political Science, LSE Library.
    3. Ashesh Rambachan, 2022. "Identifying Prediction Mistakes in Observational Data," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
    4. Amanda Dahlstrand, 2022. "Defying distance? The provision of services in the digital age," CEP Discussion Papers dp1889, Centre for Economic Performance, LSE.
    5. Hannes Ullrich & Michael Allan Ribers, 2023. "Machine predictions and human decisions with variation in payoffs and skill: the case of antibiotic prescribing," Berlin School of Economics Discussion Papers 0027, Berlin School of Economics.

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    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
    • 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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