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Health Care Adherence and Personalized Medicine

Author

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  • Mark Egan
  • Tomas J. Philipson

Abstract

Non-adherence in health care results when a patient does not initiate or continue care that a provider has recommended. Previous research identifies non-adherence as a major source of waste in US health care, totaling approximately 2.3% of GDP, and have proposed a plethora of interventions to raise adherence. However, health economics provides little explicit analyses of the important dynamic demand behavior that drives non-adherence, and it is often casually attributed to uninformed patients. We argue that whereas providers may be more informed about the population-wide effects of treatments, patients are more informed about the individual specific value of treatment. We interpret a patient’s decision to adhere to a treatment regime as an optimal stopping problem in which patients learn the value of a treatment through treatment experience. We derive strong positive and normative implications resulting from interpreting non-adherence as an optimal stopping problem. Our positive analysis derives an “adherence survival function,” depicting the share of patients still on treatment as a function of time, and predicts how various observable factors alter adherence. Our normative analysis derives the efficiency effects of non-adherence and the conditions under which adherence is too high or low. We consider the efficiency implications of this analysis for common adherence interventions. We argue that personalized medicine is intimately linked to adherence issues. It replaces the learning through treatment experience with a diagnostic test, and thereby speeds up the leaning process and cuts over-adherence and raises underadherence. We assess the quantitative implications of our analysis by calibrating the degree of over- and under-adherence for one of the largest US drug categories, cholesterol-reducing drugs. Contrary to frequent normative claims of under-adherence, our estimates suggest the efficiency loss from overadherence is over 80% larger than from under-adherence, even though only 43% of patients fully adhere.

Suggested Citation

  • Mark Egan & Tomas J. Philipson, 2014. "Health Care Adherence and Personalized Medicine," NBER Working Papers 20330, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:20330
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    References listed on IDEAS

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    1. Tomas Philipson & Larry V. Hedges, 1998. "Subject Evaluation in Social Experiments," Econometrica, Econometric Society, vol. 66(2), pages 381-408, March.
    2. Amitabh Chandra & Jonathan Gruber & Robin McKnight, 2010. "Patient Cost-Sharing and Hospitalization Offsets in the Elderly," American Economic Review, American Economic Association, vol. 100(1), pages 193-213, March.
    3. Tomas Philipson, 1997. "Data Markets and the Production of Surveys," Review of Economic Studies, Oxford University Press, vol. 64(1), pages 47-72.
    4. Ljungqvist, Lars & Sargent, Thomas J., 2012. "Recursive Macroeconomic Theory, Third Edition," MIT Press Books, The MIT Press, edition 3, volume 1, number 0262018748, May.
    5. Jovanovic, Boyan, 1979. "Firm-specific Capital and Turnover," Journal of Political Economy, University of Chicago Press, vol. 87(6), pages 1246-1260, December.
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    Cited by:

    1. Rebecca Myerson & Darius Lakdawalla & Lisandro D. Colantonio & Monika Safford & David Meltzer, 2018. "Effects of expanding health screening on treatment – What should we expect? What can we learn?," Working Papers 2018-014, Human Capital and Economic Opportunity Working Group.
    2. Nicolas Bouckaert & Erik Schokkaert, 2016. "Differing types of medical prevention appeal to different individuals," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(3), pages 317-337, April.

    More about this item

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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