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Health Care Adherence and Personalized Medicine

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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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    Cited by:

    1. Rebecca Mary 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?," NBER Working Papers 24347, National Bureau of Economic Research, Inc.
    2. Kristopher J. Hult, 2017. "Measuring the Potential Health Impact of Personalized Medicine: Evidence from MS Treatments," NBER Working Papers 23900, National Bureau of Economic Research, Inc.
    3. Carrieri, Vincenzo & Madio, Leonardo & Principe, Francesco, 2020. "Do-It-Yourself medicine? The impact of light cannabis liberalization on prescription drugs," Journal of Health Economics, Elsevier, vol. 74(C).
    4. Cohen, Jessica & Saran, Indrani, 2018. "The impact of packaging and messaging on adherence to malaria treatment: Evidence from a randomized controlled trial in Uganda," Journal of Development Economics, Elsevier, vol. 134(C), pages 68-95.
    5. Zhiwen Xie & Patricia St. Clair & Dana P Goldman & Geoffrey Joyce, 2019. "Racial and ethnic disparities in medication adherence among privately insured patients in the United States," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-9, February.
    6. Ewelina Nojszewska & Agata Sielska, 2022. "Macroeconomic and Social Indicators to Launch the PM-Based VBHC Model in the Healthcare System in Poland," IJERPH, MDPI, vol. 19(3), pages 1-25, February.
    7. Kristopher J. Hult, 2018. "Measuring the Potential Health Impact of Personalized Medicine: Evidence from Multiple Sclerosis Treatments," NBER Chapters, in: Economic Dimensions of Personalized and Precision Medicine, pages 185-216, National Bureau of Economic Research, Inc.
    8. 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.

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    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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