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Measuring the Potential Health Impact of Personalized Medicine: Evidence from MS Treatments

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  • Kristopher J. Hult

Abstract

Individuals respond to pharmaceutical treatments differently due to the heterogeneity of patient populations. This heterogeneity can make it difficult to determine how efficacious or burdensome a treatment is for an individual patient. Personalized medicine involves using patient characteristics, therapeutics, or diagnostic testing to understand how individual patients respond to a given treatment. Personalized medicine increases the health impact of existing treatments by improving the matching process between patients and treatments and by improving a patient's understanding of the risk of serious side effects. In this paper, I compare the health impact of new treatment innovations with the potential health impact of personalized medicine. I find that the impact of personalized medicine depends on the number of treatments, the correlation between treatment effects, and the amount of noise in a patient's individual treatment effect signal. For multiple sclerosis treatments, I find that personalized medicine has the potential to increase the health impact of existing treatments by roughly 50 percent by informing patients of their individual treatment effect and risk of serious side effects.

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  • 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.
    • Handle: RePEc:nbr:nberwo:23900
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    3. Mark Egan & Tomas J. Philipson, 2014. "Health Care Adherence and Personalized Medicine," NBER Working Papers 20330, National Bureau of Economic Research, Inc.
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    5. Anirban Basu & Anupam B. Jena & Dana P. Goldman & Tomas J. Philipson & Robert Dubois, 2014. "Heterogeneity In Action: The Role Of Passive Personalization In Comparative Effectiveness Research," Health Economics, John Wiley & Sons, Ltd., vol. 23(3), pages 359-373, March.
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    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics

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    Cited by:

    1. Xingyuan Wang & Yun Liu & Hongchen Liu, 2020. "Examining Users’ Adoption of Precision Medicine: The Moderating Role of Medical Technical Knowledge," IJERPH, MDPI, vol. 17(3), pages 1-16, February.

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    More about this item

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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