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Value of Information on Preference Heterogeneity and Individualized Care

Author

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  • Anirban Basu

    (Section of General Internal Medicine, Department of Medicine, Center for Health and Social Sciences, University of Chicago, Chicago, Illinois)

  • David Meltzer

    (Section of General Internal Medicine, Department of Medicine, Center for Health and Social Sciences, University of Chicago, Chicago, Illinois)

Abstract

Background . Cost-effectiveness analysis traditionally focuses on identifying when treatments are cost-effective based on their average benefits and costs in the population. However, there may be considerable value in identifying when treatments are cost-effective for individual patients given their preferences or other personal attributes. Objectives . To present a theoretical framework to assess the potential value of identifying cost-effective treatments for individual patients given their preferences and to compare the value of individualized treatment decisions with the value of treatment decisions based on traditional population-level cost-effectiveness analysis. Methods . The authors calculate the expected value of individualized care (EVIC), which represents the potential value of providing physicians information on the preferences of individual patients, such as quality-of-life (QOL) weights, so as to make individualized treatment decisions. They also show how EVIC varies with insurance structures that do not internalize relative costs of treatments. They illustrate this theory using an example in which physicians make treatment choices for 65-year-old prostate cancer patients. Results . The value of identifying cost-effective treatments at the individual level for 65-year-old prostate cancer patients in the United States is about $70 million annually. This is more than 100 times the $0.7 million annual value of identifying the cost-effective treatment on average for this population. However, failure to internalize costs almost eliminates the value of individualized care. Conclusions . The value of individualizing care can be far greater than the value of improved decision making at the group level. However, this can vary immensely with insurance. EVIC can provide a guide as to when the high value of individualized care may make population-level decision making especially at risk of providing poor guidance for coverage decisions. Future studies of the value of individualized care should also consider baseline levels of individualization of care. Key words: preferences; value of information; individualization of care; quality of life; prostate cancer. (Med Decis Making 2007; 27: 112—127)

Suggested Citation

  • Anirban Basu & David Meltzer, 2007. "Value of Information on Preference Heterogeneity and Individualized Care," Medical Decision Making, , vol. 27(2), pages 112-127, March.
    • Handle: RePEc:sae:medema:v:27:y:2007:i:2:p:112-127
    DOI: 10.1177/0272989X06297393
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    References listed on IDEAS

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    1. Mark Pauly, 1980. "Appendix to "Doctors and Their Workshops: Economic Models of Physician Behavior"," NBER Chapters, in: Doctors and Their Workshops: Economic Models of Physician Behavior, pages 119-122, National Bureau of Economic Research, Inc.
    2. Mark Pauly, 1980. "Doctors and Their Workshops: Economic Models of Physician Behavior," NBER Books, National Bureau of Economic Research, Inc, number paul80-1.
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    Citations

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    Cited by:

    1. Manuel Antonio Espinoza & Andrea Manca & Karl Claxton & Mark Sculpher, 2018. "Social value and individual choice: The value of a choice‐based decision‐making process in a collectively funded health system," Health Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 28-40, February.
    2. 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.
    3. Charles F. Manski, 2022. "Patient‐centered appraisal of race‐free clinical risk assessment," Health Economics, John Wiley & Sons, Ltd., vol. 31(10), pages 2109-2114, October.
    4. Basu Anirban, 2013. "Personalized Medicine in the Context of Comparative Effectiveness Research," Forum for Health Economics & Policy, De Gruyter, vol. 16(2), pages 73-86, June.
    5. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    6. Basu, Anirban, 2011. "Economics of individualization in comparative effectiveness research and a basis for a patient-centered health care," Journal of Health Economics, Elsevier, vol. 30(3), pages 549-559, May.
    7. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
    8. Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
    9. David Glynn & John Giardina & Julia Hatamyar & Ankur Pandya & Marta Soares & Noemi Kreif, 2024. "Integrating decision modeling and machine learning to inform treatment stratification," Health Economics, John Wiley & Sons, Ltd., vol. 33(8), pages 1772-1792, August.
    10. Dominic Hodgkin & Joanna Volpe‐Vartanian & Elizabeth L. Merrick & Constance M. Horgan & Andrew A. Nierenberg & Richard G. Frank & Sue Lee, 2012. "Customization in prescribing for bipolar disorder," Health Economics, John Wiley & Sons, Ltd., vol. 21(6), pages 653-668, June.
    11. Charles F. Manski, 2023. "Using Limited Trial Evidence to Credibly Choose Treatment Dosage when Efficacy and Adverse Effects Weakly Increase with Dose," NBER Working Papers 31305, National Bureau of Economic Research, Inc.
    12. Meltzer David O., 2013. "Opportunities in the Economics of Personalized Health Care and Prevention," Forum for Health Economics & Policy, De Gruyter, vol. 16(2), pages 13-22, June.
    13. Anna Heath & Petros Pechlivanoglou, 2022. "Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity," Medical Decision Making, , vol. 42(5), pages 649-660, July.
    14. 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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