IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2112.01639.html
   My bibliography  Save this paper

Patient-Centered Appraisal of Race-Free Clinical Risk Assessment

Author

Listed:
  • Charles F. Manski

Abstract

Until recently, there has been a consensus that clinicians should condition patient risk assessments on all observed patient covariates with predictive power. The broad idea is that knowing more about patients enables more accurate predictions of their health risks and, hence, better clinical decisions. This consensus has recently unraveled with respect to a specific covariate, namely race. There have been increasing calls for race-free risk assessment, arguing that using race to predict patient outcomes contributes to racial disparities and inequities in health care. Writers calling for race-free risk assessment have not studied how it would affect the quality of clinical decisions. Considering the matter from the patient-centered perspective of medical economics yields a disturbing conclusion: Race-free risk assessment would harm patients of all races.

Suggested Citation

  • Charles F. Manski, 2021. "Patient-Centered Appraisal of Race-Free Clinical Risk Assessment," Papers 2112.01639, arXiv.org, revised Feb 2022.
  • Handle: RePEc:arx:papers:2112.01639
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2112.01639
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
    2. 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.
    3. Meltzer, David, 2001. "Addressing uncertainty in medical cost-effectiveness analysis: Implications of expected utility maximization for methods to perform sensitivity analysis and the use of cost-effectiveness analysis to s," Journal of Health Economics, Elsevier, vol. 20(1), pages 109-129, January.
    4. Charles E. Phelps & Alvin I. Mushlin, 1988. "Focusing Technology Assessment Using Medical Decision Theory," Medical Decision Making, , vol. 8(4), pages 279-289, December.
    5. Mark Pauly, 1980. "Doctors and Their Workshops: Economic Models of Physician Behavior," NBER Books, National Bureau of Economic Research, Inc, number paul80-1.
    6. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    7. Anirban Basu & David Meltzer, 2007. "Value of Information on Preference Heterogeneity and Individualized Care," Medical Decision Making, , vol. 27(2), pages 112-127, March.
    8. Charles F. Manski, 2018. "Response to commentaries on “Reasonable patient care under uncertainty”," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1431-1434, October.
    9. Mark Pauly, 1980. "Physicians as Agents," NBER Chapters, in: Doctors and Their Workshops: Economic Models of Physician Behavior, pages 1-16, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
    3. Greve, Jane & Kristensen, Søren Rud & Lydiksen, Nis, 2023. "Patient and peer: Guideline design and expert response," Journal of Health Economics, Elsevier, vol. 92(C).
    4. David Epstein, 2019. "Beyond the cost‐effectiveness acceptability curve: The appropriateness of rank probabilities for presenting the results of economic evaluation in multiple technology appraisal," Health Economics, John Wiley & Sons, Ltd., vol. 28(6), pages 801-807, June.
    5. 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.
    6. Currie, Janet & Lin, Wanchuan & Zhang, Wei, 2011. "Patient knowledge and antibiotic abuse: Evidence from an audit study in China," Journal of Health Economics, Elsevier, vol. 30(5), pages 933-949.
    7. Tianyan Hu & Sandra L. Decker & Shin-Yi Chou, 2014. "The Impact of Health Insurance Expansion on Physician Treatment Choice: Medicare Part D and Physician Prescribing," NBER Working Papers 20708, National Bureau of Economic Research, Inc.
    8. Galina Besstremyannaya & Sergei Golovan, 2023. "Measuring heterogeneity in hospital productivity: a quantile regression approach," Journal of Productivity Analysis, Springer, vol. 59(1), pages 15-43, February.
    9. 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.
    10. Haruko Noguchi, 2015. "How does the Price Regulation Policy Impact on Patient–Nurse Ratios and the Length of Hospital Stays in Japanese Hospitals?," Asian Economic Policy Review, Japan Center for Economic Research, vol. 10(2), pages 301-323, July.
    11. Müller, Tobias & Schmid, Christian & Gerfin, Michael, 2023. "Rents for Pills: Financial incentives and physician behavior," Journal of Health Economics, Elsevier, vol. 87(C).
    12. Seema S. Sonnad & Stephen Earl Foreman, 1997. "An incentive approach to physician implementation of medical practice guidelines," Health Economics, John Wiley & Sons, Ltd., vol. 6(5), pages 467-477, September.
    13. Charles F. Manski, 2016. "Credible Ecological Inference for Personalized Medicine: Formalizing Clinical Judgment," NBER Working Papers 22643, National Bureau of Economic Research, Inc.
    14. 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.
    15. Alaka Holla & Jishnu Das & Aakash Mohpal & Karthik Muralidharan, 2015. "Quality and Accountability in Healthcare Delivery: Audit Evidence from Primary Care Providers in India," Working Papers id:7219, eSocialSciences.
    16. Sloan, Frank A. & Picone, Gabriel A. & TaylorJr., Donald H. & Chou, Shin-Yi, 2001. "Hospital ownership and cost and quality of care: is there a dime's worth of difference?," Journal of Health Economics, Elsevier, vol. 20(1), pages 1-21, January.
    17. Mitchell, Jean M. & Sass, Tim R., 1995. "Physician ownership of ancillary services: Indirect demand inducement or quality assurance?," Journal of Health Economics, Elsevier, vol. 14(3), pages 263-289, August.
    18. Rachael L. Fleurence, 2007. "Setting priorities for research: a practical application of 'payback' and expected value of information," Health Economics, John Wiley & Sons, Ltd., vol. 16(12), pages 1345-1357.
    19. Gillis, Kurt D. & Lee, David W., 1997. "Medicare, access, and physicians' willingness to accept new Medicare patients," The Quarterly Review of Economics and Finance, Elsevier, vol. 37(3), pages 579-603.
    20. Samer A. Kharroubi & Alan Brennan & Mark Strong, 2011. "Estimating Expected Value of Sample Information for Incomplete Data Models Using Bayesian Approximation," Medical Decision Making, , vol. 31(6), pages 839-852, November.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2112.01639. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.