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Focusing Technology Assessment Using Medical Decision Theory

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  • Charles E. Phelps
  • Alvin I. Mushlin

Abstract

Combining medical decision theory and epidemiologic information, the authors have devel oped a strategy to assess diagnostic technologies. For any patient, patient utilities with new diagnostic information are compared with the preferred fallback action absent that diagnostic information. After determination of whether the expected value of diagnostic information (EVDI) justifies its cost, the method adds across the eligible population to determine whether the global EVDI justifies the technology's deployment, employing a screen (Hurdle 1) that assumes that the diagnostic device has perfect accuracy. This preliminary evaluation relies on published data on treatment efficacy, population probabilities of illness, etc., but not on new clinical trials. If the technology is not sufficiently cost-effective, even with this optimistic assumption, the strategy recommends against its use. Otherwise, the next step is Hurdle II, in which the critical clinical studies, identified by the decision-theory model, are undertaken. These commonly include measuring the actual diagnostic accuracy of a device, with which the cost-effectiveness is recalculated. These studies in general do not require randomized controlled trials. Key words: technology assessment; decision theory; diagnostic information. (Med Decis Making 8:279-289, 1988)

Suggested Citation

  • Charles E. Phelps & Alvin I. Mushlin, 1988. "Focusing Technology Assessment Using Medical Decision Theory," Medical Decision Making, , vol. 8(4), pages 279-289, December.
    • Handle: RePEc:sae:medema:v:8:y:1988:i:4:p:279-289
    DOI: 10.1177/0272989X8800800409
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    References listed on IDEAS

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    1. Peter Doubilet & Barbara J. McNeil & Milton C. Weinstein, 1985. "The Decision Concerning Coronary Angiography in Patients with Chest Pain," Medical Decision Making, , vol. 5(3), pages 293-309, August.
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    Cited by:

    1. 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.
    2. George Laking & Joanne Lord & Alastair Fischer, 2006. "The economics of diagnosis," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1109-1120, October.
    3. De Donder, Philippe & Bardey, David & Zaporozhets, Vera, 2024. "The Health Technology Assessment Approach of the Economic Value of Diagnostic Tests - A Literature Review," TSE Working Papers 24-1508, Toulouse School of Economics (TSE).
    4. Joanne Lord & George Laking & Alastair Fischer, 2006. "Non‐linearity in the cost‐effectiveness frontier," Health Economics, John Wiley & Sons, Ltd., vol. 15(6), pages 565-577, June.
    5. Charles F. Manski & Eytan Sheshinski, 2024. "The Subtlety of Optimal Paternalism in a Population with Bounded Rationality," Papers 2410.13658, arXiv.org.
    6. Greve, Jane & Kristensen, Søren Rud & Lydiksen, Nis, 2023. "Patient and peer: Guideline design and expert response," Journal of Health Economics, Elsevier, vol. 92(C).
    7. Hui Zhang & Christian Wernz & Danny R. Hughes, 2018. "Modeling and designing health care payment innovations for medical imaging," Health Care Management Science, Springer, vol. 21(1), pages 37-51, March.
    8. Marjolein A. M. Mulders & Monique M. J. Walenkamp & Nico L. Sosef & Frank Ouwehand & Romuald van Velde & J. Carel Goslings & Niels W. L. Schep, 2020. "The Amsterdam Wrist Rules: how much money can they save?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(5), pages 745-750, July.
    9. Mark Sculpher & Karl Claxton, 2012. "Real Economics Needs to Reflect Real Decisions," PharmacoEconomics, Springer, vol. 30(2), pages 133-136, February.
    10. Charles F. Manski, 2020. "Towards Reasonable Patient Care Under Uncertainty," Contemporary Economic Policy, Western Economic Association International, vol. 38(2), pages 227-245, April.
    11. 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.
    12. Charles E. Phelps, 1997. "Good Technologies Gone Bad," Medical Decision Making, , vol. 17(1), pages 107-117, February.
    13. Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
    14. Katherine Payne, 2009. "Fish and chips all round? Regulation of DNA‐based genetic diagnostics," Health Economics, John Wiley & Sons, Ltd., vol. 18(11), pages 1233-1236, November.
    15. Kämpfen, F.; & Gómez-Olivé, X.; & O’Donnell, O.; & Riumallo Herl, C.;, 2023. "Effectiveness of Population-Based Hypertension Screening: A Multidimensional Regression Discontinuity Design," Health, Econometrics and Data Group (HEDG) Working Papers 23/15, HEDG, c/o Department of Economics, University of York.
    16. 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.
    17. K. Drakopoulos & R. S. Randhawa, 2021. "Why Perfect Tests May Not Be Worth Waiting For: Information as a Commodity," Management Science, INFORMS, vol. 67(11), pages 6678-6693, November.
    18. 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.
    19. Charles F. Manski, 2016. "Credible Ecological Inference for Personalized Medicine: Formalizing Clinical Judgment," NBER Working Papers 22643, National Bureau of Economic Research, Inc.

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