IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v37y2017i2p139-147.html
   My bibliography  Save this article

Why Do Health Economists Promote Technology Adoption Rather Than the Search for Efficiency? A Proposal for a Change in Our Approach to Economic Evaluation in Health Care

Author

Listed:
  • Graham Scotland
  • Stirling Bryan

Abstract

At a time of intense pressure on health care budgets, the technology management challenge is for disinvestment in low-value technologies and reinvestment in higher value alternatives. The aim of this article is to explore ways in which health economists might begin to redress the observed imbalance between the evaluation of new and existing in-use technologies. The argument is not against evaluating new technologies but in favor of the “search for efficiency,†where the ultimate objective is to identify reallocations that improve population health in the face of resource scarcity. We explore why in-use technologies may be of low value and consider how economic evaluation analysts might embrace a broader efficiency lens, first through “technology management†(a process of analysis and evidence-informed decision making throughout a technology’s life cycle) and progressing through “pathway management†(the search for efficiency gains across entire clinical care pathways). A number of model-based examples are used to illustrate the approaches.

Suggested Citation

  • Graham Scotland & Stirling Bryan, 2017. "Why Do Health Economists Promote Technology Adoption Rather Than the Search for Efficiency? A Proposal for a Change in Our Approach to Economic Evaluation in Health Care," Medical Decision Making, , vol. 37(2), pages 139-147, February.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:2:p:139-147
    DOI: 10.1177/0272989X16653397
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X16653397
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X16653397?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sander Greenland, 2005. "Multiple‐bias modelling for analysis of observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 267-306, March.
    2. Christopher J.L. Murray & David B. Evans & Arnab Acharya & Rob M.P.M. Baltussen, 2000. "Development of WHO guidelines on generalized cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 9(3), pages 235-251, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. MacNeil, Maggie & Koch, Melissa & Kuspinar, Ayse & Juzwishin, Don & Lehoux, Pascale & Stolee, Paul, 2019. "Enabling health technology innovation in Canada: Barriers and facilitators in policy and regulatory processes," Health Policy, Elsevier, vol. 123(2), pages 203-214.
    2. Hofmann, Bjørn, 2020. "Biases distorting priority setting," Health Policy, Elsevier, vol. 124(1), pages 52-60.

    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. Lawrence C. McCandless & Sylvia Richardson & Nicky Best, 2012. "Adjustment for Missing Confounders Using External Validation Data and Propensity Scores," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 40-51, March.
    2. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    3. Michaël Schwarzinger & Jean‐Louis Lanoë & Erik Nord & Isabelle Durand‐Zaleski, 2004. "Lack of multiplicative transitivity in person trade‐off responses," Health Economics, John Wiley & Sons, Ltd., vol. 13(2), pages 171-181, February.
    4. Douglas E. Schaubel & Guanghui Wei, 2011. "Double Inverse-Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(1), pages 29-38, March.
    5. Carlos Díaz-Venegas, 2014. "Identifying the Confounders of Marginalization and Mortality in Mexico, 2003–2007," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 851-875, September.
    6. A. Goubar & A. E. Ades & D. De Angelis & C. A. McGarrigle & C. H. Mercer & P. A. Tookey & K. Fenton & O. N. Gill, 2008. "Estimates of human immunodeficiency virus prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 541-580, June.
    7. Siying Wang & Liubao Peng & Jianhe Li & Xiaohui Zeng & Lihui Ouyang & Chongqing Tan & Qiong Lu, 2013. "A Trial-Based Cost-Effectiveness Analysis of Erlotinib Alone versus Platinum-Based Doublet Chemotherapy as First-Line Therapy for Eastern Asian Nonsquamous Non–Small-Cell Lung Cancer," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-9, March.
    8. McCandless Lawrence C., 2012. "Meta-Analysis of Observational Studies with Unmeasured Confounders," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-31, January.
    9. Maria Gheorghe & Susan Picavet & Monique Verschuren & Werner B. F. Brouwer & Pieter H. M. Baal, 2017. "Health losses at the end of life: a Bayesian mixed beta regression approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 723-749, June.
    10. K. M. Rhodes & J. Savović & R. Elbers & H. E. Jones & J. P. T. Higgins & J. A. C. Sterne & N. J. Welton & R. M. Turner, 2020. "Adjusting trial results for biases in meta‐analysis: combining data‐based evidence on bias with detailed trial assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 193-209, January.
    11. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
    12. Rebecca M. Turner & David J. Spiegelhalter & Gordon C. S. Smith & Simon G. Thompson, 2009. "Bias modelling in evidence synthesis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 21-47, January.
    13. Quartey, Jonathan D. & Nyarko, Lydia Gyamea, 2022. "Economic Sustainability of Mobile Money Payments in Ghana: Does a Tax on Transactions Matter?," African Journal of Economic Review, African Journal of Economic Review, vol. 10(5), December.
    14. S. Dias & N. J. Welton & V. C. C. Marinho & G. Salanti & J. P. T. Higgins & A. E. Ades, 2010. "Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 613-629, July.
    15. Qi Zhou & Yoo-Mi Chin & James D. Stamey & Joon Jin Song, 2020. "Bayesian sensitivity analysis to unmeasured confounding for misclassified data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 577-596, December.
    16. Lai, Taavi & Habicht, Jarno & Reinap, Marge & Chisholm, Dan & Baltussen, Rob, 2007. "Costs, health effects and cost-effectiveness of alcohol and tobacco control strategies in Estonia," Health Policy, Elsevier, vol. 84(1), pages 75-88, November.
    17. de Luna, Xavier & Lundin, Mathias, 2009. "Sensitivity analysis of the unconfoundedness assumption in observational studies," Working Paper Series 2009:12, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    18. Leah Comment & Brent A. Coull & Corwin Zigler & Linda Valeri, 2022. "Bayesian data fusion: Probabilistic sensitivity analysis for unmeasured confounding using informative priors based on secondary data," Biometrics, The International Biometric Society, vol. 78(2), pages 730-741, June.
    19. N. J. Welton & A. E. Ades & J. B. Carlin & D. G. Altman & J. A. C. Sterne, 2009. "Models for potentially biased evidence in meta‐analysis using empirically based priors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 119-136, January.
    20. Baltussen, Rob, 2006. "Priority setting of public spending in developing countries: Do not try to do everything for everybody," Health Policy, Elsevier, vol. 78(2-3), pages 149-156, October.

    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:sae:medema:v:37:y:2017:i:2:p:139-147. 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: SAGE Publications (email available below). General contact details of provider: .

    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.