IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v19y2010i1p31-42.html
   My bibliography  Save this article

Investment in quality improvement: how to maximize the return

Author

Listed:
  • Afschin Gandjour

Abstract

Today, one of the most pressing concerns of health‐care policymakers in industrialized countries are deficits in the quality of health care. This paper presents a decision program that addresses the question in which disease areas and at what intensity to invest in quality improvement (QI) in order to maximize population health. The decision program considers both a budget constraint as well as time constraints of educators and health professionals to participate in educational activities. The calculations of the model are based on a single assumption which is that more intense quality efforts lead to larger QIs, but with diminishing returns. This assumption has been validated by previous studies. All other relationships described by the model are deduced from this assumption. The model uses data from QI trials published in the literature. Thus, it is able to assess how the vast number of published QI strategies compare in terms of their value. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Afschin Gandjour, 2010. "Investment in quality improvement: how to maximize the return," Health Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 31-42, January.
  • Handle: RePEc:wly:hlthec:v:19:y:2010:i:1:p:31-42
    DOI: 10.1002/hec.1449
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hec.1449
    Download Restriction: no

    File URL: https://libkey.io/10.1002/hec.1449?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. Afschin Gandjour & Karl Wilhelm Lauterbach, 2003. "When Is It Worth Introducing a Quality Improvement Program? A Mathematical Model," Medical Decision Making, , vol. 23(6), pages 518-525, November.
    2. M. D. Stevenson & J. Oakley & J. B. Chilcott, 2004. "Gaussian Process Modeling in Conjunction with Individual Patient Simulation Modeling: A Case Study Describing the Calculation of Cost-Effectiveness Ratios for the Treatment of Established Osteoporosis," Medical Decision Making, , vol. 24(1), pages 89-100, January.
    3. Afschin Gandjour & Karl Wilhelm Lauterbach, 2005. "How Much Does It Cost to Change the Behavior of Health Professionals? A Mathematical Model and an Application to Academic Detailing," Medical Decision Making, , vol. 25(3), pages 341-347, May.
    4. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    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. Rita Faria & Simon Walker & Sophie Whyte & Simon Dixon & Stephen Palmer & Mark Sculpher, 2017. "How to Invest in Getting Cost-effective Technologies into Practice? A Framework for Value of Implementation Analysis Applied to Novel Oral Anticoagulants," Medical Decision Making, , vol. 37(2), pages 148-161, February.
    2. Mili Mehrotra & Karthik V. Natarajan, 2020. "Value of Combining Patient and Provider Incentives in Humanitarian Health Care Service Programs," Production and Operations Management, Production and Operations Management Society, vol. 29(3), pages 571-594, March.
    3. Mehdi Ammi & Christine Peyron, 2016. "Heterogeneity in general practitioners’ preferences for quality improvement programs: a choice experiment and policy simulation in France," Health Economics Review, Springer, vol. 6(1), pages 1-11, December.

    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. Neil Hawkins & Mark Sculpher & David Epstein, 2005. "Cost-Effectiveness Analysis of Treatments for Chronic Disease: Using R to Incorporate Time Dependency of Treatment Response," Medical Decision Making, , vol. 25(5), pages 511-519, September.
    2. Mark Strong & Jeremy E. Oakley & Alan Brennan, 2014. "Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample," Medical Decision Making, , vol. 34(3), pages 311-326, April.
    3. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    4. Gandjour, Afschin & Stock, Stephanie, 2007. "A national hypertension treatment program in Germany and its estimated impact on costs, life expectancy, and cost-effectiveness," Health Policy, Elsevier, vol. 83(2-3), pages 257-267, October.
    5. Afschin Gandjour, 2010. "A model to predict the cost‐effectiveness of disease management programs," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 697-715, June.
    6. Peter Bacchetti & Charles E. McCulloch & Mark R. Segal, 2008. "Simple, Defensible Sample Sizes Based on Cost Efficiency," Biometrics, The International Biometric Society, vol. 64(2), pages 577-585, June.
    7. Rachael DiSantostefano & Andrea Biddle & John Lavelle, 2006. "The Long-Term Cost Effectiveness of Treatments for Benign Prostatic Hyperplasia," PharmacoEconomics, Springer, vol. 24(2), pages 171-191, February.
    8. Aditya Sai & Carolina Vivas-Valencia & Thomas F. Imperiale & Nan Kong, 2019. "Multiobjective Calibration of Disease Simulation Models Using Gaussian Processes," Medical Decision Making, , vol. 39(5), pages 540-552, July.
    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. Simon Eckermann & Tim Coelli, 2008. "Including quality attributes in a model of health care efficiency: A net benefit approach," CEPA Working Papers Series WP032008, School of Economics, University of Queensland, Australia.
    11. John Hutton, 2012. "‘Health Economics’ and the evolution of economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 13-18, January.
    12. 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.
    13. Sofia Dias & Alex J. Sutton & Nicky J. Welton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 6," Medical Decision Making, , vol. 33(5), pages 671-678, July.
    14. 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.
    15. Nicola J. Cooper & Keith R. Abrams & Alex J. Sutton & David Turner & Paul C. Lambert, 2003. "A Bayesian approach to Markov modelling in cost‐effectiveness analyses: application to taxane use in advanced breast cancer," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(3), pages 389-405, October.
    16. Eric Jutkowitz & Fernando Alarid-Escudero & Hyon K. Choi & Karen M. Kuntz & Hawre Jalal, 2017. "Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis," PharmacoEconomics, Springer, vol. 35(10), pages 1073-1085, October.
    17. Stefano Conti & Karl Claxton, 2008. "Dimensions of design space: a decision-theoretic approach to optimal research design," Working Papers 038cherp, Centre for Health Economics, University of York.
    18. Simon Eckermann & Andrew Briggs & Andrew R. Willan, 2008. "Health Technology Assessment in the Cost-Disutility Plane," Medical Decision Making, , vol. 28(2), pages 172-181, March.
    19. Doug Coyle & Jeremy Oakley, 2008. "Estimating the expected value of partial perfect information: a review of methods," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 9(3), pages 251-259, August.
    20. Karl Claxton & Elisabeth Fenwick & Mark J. Sculpher, 2012. "Decision-making with Uncertainty: The Value of Information," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 51, Edward Elgar Publishing.

    More about this item

    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:wly:hlthec:v:19:y:2010:i:1:p:31-42. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    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.