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Subcategorizing the Expected Value of Perfect Implementation to Identify When and Where to Invest in Implementation Initiatives

Author

Listed:
  • Kasper Johannesen

    (Department of Medical and Health Sciences, Linköping University, Linköping, Sweden)

  • Magnus Janzon

    (Department of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden)

  • Tomas Jernberg

    (Department of Clinical Sciences, Karolinska Institute, Danderyd University Hospital, Stockholm, Sweden)

  • Martin Henriksson

    (Department of Medical and Health Sciences, Linköping University, Linköping, Sweden)

Abstract

Purpose . Clinical practice variations and low implementation of effective and cost-effective health care technologies are a key challenge for health care systems and may lead to suboptimal treatment and health loss for patients. The purpose of this work was to subcategorize the expected value of perfect implementation (EVPIM) to enable estimation of the absolute and relative value of eliminating slow, low, and delayed implementation. Methods . Building on the EVPIM framework, this work defines EVPIM subcategories to estimate the expected value of eliminating slow, low, or delayed implementation. The work also shows how information on regional implementation patterns can be used to estimate the value of eliminating regional implementation variation. The application of this subcategorization is illustrated by a case study of the implementation of an antiplatelet therapy for the secondary prevention after myocardial infarction in Sweden. Incremental net benefit (INB) estimates are based on published cost-effectiveness assessments and a threshold of SEK 250,000 (£22,300) per quality-adjusted life year (QALY). Results . In the case study, slow, low, and delayed implementation was estimated to represent 22%, 34%, and 44% of the total population EVPIM (2941 QALYs or SEK 735 million), respectively. The value of eliminating implementation variation across health care regions was estimated to 39% of total EVPIM (1138 QALYs). Conclusion . Subcategorizing EVPIM estimates the absolute and relative value of eliminating different parts of suboptimal implementation. By doing so, this approach could help decision makers to identify which parts of suboptimal implementation are contributing most to total EVPIM and provide the basis for assessing the cost and benefit of implementation activities that may address these in future implementation of health care interventions.

Suggested Citation

  • Kasper Johannesen & Magnus Janzon & Tomas Jernberg & Martin Henriksson, 2020. "Subcategorizing the Expected Value of Perfect Implementation to Identify When and Where to Invest in Implementation Initiatives," Medical Decision Making, , vol. 40(3), pages 327-338, April.
    • Handle: RePEc:sae:medema:v:40:y:2020:i:3:p:327-338
    DOI: 10.1177/0272989X20907353
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    References listed on IDEAS

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    1. Andrew R. Willan & Simon Eckermann, 2010. "Optimal clinical trial design using value of information methods with imperfect implementation," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 549-561, May.
    2. Lazaros Andronis & Pelham M. Barton, 2016. "Adjusting Expected Value of Sample Information Using Realistic Expectations around Implementation," Medical Decision Making, , vol. 36(3), pages 284-284, April.
    3. Kasper M. Johannesen & Karl Claxton & Mark J. Sculpher & Allan J. Wailoo, 2018. "How to design the cost‐effectiveness appraisal process of new healthcare technologies to maximise population health: A conceptual framework," Health Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 41-54, February.
    4. 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.
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    Cited by:

    1. Naimi Johansson & Mikael Svensson, 2022. "Regional variation in prescription drug spending: Evidence from regional migrants in Sweden," Health Economics, John Wiley & Sons, Ltd., vol. 31(9), pages 1862-1877, September.

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