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Current Best Practice for Presenting Probabilities in Patient Decision Aids: Fundamental Principles

Author

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  • Carissa Bonner

    (Faculty of Medicine and Health, School of Public Health, The University of Sydney, Sydney, NSW, Australia
    ASK-GP NHMRC Centre of Research Excellence, The University of Sydney, Australia)

  • Lyndal J. Trevena

    (Faculty of Medicine and Health, School of Public Health, The University of Sydney, Sydney, NSW, Australia
    ASK-GP NHMRC Centre of Research Excellence, The University of Sydney, Australia)

  • Wolfgang Gaissmaier

    (University of Konstanz, Konstanz, Baden-Wurttemberg, Germany)

  • Paul K. J. Han

    (Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, Portland, ME, USA
    School of Medicine, Tufts University, USA)

  • Yasmina Okan

    (Centre for Decision Research, University of Leeds, Leeds, UK)

  • Elissa Ozanne

    (University of Utah, Salt Lake City, UT, USA)

  • Ellen Peters

    (Center for Science Communication Research, University of Oregon, Eugene, OR, USA)

  • Daniëlle Timmermans

    (Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, North Holland, The Netherlands)

  • Brian J. Zikmund-Fisher

    (University of Michigan, Ann Arbor, MI, USA)

Abstract

Background Shared decision making requires evidence to be conveyed to the patient in a way they can easily understand and compare. Patient decision aids facilitate this process. This article reviews the current evidence for how to present numerical probabilities within patient decision aids. Methods Following the 2013 review method, we assembled a group of 9 international experts on risk communication across Australia, Germany, the Netherlands, the United Kingdom, and the United States. We expanded the topics covered in the first review to reflect emerging areas of research. Groups of 2 to 3 authors reviewed the relevant literature based on their expertise and wrote each section before review by the full authorship team. Results Of 10 topics identified, we present 5 fundamental issues in this article. Although some topics resulted in clear guidance (presenting the chance an event will occur, addressing numerical skills), other topics (context/evaluative labels, conveying uncertainty, risk over time) continue to have evolving knowledge bases. We recommend presenting numbers over a set time period with a clear denominator, using consistent formats between outcomes and interventions to enable unbiased comparisons, and interpreting the numbers for the reader to meet the needs of varying numeracy. Discussion Understanding how different numerical formats can bias risk perception will help decision aid developers communicate risks in a balanced, comprehensible manner and avoid accidental “nudging†toward a particular option. Decisions between probability formats need to consider the available evidence and user skills. The review may be useful for other areas of science communication in which unbiased presentation of probabilities is important.

Suggested Citation

  • Carissa Bonner & Lyndal J. Trevena & Wolfgang Gaissmaier & Paul K. J. Han & Yasmina Okan & Elissa Ozanne & Ellen Peters & Daniëlle Timmermans & Brian J. Zikmund-Fisher, 2021. "Current Best Practice for Presenting Probabilities in Patient Decision Aids: Fundamental Principles," Medical Decision Making, , vol. 41(7), pages 821-833, October.
  • Handle: RePEc:sae:medema:v:41:y:2021:i:7:p:821-833
    DOI: 10.1177/0272989X21996328
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    References listed on IDEAS

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    2. Dawn Stacey & Robert J. Volk, 2021. "The International Patient Decision Aid Standards (IPDAS) Collaboration: Evidence Update 2.0," Medical Decision Making, , vol. 41(7), pages 729-733, October.
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    4. Andreas D. Meid & Lucas Wirbka, 2022. "Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants," Medical Decision Making, , vol. 42(5), pages 587-598, July.

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