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Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts.

Lyndal J Trevena1,2, Carissa Bonner1,2, Yasmina Okan3

  • 1Faculty of Medicine and Health, School of Public Health, The University of Sydney, Sydney, NSW, Australia.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
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Summary

Presenting complex numeric information in patient decision aids effectively requires using incremental absolute risk differences and well-designed visuals. Understanding graph literacy is key to enhancing comprehension and minimizing bias in health decisions.

Keywords:
decision aidsrisk communicationstandards

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Area of Science:

  • Health communication
  • Decision science
  • Medical informatics

Background:

  • Patient decision aids (PDAs) must present complex, task-specific numeric information clearly to minimize bias and promote understanding.
  • This article focuses on advanced, context-specific methods for conveying numerical data in PDAs, building upon fundamental principles.

Purpose of the Study:

  • To update evidence on presenting complex numeric information in patient decision aids.
  • To provide guidance on best practices for enhancing patient comprehension and minimizing bias.

Main Methods:

  • An international expert panel of 9 members revised and expanded topics from a 2013 review.
  • Evidence was updated through targeted literature searches and group work, followed by full panel consensus.

Main Results:

  • Strong evidence supports using independent event rates and incremental absolute risk differences for test/screening outcomes.
  • Well-designed visual formats can reduce judgment biases and improve comprehension, but effectiveness depends on graph literacy.
  • Limited evidence exists for personalized and interactive risk estimates due to complexity and feasibility concerns.

Conclusions:

  • Incremental absolute risk differences and appropriate visual formats, considering graph literacy, improve understanding of complex numeric information in PDAs.
  • Further research is needed on the optimal use of personalized/interactive risk estimates in clinical practice.