Zikmund-Fisher Brian J, Benda Natalie C, Ancker Jessica S
Department of Health Behavior and Health Equity, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Columbia University School of Nursing, New York, NY, USA.
Med Decis Making. 2025 Jul 7:272989X251346811. doi: 10.1177/0272989X251346811.
PurposeTo summarize the degree to which evidence from our recent Making Numbers Meaningful (MNM) systematic review of the effects of data presentation format on communication of health numbers supports recommendations from the 2021 International Patient Decision Aids Standards (IPDAS) Collaboration papers on presenting probabilities.MethodsThe MNM review generated 1,119 distinct findings (derived from 316 papers) related to communication of probabilities to patients or other lay audiences, classifying each finding by its relation to audience task, type of stimulus (data and data presentation format), and up to 10 distinct sets of outcomes: identification and/or recall, contrast, categorization, computation, probability perceptions and/or feelings, effectiveness perceptions and/or feelings, behavioral intentions or behavior, trust, preference, and discrimination. Here, we summarize the findings related to each of the 35 IPDAS paper recommendations.ResultsStrong evidence exists to support several IPDAS recommendations, including those related to the use of part-to-whole graphical formats (e.g., icon arrays) and avoidance of verbal probability terms, 1-in-X formats, and relative risk formats to prevent amplification of probability perceptions, effectiveness perceptions, and/or behavioral intentions as well as the use of consistent denominators to improve computation outcomes. However, the evidence base appears weaker and less complete for other IPDAS recommendations (e.g., recommendations regarding numerical estimates in context and evaluative labels). The IPDAS papers and the MNM review agree that both communication of uncertainty and use of interactive formats need further research.ConclusionsThe idea that no one visual or numerical format is optimal for every probability communication situation is both an IPDAS panel recommendation and foundational to the MNM project's design. Although no MNM evidence contradicts IPDAS recommendations, the evidence base needed to support many common probability communication recommendations remains incomplete.HighlightsThe Making Numbers Meaningful (MNM) systematic review of the literature on communicating health numbers provides mixed support for the recommendations of the 2021 International Patient Decision Aids Standards (IPDAS) evidence papers on presenting probabilities in patient decision aids.Both the IPDAS papers and the MNM project agree that no single visual or numerical format is optimal for every probability communication situation.The MNM review provides strong evidentiary support for IPDAS recommendations in favor of using part-to-whole graphical formats (e.g., icon arrays) and consistent denominators.The MNM review also supports the IPDAS cautions against verbal probability terms and 1-in-X formats as well as its concerns about the potential biasing effects of relative risk formats and framing.MNM evidence is weaker related to IPDAS recommendations about placing numerical estimates in context and use of evaluative labels.
目的
总结我们最近开展的“让数字有意义”（MNM）系统评价中关于数据呈现形式对健康数字沟通效果的证据，以支持2021年国际患者决策辅助工具标准（IPDAS）协作文件中有关概率呈现的建议。
方法
MNM评价产生了1119项与向患者或其他普通受众传达概率相关的不同研究结果（源自316篇论文），根据每个结果与受众任务、刺激类型（数据和数据呈现形式）以及多达10组不同结果的关系进行分类：识别和/或回忆、对比、分类、计算、概率认知和/或感受、有效性认知和/或感受、行为意图或行为、信任、偏好和歧视。在此，我们总结与IPDAS文件35条建议中每条建议相关的研究结果。
结果
有强有力的证据支持多项IPDAS建议，包括与使用部分与整体图形形式（如图标阵列）相关的建议，以及避免使用文字概率术语、X分之一形式和相对风险形式，以防止概率认知、有效性认知和/或行为意图的放大，以及使用一致的分母以改善计算结果。然而，对于其他IPDAS建议（如关于情境中数值估计和评估标签的建议），证据基础似乎更薄弱且不完整。IPDAS文件和MNM评价一致认为，不确定性沟通和交互式形式的使用都需要进一步研究。
结论
没有一种视觉或数字形式在每种概率沟通情境中都是最优的，这一观点既是IPDAS小组的建议，也是MNM项目设计的基础。虽然没有MNM证据与IPDAS建议相矛盾，但支持许多常见概率沟通建议所需的证据基础仍然不完整。
要点
“让数字有意义”（MNM）对健康数字沟通文献的系统评价为2021年国际患者决策辅助工具标准（IPDAS）证据文件中关于在患者决策辅助工具中呈现概率的建议提供了混合支持。
IPDAS文件和MNM项目都认为，没有一种单一的视觉或数字形式在每种概率沟通情境中都是最优的。
MNM评价为IPDAS支持使用部分与整体图形形式（如图标阵列）和一致分母的建议提供了强有力的证据支持。
MNM评价还支持IPDAS对文字概率术语和X分之一形式的警告，以及其对相对风险形式和框架潜在偏差效应的担忧。
与IPDAS关于将数值估计置于情境中以及使用评估标签的建议相关的MNM证据较弱。
