Lyndal J Trevena1,2, Carissa Bonner1,2, Yasmina Okan3, Ellen Peters4, Wolfgang Gaissmaier5, Paul K J Han6,7, Elissa Ozanne8, Danielle Timmermans9, Brian J Zikmund-Fisher10. 1. Faculty of Medicine and Health, School of Public Health, The University of Sydney, Sydney, NSW, Australia. 2. Ask Share Know NHMRC Centre for Research Excellence, The University of Sydney, Australia. 3. Centre for Decision Research, University of Leeds, Leeds, UK. 4. University of Oregon, Eugene, OR, USA. 5. University of Konstanz, Konstanz, Baden-Wurttemberg, Germany. 6. Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, Portland, ME, USA. 7. School of Medicine, Tufts University, Medford, MA, USA. 8. University of Utah, Salt Lake City, UT, USA. 9. Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, North Holland, The Netherlands. 10. University of Michigan, Ann Arbor, MI, USA.
Abstract
BACKGROUND: Decision aid developers have to convey complex task-specific numeric information in a way that minimizes bias and promotes understanding of the options available within a particular decision. Whereas our companion paper summarizes fundamental issues, this article focuses on more complex, task-specific aspects of presenting numeric information in patient decision aids. METHODS: As part of the International Patient Decision Aids Standards third evidence update, we gathered an expert panel of 9 international experts who revised and expanded the topics covered in the 2013 review working in groups of 2 to 3 to update the evidence, based on their expertise and targeted searches of the literature. The full panel then reviewed and provided additional revisions, reaching consensus on the final version. RESULTS: Five of the 10 topics addressed more complex task-specific issues. We found strong evidence for using independent event rates and/or incremental absolute risk differences for the effect size of test and screening outcomes. Simple visual formats can help to reduce common judgment biases and enhance comprehension but can be misleading if not well designed. Graph literacy can moderate the effectiveness of visual formats and hence should be considered in tool design. There is less evidence supporting the inclusion of personalized and interactive risk estimates. DISCUSSION: More complex numeric information. such as the size of the benefits and harms for decision options, can be better understood by using incremental absolute risk differences alongside well-designed visual formats that consider the graph literacy of the intended audience. More research is needed into when and how to use personalized and/or interactive risk estimates because their complexity and accessibility may affect their feasibility in clinical practice.
BACKGROUND: Decision aid developers have to convey complex task-specific numeric information in a way that minimizes bias and promotes understanding of the options available within a particular decision. Whereas our companion paper summarizes fundamental issues, this article focuses on more complex, task-specific aspects of presenting numeric information in patient decision aids. METHODS: As part of the International Patient Decision Aids Standards third evidence update, we gathered an expert panel of 9 international experts who revised and expanded the topics covered in the 2013 review working in groups of 2 to 3 to update the evidence, based on their expertise and targeted searches of the literature. The full panel then reviewed and provided additional revisions, reaching consensus on the final version. RESULTS: Five of the 10 topics addressed more complex task-specific issues. We found strong evidence for using independent event rates and/or incremental absolute risk differences for the effect size of test and screening outcomes. Simple visual formats can help to reduce common judgment biases and enhance comprehension but can be misleading if not well designed. Graph literacy can moderate the effectiveness of visual formats and hence should be considered in tool design. There is less evidence supporting the inclusion of personalized and interactive risk estimates. DISCUSSION: More complex numeric information. such as the size of the benefits and harms for decision options, can be better understood by using incremental absolute risk differences alongside well-designed visual formats that consider the graph literacy of the intended audience. More research is needed into when and how to use personalized and/or interactive risk estimates because their complexity and accessibility may affect their feasibility in clinical practice.
Authors: Holly O Witteman; Ruth Ndjaboue; Gratianne Vaisson; Selma Chipenda Dansokho; Bob Arnold; John F P Bridges; Sandrine Comeau; Angela Fagerlin; Teresa Gavaruzzi; Melina Marcoux; Arwen Pieterse; Michael Pignone; Thierry Provencher; Charles Racine; Dean Regier; Charlotte Rochefort-Brihay; Praveen Thokala; Marieke Weernink; Douglas B White; Celia E Wills; Jesse Jansen Journal: Med Decis Making Date: 2021-10 Impact factor: 2.583
Authors: Inge S van Strien-Knippenberg; Marieke C S Boshuizen; Domino Determann; Jasmijn H de Boer; Olga C Damman Journal: Health Expect Date: 2022-05-17 Impact factor: 3.318
Authors: Ruben D Vromans; Saar Hommes; Felix J Clouth; Deborah N N Lo-Fo-Wong; Xander A A M Verbeek; Lonneke van de Poll-Franse; Steffen Pauws; Emiel Krahmer Journal: BMC Med Inform Decis Mak Date: 2022-10-05 Impact factor: 3.298