Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers

BMC Medical Informatics and Decision Making

Review

Paul KJ Han¹  Adrian Edwards²  John King³  Suzanne K Linder⁴  Brian J Zikmund-Fisher⁵  Margaret L Lawson⁶  Ellen Peters⁷  Danielle Timmermans⁸  Elissa Ozanne⁹  Steven Woloshin^1,10  Isaac Lipkus^1,11  Mirta Galesic^1,12  Wolfgang Gaissmaier^1,13  Lyndal J Trevena^1,14 

[1] Center for Outcomes Research and Evaluation, Maine Medical Center Research Institute, 509 Forest Avenue, 04101, Portland, ME, USA;Cochrane Institute of Primary Care and Public Health, School of Medicine, Cardiff University, Neuadd Meirionnydd, CF14 4YS, Heath Park, Cardiff, UK;Department of Family Medicine, University of Vermont College of Medicine, University of Vermont, 235 Rowell, 106 Carrigan Drive, 05405, Burlington, Vermont, USA;Department of General Internal Medicine, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, 77030, Houston, TX, USA;Department of Health Behavior & Health Education, School of Public Health, Department of Internal Medicine, School of Medicine, and Center for Bioethics and Social Sciences in Medicine, University of Michigan, 1415 Washington Heights, 48109, Ann Arbor, MI, USA;Department of Pediatrics, Children’s Hospital of Eastern Ontario, University of Ottawa, 401 Smyth Road, K1H 8L1, Ottawa, Ontario, Canada;Department of Psychology, Ohio State University, 235 Psychology Building, 1835 Neil Avenue, 43210, Columbus, OH, USA;Department of Public and Occupational Health, EMGO Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands;Department of Surgery and Institute for Health Policy Studies, University of California, San Francisco, 3333 California St. Suite 265, 94143-0936, San Francisco, CA, USA;Departments of Medicine and of Community & Family Medicine and The Dartmouth Institute for Health Policy & Clinical Practice at the Geisel School of Medicine at Dartmouth and the VA Outcomes Group, VA Medical Center, 215 North Main Street, 05009-0001, White River Junction, VT, USA;Duke University School of Nursing, 307 Trent Drive, 27710, Durham, NC, USA;Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Lentzeallee 94, 14195, Berlin, Germany;Max Planck Institute for Human Development, Harding Center for Risk Literacy, Lentzeallee 94, 14195, Berlin, Germany;Primary Health Care, School of Public Health, University of Sydney, Room 321b, Edward Ford Building (A27), 2006, NSW, Australia;

关键词: Risk Perception;    Risk Communication;    Reference Class;    Aleatory Uncertainty;    Screening Decision;   

DOI  :  10.1186/1472-6947-13-S2-S7

来源: Springer

【 摘 要 】

BackgroundMaking evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients’ risk perception and leads to better informed decision making. This paper summarises current “best practices” in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools.MethodAn expert consensus group of fourteen researchers from North America, Europe, and Australasia identified eleven main issues in risk communication. Two experts for each issue wrote a “state of the art” summary of best evidence, drawing on the PtDA, health, psychological, and broader scientific literature. In addition, commonly used terms were defined and a set of guiding principles and key messages derived from the results.ResultsThe eleven key components of risk communication were: 1) Presenting the chance an event will occur; 2) Presenting changes in numeric outcomes; 3) Outcome estimates for test and screening decisions; 4) Numeric estimates in context and with evaluative labels; 5) Conveying uncertainty; 6) Visual formats; 7) Tailoring estimates; 8) Formats for understanding outcomes over time; 9) Narrative methods for conveying the chance of an event; 10) Important skills for understanding numerical estimates; and 11) Interactive web-based formats. Guiding principles from the evidence summaries advise that risk communication formats should reflect the task required of the user, should always define a relevant reference class (i.e., denominator) over time, should aim to use a consistent format throughout documents, should avoid “1 in x” formats and variable denominators, consider the magnitude of numbers used and the possibility of format bias, and should take into account the numeracy and graph literacy of the audience.ConclusionA substantial and rapidly expanding evidence base exists for risk communication. Developers of tools to facilitate evidence-based decision making should apply these principles to improve the quality of risk communication in practice.

【 授权许可】

CC BY   
© Trevena et al; licensee BioMed Central Ltd. 2013