Grant Number:	5R01CA087595-02 Interpret this number
Primary Investigator:	Ubel, Peter
Organization:	University Of Michigan At Ann Arbor
Project Title:	Identify & Reduce Cognitive Biases of Decision AIDS
Fiscal Year:	2003

In recognition of the importance of patient values in medical decisions, researchers have begun developing decision aids that inform patients about the probability of various harms and benefits associated with available treatment alternatives. Decision aids have great potential to help patients make informed decisions. But they could also bias patients' decisions. Our overall objective is to identify cognitive biases that are likely to occur when patients use decision aids and test ways to reduce these biases. We have identified four cognitive biases that are especially relevant to the design of decision aids: Framing effects: People's treatment choices depend on whether treatment benefits are framed in terms of survival or mortality. Undue influence of patient testimonials: Patient testimonials regarding the desirability or undesirability of a treatment influence treatment choices independent of statistical information about treatment effectiveness. Preference reversal after addition of a third treatment option: A person who prefers treatment option A over B may prefer B after the addition of a third option. Undue influence of outcome information: The addition of information about treatment outcomes may unduly influence patients' choices. We will develop decision aid materials using written and computerized surveys administered to the general public, with one-third of the sample having cancer-related experience. Our goal is not to test decision aids on patients facing difficult clinical situations but, instead, to develop methods by which such decision aids can be produced - general principles for the creation of decision aids that will increase patients' understanding of their treatment choices without introducing cognitive bias.




Physician Recommendations Trump Patient Preferences in Prostate Cancer Treatment Decisions.
Authors: Scherr K.A. , Fagerlin A. , Hofer T. , Scherer L.D. , Holmes-Rovner M. , Williamson L.D. , Kahn V.C. , Montgomery J.S. , Greene K.L. , Zhang B. , et al. .
Source: Medical Decision Making : An International Journal Of The Society For Medical Decision Making, 2017 01; 37(1), p. 56-69.
EPub date: 2016-08-10 00:00:00.0.
PMID: 27510740
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Source: The Patient, 2016 Apr; 9(2), p. 149-59.
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Source: Health Affairs (project Hope), 2016 Apr; 35(4), p. 654-61.
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Source: Medical Decision Making : An International Journal Of The Society For Medical Decision Making, 2014 May; 34(4), p. 443-53.
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Source: Health, Risk & Society, 2014; 16(7-8), p. 649-666.
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Imagining Life With An Ostomy: Does A Video Intervention Improve Quality-of-life Predictions For A Medical Condition That May Elicit Disgust?
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Source: Patient Education And Counseling, 2013 Apr; 91(1), p. 113-9.
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In a survey, marked inconsistency in how oncologists judged value of high-cost cancer drugs in relation to gains in survival.
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Risk Estimates From An Online Risk Calculator Are More Believable And Recalled Better When Expressed As Integers
Authors: Witteman H.O. , Zikmund-Fisher B.J. , Waters E.A. , Gavaruzzi T. , Fagerlin A. .
Source: Journal Of Medical Internet Research, 2011; 13(3), p. e54.
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Risky feelings: why a 6% risk of cancer does not always feel like 6%.
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Images Of Illness: How Causal Claims And Racial Associations Influence Public Preferences Toward Diabetes Research Spending
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Testing whether decision aids introduce cognitive biases: results of a randomized trial.
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