Supporting Health and Medical Decision Making: Findings and Insights from Fuzzy-Trace Theory

Valerie F. Reyna, Sarah Edelson, Bridget Hayes and David Garavito
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Valerie F. Reyna: Human Neuroscience Institute and Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, USA
Sarah Edelson: Human Neuroscience Institute and Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, USA
Bridget Hayes: Human Neuroscience Institute and Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, USA
David Garavito: Human Neuroscience Institute and Center for Behavioral Economics and Decision Research, Cornell University, Ithaca, NY, USA

Medical Decision Making, 2022, vol. 42, issue 6, 741-754

Abstract: Theory—understanding mental processes that drive decisions—is important to help patients and providers make decisions that reflect medical advances and personal values. Building on a 2008 review, we summarize current tenets of fuzzy-trace theory (FTT) in light of new evidence that provides insight regarding mental representations of options and how such representations connect to values and evoke emotions. We discuss implications for communicating risks, preventing risky behaviors, discouraging misinformation, and choosing appropriate treatments. Findings suggest that simple, fuzzy but meaningful gist representations of information often determine decisions. Within minutes of conversing with their doctor, reading a health-related web post, or processing other health information, patients rely on gist memories of that information rather than verbatim details. This fuzzy-processing preference explains puzzles and paradoxes in how patients (and sometimes providers) think about probabilities (e.g., “50-50†chance), outcomes of treatment (e.g., with antibiotics), experiences of pain, end-of-life decisions, memories for medication instructions, symptoms of concussion, and transmission of viruses (e.g., in AIDS and COVID-19). As examples, participation in clinical trials or seeking treatments with low probabilities of success (e.g., with antibiotics or at the end of life) may indicate a defensibly different categorical gist perspective on risk as opposed to simply misunderstanding probabilities or failing to make prescribed tradeoffs. Thus, FTT explains why people avoid precise tradeoffs despite computing them. Facilitating gist representations of information offers an alternative approach that goes beyond providing uninterpreted “neutral†facts versus persuading or shifting the balance between fast versus slow thinking (or emotion vs. cognition). In contrast to either taking mental shortcuts or deliberating about details, gist processing facilitates application of advanced knowledge and deeply held values to choices. Highlights Fuzzy-trace theory (FTT) supports practical approaches to improving health and medicine. FTT differs in important respects from other theories of decision making, which has implications for how to help patients, providers, and health communicators. Gist mental representations emphasize categorical distinctions, reflect understanding in context, and help cue values relevant to health and patient care. Understanding the science behind theory is crucial for evidence-based medicine.

Keywords: decision theory; fuzzy-trace theory; health education; provider-patient communication; risk communication; risk perception; shared decision making (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:42:y:2022:i:6:p:741-754

DOI: 10.1177/0272989X221105473

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