Values Clarification Methods in Decision Support Tools for Lung Cancer Screening: A Systematic Review and Content Analysis

Norah L. Crossnohere, Rosa Negash, Manny Schwimmer, Christiane Voisin, John F. P. Bridges and Daniel E. Jonas
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Norah L. Crossnohere: Division of General Internal Medicine, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA
Rosa Negash: Division of General Internal Medicine, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA
Manny Schwimmer: Division of General Internal Medicine, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA
Christiane Voisin: Division of General Internal Medicine, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA
John F. P. Bridges: Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
Daniel E. Jonas: Division of General Internal Medicine, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH, USA

Medical Decision Making, 2025, vol. 45, issue 7, 811-825

Abstract: Background Values clarification methods may be particularly appropriate for decision support in lung cancer screening (LCS), for which patients must consider a complex tradeoff of benefits and harms. Values clarification methods that are explicit and use theory-based methods may best support decision making. Purpose To characterize values clarification methods in decision support tools for LCS and explore associations with behavioral and decisional outcomes. Data Sources PubMed, Cochrane Library, CINAHL, APA PsycINFO, and Embase, supplemented with gray literature and hand searches. Study Selection Studies evaluating patient-facing LCS decision support tools. Data Extraction We extracted information on study characteristics and the decision support tools evaluated in each study, including method of values clarification (explicit, implicit, or none). Study quality was evaluated using an adapted version of the SUNDAE Checklist. Data Synthesis We identified 48 studies (10,233 participants) evaluating 32 unique decision support tools for LCS. More than 80% of tools included values clarification methods, split between explicit ( n = 13) and implicit ( n = 13) methods. Only 1 explicit values clarification used a theory-based method. Meta-analysis of randomized controlled trials indicated that using a decision support tool doubled the odds of receiving LCS (pooled odds ratio 1.98, 95% confidence interval 1.21–3.25, 9 studies), a pattern driven by increased uptake of screening following use of tools with explicit or no values clarification. Studies lacking values clarification were of lower quality than those with explicit or implicit methods ( P = 0.04). Limitations Almost no tools applied theory-based methods for explicit values clarification, limiting conclusions about their impact. Conclusions LCS decision support tools routinely incorporate values clarification methods and appear to enhance screening uptake. However, theory-based values clarification methods, which may further improve decision support quality, remain underutilized. Highlights Values clarification is a core aspect of shared decision making. It may be especially valuable for decision making regarding lung cancer screening (LCS), as patients must weigh a complex balance of benefits and harms. This systematic review identified 48 studies assessing 32 unique decision support tools for LCS. More than 80% of these tools incorporated values clarification methods, with an equal distribution of explicit and implicit methods. Among the subset of studies using a randomized controlled trial, the use of a decision support tool doubled the odds of an individual undergoing LCS. Decision support tools designed to support shared decision making in LCS commonly incorporate values clarification methods. However, they infrequently use theory-based methods, which are increasingly thought to provide high-quality decision support.

Keywords: patient preferences; shared decision making; lung neoplasms; early detection of cancer (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:7:p:811-825

DOI: 10.1177/0272989X251355906

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