Patient Preferences in Diagnostic Imaging: A Scoping Review

Trey A. Baird, Davene R. Wright, Maria T. Britto, Ellen A. Lipstein, Andrew T. Trout and Shireen E. Hayatghaibi ()
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Trey A. Baird: University of Cincinnati, College of Medicine
Davene R. Wright: Harvard Medical School and Harvard Pilgrim Health Care Institute
Maria T. Britto: University of Cincinnati, College of Medicine
Ellen A. Lipstein: University of Cincinnati, College of Medicine
Andrew T. Trout: University of Cincinnati, College of Medicine
Shireen E. Hayatghaibi: University of Cincinnati, College of Medicine

The Patient: Patient-Centered Outcomes Research, 2023, vol. 16, issue 6, No 2, 579-591

Abstract: Abstract Background As new diagnostic imaging technologies are adopted, decisions surrounding diagnostic imaging become increasingly complex. As such, understanding patient preferences in imaging decision making is imperative. Objectives We aimed to review quantitative patient preference studies in imaging-related decision making, including characteristics of the literature and the quality of the evidence. Methods The Pubmed, Embase, EconLit, and CINAHL databases were searched to identify studies involving diagnostic imaging and quantitative patient preference measures from January 2000 to June 2022. Study characteristics that were extracted included the preference elicitation method, disease focus, and sample size. We employed the PREFS (Purpose, Respondents, Explanation, Findings, Significance) checklist as our quality assessment tool. Results A total of 54 articles were included. The following methods were used to elicit preferences: conjoint analysis/discrete choice experiment methods (n = 27), contingent valuation (n = 16), time trade-off (n = 4), best-worst scaling (n = 3), multicriteria decision analysis (n = 3), and a standard gamble approach (n = 1). Half of the studies were published after 2016 (52%, 28/54). The most common scenario (n = 39) for eliciting patient preferences was cancer screening. Computed tomography, the most frequently studied imaging modality, was included in 20 studies, and sample sizes ranged from 30 to 3469 participants (mean 552). The mean PREFS score was 3.5 (standard deviation 0.8) for the included studies. Conclusions This review highlights that a variety of quantitative preference methods are being used, as diagnostic imaging technologies continue to evolve. While the number of preference studies in diagnostic imaging has increased with time, most examine preventative care/screening, leaving a gap in knowledge regarding imaging for disease characterization and management.

Date: 2023
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DOI: 10.1007/s40271-023-00646-7

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