Patient Preferences in Diagnostic Imaging: A Scoping Review.

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.