Patient preferences in shared decision-making for cardiometabolic care: A scoping review of classification, measurement, outcomes, and mechanisms.

Abstract

This review aimed to map patient preferences within shared decision-making (SDM) for cardiometabolic diseases by systematically examining their classification, measurement, outcomes, and influencing mechanisms. PubMed, Web of Science, and Scopus were searched for studies on patient preferences in cardiometabolic care. Two researchers independently screened studies and extracted data from eligible studies. A narrative synthesis and thematic analysis were conducted to classify preference types and synthesize their definitions, measurement methods, associated outcomes, and influencing mechanisms. Fifty-one studies were included. Drawing on two complementary theoretical frameworks, patient preferences were classified into four types aligned with the SDM process: decision role, decision goal, treatment alternative, and treatment implementation preferences. Measurement methods varied widely, from validated scales to discrete choice experiments. Preferences were associated with outcomes across four domains: health outcomes, decision outcomes, behavioral adherence, and economic and resource utilization. Potential mechanisms linking preferences to outcomes were synthesized and proposed at patient, clinician, and clinician-patient interaction (technological) levels. Patient preferences are not a single construct but multiple considerations spanning the SDM process, associated with outcomes and potentially linked to them through mechanisms at patient, clinician, and clinician-patient interaction levels. Future research should prioritize diverse populations, longitudinal designs, mechanism testing, and validated measures. Clinicians may consider eliciting and addressing distinct preference domains across the stages of SDM and checking whether agreed care plans reflect patients' priorities and practical circumstances. Because most preference instruments served research purposes, the added clinical benefits of routine structured preference assessment require further evaluation, and AI-based preference tools remain a future research direction rather than a current practice recommendation.

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