Communication Accommodation and Patient Trust in Healthcare and Chronic Illness Care

Abstract

Communication accommodation in healthcare includes adapting explanations, listening, responding to concerns, and supporting patient participation. This quantitative study examined the association between perceived communication accommodation and patient trust among adults receiving chronic illness care. A cross-sectional survey design was used to assess whether patients who reported more responsive and understandable communication also reported greater confidence in their healthcare professionals. Adults aged 18 years or older with a diagnosed chronic condition who had received care from a healthcare professional were eligible. A structured questionnaire measured accommodation across clarity, responsiveness, listening, adaptation, emotional support, and opportunity to participate. Patient trust was assessed through perceived clinician competence, honesty, reliability, and concern for patient welfare. The analysis included descriptive statistics, scale reliability assessment, correlation, adjusted linear regression, and exploratory subgroup comparisons. Of 300 participants in the sample, 264 had complete data for the primary analysis. Mean accommodation was 3.92 (SD = 0.61) and mean trust was 3.88 (SD = 0.64) on five-point scales. Accommodation and trust were positively correlated (r = .58, p < .001). In the adjusted model, a one-point increase in accommodation was associated with a 0.48-point increase in trust (95% CI [0.37, 0.58], p < .001); the model explained 40% of trust-score variance (R² = .40). Subgroup estimates were positive across language, health literacy, chronic condition, treatment burden, visit frequency, and continuity categories, but interaction tests did not show statistically significant differences. The findings indicate that more favorable patient perceptions of communication accommodation were associated with greater trust. Because the design was cross-sectional, the association does not establish causality or show how trust changes across repeated consultations. These numerical findings are examples only, not results from an actual dataset, and should not be interpreted as empirical evidence until verified through data collection and analysis.