Expectation-perception gaps in healthcare service quality: A systematic review and meta-analysis toward service design-oriented healthcare evaluation.

Abstract

Background: The SERVQUAL model is widely used to evaluate healthcare service quality; however, evidence regarding expectation-perception gaps remains fragmented across healthcare settings. Existing studies have primarily interpreted these gaps as indicators of patient dissatisfaction, potentially overlooking broader challenges in healthcare service delivery. This study aimed to quantify SERVQUAL expectation-perception gaps across healthcare settings and explore their implications for healthcare quality evaluation from a service design perspective.

Methods: A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline. Electronic databases were searched from inception to January 2026. Studies applying the SERVQUAL model in healthcare settings and reporting quantitative expectation-perception data were eligible. Twenty-five studies involving 9,640 participants were included. Random-effects meta-analysis using restricted maximum likelihood (REML) estimation was performed to calculate pooled mean differences (MDs) after scores were harmonized to a common five-point scale. Methodological quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies.

Results: A statistically significant average negative expectation-perception gap was identified across healthcare settings (MD = -0.578; 95% confidence interval (CI), -0.781 to - 0.376; 95% prediction interval (PI), -1.662 to 0.505; p < 0.001). Significant average negative gaps were observed across all five SERVQUAL dimensions. The largest pooled gaps were identified for Assurance (MD = -0.628), Reliability (MD = -0.627), and Responsiveness (MD = -0.616), followed by Empathy (MD = -0.534) and Tangibles (MD = -0.489). Regional subgroup analyses demonstrated substantial geographic variation in pooled expectation-perception gaps. Although substantial between-study heterogeneity was observed (I² = 99.74%), leave-one-out sensitivity analyses indicated that the direction and statistical significance of the pooled estimate were not materially altered by omission of any individual study. Funnel plot asymmetry was detected; however, this pattern may reflect both small-study effects and substantial between-study heterogeneity.

Conclusions: An average negative SERVQUAL expectation-perception gap was observed across the included studies, with the largest pooled mean gaps occurring in Assurance, Reliability, and Responsiveness. However, these findings should be interpreted cautiously because of the extreme between-study heterogeneity, prediction intervals crossing the null, and the predominance of cross-sectional evidence. The observed gaps may reflect context-dependent service delivery misalignments involving communication, coordination, responsiveness, and patient journeys. A service design perspective may therefore complement conventional healthcare quality evaluation and support context-sensitive, patient-centred quality improvement.