Measurement invariance and multigroup analysis of hospital infrastructure’s impact on patient satisfaction and health outcomes: evidence from a Thai public hospital

Purpose This study aims to assess how the current infrastructure of Songkhla City Hospital influences patient satisfaction and health outcomes. Rather than examining modernisation efforts, this research evaluates existing infrastructural conditions to identify which elements enhance or hinder healthcare experiences, thereby providing evidence-based recommendations for improvement. Design/methodology/approach Grounded in Donabedian’s structure–process–outcome model, the study uses a quantitative approach using survey data from 332 hospital visitors. Partial least squares structural equation modelling, multigroup analysis and measurement invariance of composite models were used to study the relationships among hospital infrastructure, perceived service quality, patient satisfaction and health outcomes. Findings Hospital infrastructure significantly influenced both patient satisfaction (β = 0.73, p < 0.001) and perceived service quality (β = 0.74, p < 0.001). Although the direct effect on health outcomes was marginal (β = 0.14, p = 0.08), indirect effects via satisfaction (β = 0.27, p < 0.001) and service quality (β = 0.22, p < 0.001) were substantial. The model demonstrated strong explanatory power (R² = 0.57 for health outcomes), with consistent results across gender groups. These findings underscore the importance of experiential mediators in translating infrastructural investments into improved patient-reported outcomes. Practical implications This study identifies key areas within the current infrastructure that require redesign or improvement to enhance service delivery and patient well-being. Insights can support hospital management and policymakers in prioritising targeted upgrades. Originality/value This research makes a significant contribution by empirically assessing the performance of existing hospital infrastructure and providing a structured framework for improvement in resource-constrained healthcare settings.