BACKGROUND: Patient satisfaction is an important indicator of healthcare quality and system responsiveness, particularly in primary healthcare systems of low- and middle-income countries. In Bangladesh, empirical evidence on determinants of patient satisfaction in public primary healthcare facilities remains limited. This study examined factors associated with patient satisfaction in a public primary healthcare facility in Bangladesh and explored predictive patterns using machine-learning models to complement conventional analysis. METHODS: A cross-sectional study was conducted among 508 patients attending the Savar Upazila Health Complex between April and May 2025. Although conducted in a single upazila-level public healthcare facility, the setting reflects typical primary healthcare service delivery in Bangladesh. Patient satisfaction was assessed using the Patient Satisfaction Questionnaire Short Form (PSQ-18). Sociodemographic, patient-related, and facility-related variables were collected through structured interviews. Bivariate analyses and multivariable binary logistic regression were performed to identify determinants of higher patient satisfaction. In addition, six machine-learning models were applied to evaluate predictive performance and identify influential predictors. Models were tuned using five-fold stratified cross-validation optimizing ROC-AUC and evaluated on a held-out test set. RESULTS: The mean patient satisfaction score was 64.23 (SD = 8.86; score range: 18-90). Based on Bloom's cutoff criteria, 74.2% of respondents were classified as having moderate-good satisfaction (≥ 60% of the possible score range). Multivariable logistic regression identified several factors significantly associated with higher satisfaction, including transportation convenience (AOR = 2.54, 95% CI: 1.04-6.19), comfortable waiting-room seating (AOR = 2.18, 95% CI: 1.12-4.24), availability of healthy food (AOR = 2.48, 95% CI: 1.25-4.93), receipt of free medicines (AOR = 2.35, 95% CI: 1.18-4.69), dental-related conditions (AOR = 5.23, 95% CI: 1.13-24.26), and nursing staff behavior (overall association, p < 0.001). Among the machine-learning models, Random Forest demonstrated the strongest discrimination (ROC-AUC ≈ 0.82), while XGBoost achieved the highest classification balance (F1-score ≈ 0.87). Feature-importance analysis highlighted interpersonal care and facility-related factors as dominant predictors of patient satisfaction. CONCLUSION: Patient satisfaction in primary healthcare settings is influenced by accessibility, facility environment, and interpersonal care. Within the studies upazila-level context and similar primary healthcare settings in Bangladesh, improving transportation accessibility, waiting-area conditions, consistent availability of free medicines, and patient-centered communication among healthcare staff may substantially enhance patient experience and quality of care.
Habib et al. (Wed,) studied this question.