Scholarlink

Background <p>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.</p> Methods <p>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.</p> Results <p>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, <i>p</i> &lt; 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.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Determinants of patient satisfaction in a primary healthcare facility in Bangladesh

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月27日

查看全文
  • Abdullah Al Habib,
  • Md Emran Hasan,
  • Firoj Al-Mamun,
  • Moneerah Mohammad ALmerab,
  • Mohammed A. Mamun,
  • Mohammad Tarikul Islam

摘要

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.