Back

Determinants of Patient Satisfaction in a Bangladeshi Public Hospital Outpatient Department: A Cross-Sectional Study

Karim, M. R.; Akhter, S.; Zannat, T.; Sajid, T.

2025-08-05 health systems and quality improvement
10.1101/2025.08.01.25332704 medRxiv
Show abstract

BackgroundPatient satisfaction is critical for healthcare utilization in resource-constrained settings like Bangladesh, yet evidence on outpatient satisfaction determinants remains limited. This study assessed perceived service quality and identified predictors of satisfaction in a public medical college hospital. MethodsA cross-sectional study (March-May 2025) recruited 1,089 adult outpatients via systematic random sampling. Data were collected using validated Bengali instruments measuring perceived quality (24 items) and satisfaction (14 items). Binary logistic regression was used to identify significant predictors (p < 0.05). The model was validated using the area under the curve (AUC), pseudo-R{superscript 2}, and classification accuracy. Results were reported as odds ratios with 95% confidence intervals. Data entry, editing, and analysis were performed using SPSS and Jamovi software. ResultsParticipants (mean age 34{+/-}15.64 years; 51.5% male; 63.0% rural) reported positive physician interactions (e.g., 56.7% felt respected). Critical deficiencies included poor toilet cleanliness (75.5%), inadequate drinking water (67.6%), and medication shortages (30.6%). Female patients reported insufficient consultation time (17.5%). Regression analysis (AUC=0.92) revealed higher satisfaction among males (AOR=1.79, 95% CI: 1.02-3.16), suburban residents (vs. rural; AOR=2.01, 1.06-3.81), businesspersons (vs. students; AOR=2.89, 1.19-7.04), and those with prior positive experiences (AOR=1.50, 1.42-1.58). Upper-middle-class patients had lower satisfaction (AOR=0.40, 0.20-0.79). Positive perceptions of management and administration independently increased satisfaction (AOR=1.14 for both). ConclusionPatient satisfaction in a Bangladeshi public hospital outpatient department is influenced by multifaceted determinants: demographics, service experiences, and perceived quality. Key predictors of higher satisfaction included male gender, suburban residence, business occupation, positive prior experiences, and perceived administrative quality. Conversely, upper-middle-class status and female gender predicted lower satisfaction, indicating equity concerns. These findings necessitate urgent, targeted improvements in non-clinical areas (administration, resource logistics, facility environment) and gender-sensitive, equity-focused interventions to enhance satisfaction, trust, and service utilization. What is already knownPatient satisfaction in LMIC public health settings is shaped by clinical interactions (e.g., provider competence) and non-clinical factors (e.g., waiting times, sanitation). Gender and socioeconomic disparities persist, but evidence rarely controls for prior experiences, risking confounding. What this study addsIn 1,089 Bangladeshi OPD patients: O_LIQuantified disparities: Males had 79% higher satisfaction than females (AOR=1.79); upper-middle-class patients had 60% lower satisfaction than lower-class peers (AOR=0.40). C_LIO_LIIsolated current drivers: Controlling for prior satisfaction, management ({rho}=0.28), and administration ({rho}=0.22) independently predicted satisfaction. C_LIO_LIExposed systemic gaps: 75.5% reported poor sanitation; 17.5% of women received insufficient consultation time. C_LI ImplicationsO_LIResearch: Control for prior satisfaction in patient experience studies. C_LIO_LIPractice: Prioritize gender-transformative training and sanitation investments. C_LIO_LIPolicy: Integrate patient feedback systems and equity standards for rural access C_LI

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.