Authors : Ibrahim Lekan Jubril

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/y4up32uf

Scribd : https://tinyurl.com/5dyf9cha

DOI : https://doi.org/10.38124/ijisrt/26apr306

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Abstract : Objectives: To quantify domain-specific patient satisfaction across clinical and non-clinical service dimensions in a private specialist hospital in northern Nigeria; to identify service dimensions most strongly associated with overall satisfaction; and to segment the patient population by satisfaction profile in order to characterise groups whose experience is disproportionately shaped by non-clinical operational deficiencies.  Design: Quantitative cross-sectional study. Data were analysed using descriptive statistics, Pearson’s correlation, and silhouette-optimised K-means clustering.  Setting: Private specialist hospital, Kano, Kano State, Nigeria. Data collection period: January to June 2024.  Participants: 64 adult patients (response rate 85.3%) recruited via systematic random sampling from facility records. Eligibility required documented care attendance within the preceding four weeks and capacity to provide informed verbal consent.  Outcome Measures: Patient satisfaction across nine service domains, operationalised through a structured questionnaire (Cronbach’s α = 0.82), rated on a 20-point composite Likert scale. Primary domains included staff competence, empathy, communication clarity, treatment outcomes, administrative efficiency, and infrastructural adequacy.  Results: Clinical service domains returned consistently high satisfaction scores (staff competence: mean 17.89, SD 3.52; empathy: mean 18.12, SD 3.27; treatment outcomes: mean 17.91, SD 4.02). Non-clinical domains scored substantially lower (filing system efficiency: mean 12.81, SD 2.12; maintenance responsiveness: mean 12.50, SD 3.98). Staff professionalism was the strongest correlate of overall satisfaction (r = 0.78, p < 0.01). K-means clustering identified two patient segments: Cluster 1 (uniformly high satisfaction) and Cluster 2, whose overall experience was disproportionately depressed by deficiencies in administrative and infrastructural domains, despite clinical scores equivalent to Cluster 1.  Conclusions: The study identifies a structural pattern termed service visibility bias, in which clinical interactions receive disproportionate investment attention relative to operational systems, producing a pronounced satisfaction gap between clinical and non-clinical domains. Targeted investment in administrative efficiency and facility infrastructure represents the highest-leverage intervention for closing this gap. Multi-centre replication is required to establish the generalisability of these findings across the Nigerian private specialist hospital sector.

Keywords : Patient Satisfaction, Service Visibility Bias, Donabedian Framework, Private Specialist Hospital, K-Means Clustering, Healthcare Quality, Kano, Nigeria, LMIC Health Systems.

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