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School of Medicine and Health Sciences

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    Determinants of Quality Healthcare Service Provision at Kenyatta National Hospital, Kenya
    (KeMU, 2025-10) Olinyo, Diana Chebet
    Quality healthcare service provision is a cornerstone for achieving Sustainable Development Goals (SDGs), a challenge particularly acute in national referral hospitals within resource-limited settings. Kenyatta National Hospital (KNH) in Kenya, as a pivotal healthcare institution, faces persistent challenges in delivering consistent, high-quality care. This study aimed to identify and analyze the key determinants influencing the provision of quality healthcare at KNH, specifically focusing on human resources, hospital infrastructure, system optimization, and health financing. A descriptive cross-sectional research design was employed for this investigation. The study targeted a population of 5,779 healthcare workers within the Surgical Services Division of KNH. A stratified random sampling technique was used to select a sample of 374 respondents, from which 330 completed questionnaires were collected, yielding a high response rate of 88.2%. Data collection was conducted using pre-tested, self-administered questionnaires. The collected data underwent rigorous analysis, utilizing descriptive statistics to summarize the data and inferential statistics, including Pearson’s correlation and binary logistic regression, to examine relationships and predictive capacities between the independent variables and the dependent variable. The reliability of the research instrument was confirmed with a Cronbach's Alpha score of 0.811. Descriptive statistics revealed that all study variables had mean scores above 3.0, indicating a general consensus among respondents on their importance. Inferential analysis demonstrated significant positive correlations between hospital infrastructure (r=0.445, p<0.01), system optimization (r=0.306, p<0.01), and the provision of quality healthcare. The logistic regression model further identified hospital infrastructure (β=0.593) as the most potent predictor of quality care, followed by system optimization, human resources, and health financing. The overall model was statistically significant and explained 66.8% of the variance in quality healthcare provision (Cox & Snell R² = 0.668). The findings underscore the paramount importance of infrastructure investment and operational process efficiency as foundational to enhancing healthcare quality. The study concludes that a multi-pronged strategy is essential for sustainable quality improvement at KNH. Consequently, it is recommended that hospital management and policymakers prioritize strategic investment in infrastructure upgrades, enhance staff training and retention programs and foster a culture of data-driven decision-making to bridge existing service delivery gaps and achieve superior patient outcomes.
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    Factors Influencing Inter-Professional Collaboration Among Healthcare Workers in Primary Health Care Facilities. A Case of Nakuru County Kenya
    (KeMU, 2020-11) Koech, Reuben Cherwon
    Professionals from varying disciplines work collaboratively to serve patients. Although inter-professional collaboration is essential, existing barriers can prohibit inter-professional teams from working together effectively and efficiently. Inter-professional collaborative education and practice can prepare health workers to work on inter-professional teams by educating them about key concepts related to inter-professional collaboration. Therefore, the study sought to establish factors influencing inter-professional collaboration among the healthcare workers in primary healthcare facilities in Nakuru County. The specific objectives were to establish patient-related, professional-related, interpersonal and organizational factors influencing inter-professional collaboration in Nakuru County, Kenya. The study employed a Cross Sectional Survey Research Design and Self-Administered Questionnaire to collect data from 146 healthcare workers. Purposive sampling, Stratified sampling and Simple random sampling techniques was used to sample the Sub-Counties, Primary healthcare facilities and respondents respectively. Data was analyzed using SPSS and relationships between variables were tested using correlation analysis and multiple regression. The study established that Patient-Related Factors (β = 0.263, p = .006