Thesis:

Determinants of data driven decision making among health providers in Mombasa county

The utilization of data by healthcare providers remains deficient, particularly concerning routinely collected data. Utilizing health information (HI) for decision- making by healthcare managers in the country, Kenya lacks incentives or motivations to cultivate an information improvement culture. The regulations within the HMIS are lacking, and there is limited awareness concerning practicality of HIS data in decision- making. The primary objective was to explore the variables shaping data-driven decision-making among Mombasa County public health facilities’ health providers. Specifically, the study investigated the effect of technical, organizational, and behavioral factors on data-driven decision-making among these health providers. The theoretical frameworks guiding the study were the socio-technical theory, technology acceptance model, and resource-based view theory. The study employed an analytical cross-sectional survey design, targeting 303 healthcare staff in Mombasa County. The sample size consisted of 172 respondents, recruited via Taro Yamane formula, alongside stratified random sampling technique. Primary data were gathered through a Likert-scale questionnaire and SPSS version 25 helped in analysis. The researcher used Pearson correlation analysis, descriptive statistics, and multinomial logistic regression to predict influence of technical, behavioral, organizational factors, and government policies on data-driven decision-making (with a significance level of 0.05). The coefficient of Pearson correlation exhibited a positive and significant association between technical factors and data-led decision-making. Similarly, a strong positive correlation existed between behavioral factors and data-driven decision-making. Organizational factors also exhibited a positive and significant relation with data-driven decision-making. Based on a significance level of p=0.05, the likelihood ratio tests demonstrated that both technical and organizational factors significantly predicted data-driven decision-making among health providers, whereas behavioral factors did not have a statistically significant impact. The researcher's recommendations include providing training for health workers at the county level to enhance data utilization skills, ensuring thorough data verification before submission, promoting the use of HI in decision-making, addressing perceptions and attitudes toward health information system use, establishing feedback mechanisms for data utilization, and allocating sufficient resources for supportive supervision of data systems.