Individual Determinants of Data Analytics Adoption in Maternal Health Programs: A Study among Health Managers in Kericho and Vihiga Counties, Kenya
Résumé
Introduction: Data analytics has the potential to transform maternal health programming by enabling more informed decision-making, improving patient outcomes, and optimizing health system operations.Despite these benefits, the adoption of data analytics tools remains limited in many settings, including Kenya, which hampers efforts to reduce maternal mortality and enhance service delivery.Theoretical frameworks such as the Diffusion of Innovation (DOI) and the Technology Acceptance Model (TAM) shed light on the factors influencing technology adoption, including perceived usefulness, ease of use, relative advantage, and complexity.However, little is known about how individual determinants affect healthcare providers' willingness to adopt data analytics within maternal health programs.Therefore, this study aims to examine the personal characteristics that influence the adoption of data analytics for maternal programming in Kenya.Methods: This quantitative study used the Diffusion of Innovation and Technology Acceptance Models to explore factors influencing data analytics adoption in maternal health in Kenya.The research involved healthcare providers and managers from Kericho and Vihiga Counties, selected based on health coverage and maternal mortality rates.Data was collected through pre-tested questionnaires, with reliability ensured through pilot testing and statistical validation.Data analysis involved descriptive statistics and advanced tools like R and Power BI, with ethical approval obtained from relevant authorities and informed consent from participants.Findings: The findings show that individual demographic factors significantly influence the adoption of data analytics in maternal health programs.Education level, years of experience, age, gender, and professional cadre all showed notable differences in adoption rates, with higher education, greater experience, and senior roles being associated with increased engagement.Specifically, education and years of service had the strongest impact, while gender and age showed less consistent effects.Statistical analyses confirmed that more educated, experienced, and senior cadre health managers are more likely to adopt data analytics, highlighting the importance of targeted capacity-building and training across demographic groups to enhance data utilization in maternal health initiatives.
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