Effect of Artificial Intelligence Adoption on Performance of Healthcare Sector in Kenya: A Case of Nairobi Public Hospitals

Abstract

Purpose: This study investigated the impact of Artificial Intelligence (AI) adoption on healthcare performance in public hospitals within Nairobi City County, Kenya. Despite AI's potential to transform healthcare through enhanced diagnostic accuracy, resource allocation, and operational efficiency, its adoption remained notably low in Nairobi's healthcare sector. Methodology: The research adopted a descriptive correlational design to examine relationships between AI adoption and healthcare performance metrics, focusing on three key areas: AI adoption rates, system integration, and disease prediction capabilities. The study targeted healthcare professionals, administrators, and technical staff across public hospitals in Nairobi, with a population of 210 respondents and a sample size of 138, determined using Yamane's formula. Data collection utilized structured questionnaires administered to physicians, nurses, IT specialists, administrators, and policymakers between 2020 and 2024, examining adoption trends particularly in light of the COVID-19 pandemic's acceleration of digital healthcare transformation. Findings: The findings revealed statistically significant positive relationships between all AI implementation dimensions and healthcare performance. Among them, AI utilization for disease prediction had the strongest impact explaining 44.6% of variance in performance followed closely by AI integration at 43.1%, and AI adoption rate contributed significantly at 41.7% of the variance. Healthcare performance metrics showed marked improvements, with service quality achieving the highest mean score (4.08), followed by treatment outcomes (3.98) and patient satisfaction (3.95). The study concludes that predictive AI applications provide the highest value for healthcare performance enhancement, while successful integration requires user-centered design and comprehensive technical support. Unique Contribution to Theory, Practice and Policy: Key recommendations include establishing mandatory AI training programs, developing robust data governance frameworks, implementing phased adoption strategies, and creating AI transparency initiatives to address staff trust issues and maximize the demonstrated performance improvements across all implementation dimensions.

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