INFLUENCE OF AI-DRIVEN DIAGNOSTIC TOOLS ON HEALTHCARE PERFORMANCE IN LEVEL 6 HOSPITALS IN NAIROBI CITY COUNTY, KENYA
Keywords:
Artificial intelligence, diagnostic tools, healthcare performance,, Level 6 hospitals, Technology Acceptance Model, KenyaAbstract
Purpose of the Study: This study examined the influence of AI-driven diagnostic tools on healthcare performance in Level 6 hospitals in Nairobi City County, Kenya. The research addressed the growing need to improve diagnostic accuracy, reduce clinical inefficiencies, and enhance patient outcomes while filling a gap in empirical evidence on AI adoption in African public hospitals.
Methodology: The study was guided by the Technology Acceptance Model (TAM) and adopted a mixed-methods research design. From a target population of 719 healthcare professionals, a sample of 257 respondents was selected using stratified random sampling. Data were collected through questionnaires and interviews and analyzed using SPSS Version 27 and thematic analysis.
Findings: The study achieved a response rate of 97.5%. Findings revealed that AI-driven diagnostic tools had a positive and statistically significant influence on healthcare performance (β = 0.289, t = 3.506, p = 0.001). The regression model explained 57.7% of the variation in healthcare performance (R² = 0.577, F = 58.213, p < 0.001). Descriptive results indicated strong agreement among respondents that AI tools improved diagnostic accuracy (M = 4.03), reduced diagnostic errors (M = 3.99), shortened diagnosis time (M = 3.97), and enhanced patient outcomes (M = 3.85). These findings demonstrate the substantial contribution of AI technologies to improved healthcare service delivery.
Conclusion: The study concludes that AI-driven diagnostic tools significantly enhance healthcare performance in Kenya's Level 6 hospitals. Their adoption improves diagnostic precision, efficiency, and patient outcomes. The study recommends increased investment in digital infrastructure, continuous clinical training, and the development of a coordinated national framework to support AI implementation.
DOI: https://doi.org/10.5281/zenodo.20527431
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