Open Access
QUANTITATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE IN HOSPITAL MANAGEMENT: SYSTEMATIC REVIEW OF REAL-WORLD IMPLEMENTATIONS AND OUTCOMES (2019–2024)
4
Doctoral School of the University of Burundi, Center for Research in Infrastructure, Environment and Technology (CRIET), Burundi
4
Department of Information and Communication Technologies, Higher Institute of Applied Sciences, University of Burundi, Burundi
4
Institute of Digital Technologies, University of Burundi, Burundi
4
EPAC, Ecole Polytechnique d’Abomey-Calavi; Université d’Abomey-Calavi, Cotonou-Bénin
Abstract
Hospitals around the world are under growing pressure due to limited resources, shifting demographics, and rising demands for quality care. Artificial intelligence (AI) has emerged as a promising ally to help address these challenges, yet real-world evidence about its implementation and impact remains scattered. This study, conducted following PRISMA 2020 guidelines, reviewed 52 empirical investigations published between 2019 and 2024 that reported quantitative outcomes of AI applications in hospital management. Our findings show that AI adoption rose from 66% in 2023 to 71% in 2024, although sharp disparities persist between university hospitals (87%) and rural facilities (41%). Meta-analyses revealed significant benefits: administrative efficiency improved by 30–45%, diagnostic accuracy by 12–18%, hospital stays shortened by 1.2–2.1 days, and resource allocation costs dropped by 15–25%. Despite initial investments ranging from $430,000 to $6.2 million, the average return on investment reached 267% within three years. However, implementation remains challenging—77% of projects faced technical integration issues, 71% reported inadequate staff training, and 56% struggled with regulatory compliance. Overall, while AI brings measurable and meaningful gains to hospital management, its success depends as much on human and organizational readiness as on technological capability. Bridging the equity gap between well-resourced and under-resourced institutions should be a policy priority, and future research must focus on long-term sustainability, standardized evaluation frameworks, and strategies adapted to resource-limited settings.
Keywords
Artificial Intelligence,
Hospital Management,
Healthcare Administration
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