HARNESSING ARTIFICIAL INTELLIGENCE FOR ENHANCING WATER, SANITATION, AND HYGIENE (WASH) SYSTEMS IN NIGERIAN HOSPITALS: PATHWAYS TO SAFER AND SMARTER HEALTHCARE DELIVERY
Keywords:
infection prevention and control, Nigeria, healthcare-associated infections, artificial intelligence , water sanitation hygieneAbstract
Background:
Water, sanitation, and hygiene (WASH) systems are critical for infection prevention and safe hospital environments. In Nigeria, persistent deficiencies in WASH infrastructure and practices contribute to healthcare-associated infections, maternal and neonatal morbidity, and poor patient outcomes. With healthcare increasingly adopting digital innovations, artificial intelligence (AI) offers opportunities to enhance WASH service delivery through real-time monitoring, predictive analytics, and automated feedback mechanisms. However, AI adoption in fragile health systems requires careful consideration of infrastructure limitations, ethical implications, and local contextual realities.
Materials and Methods:
A structured narrative literature search was conducted across major academic databases (PubMed, Google Scholar, Scopus) and grey literature sources (government reports, programme evaluations, policy documents). The search covered publications between 2019 and 2025, focusing on WASH systems in healthcare facilities, AI and IoT applications in healthcare and water quality monitoring, and infection prevention in low-resource settings. Due to the narrative design, formal quality appraisal was not applied; findings are synthesised thematically rather than quantitatively.
Findings:
Findings revealed widespread infrastructural and behavioural gaps in Nigerian hospital WASH systems, including unreliable water supply, poor sanitation infrastructure, and low hand hygiene compliance. Evidence from predominantly high-income and non-African settings supports AI-enabled solutions including real-time water quality monitoring, AI- driven hand hygiene surveillance, predictive maintenance of sanitation infrastructure, behavioural analytics, and centralised WASH dashboards. The generalisability of these findings to Nigeria requires cautious interpretation given significant differences in infrastructure and health system capacity.
Conclusion:
AI presents practical solutions to strengthen WASH systems in Nigerian hospitals by addressing infrastructural, behavioural, and managerial weaknesses. However, successful implementation will depend on supportive policies, ethical governance, sustained investment in infrastructure and human capacity, and rigorous local evaluation to establish real-world feasibility and cost-effectiveness within the Nigerian health system.
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