Artificial Intelligence-Enabled Microbiology in Supply Chain and Transport Management: Evidence from Nigerian Teaching Hospitals
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2026
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| _version_ | 1866901147174830080 |
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| author | Oboh, Christopher Ikhianosime Nwaononiwu, Ngozi Linda (Ph.D) Uchendu, Caroline Ifeoma (Ph.D) Yusuf, Hajara Oziohu (Ph.D) |
| author_facet | Oboh, Christopher Ikhianosime Nwaononiwu, Ngozi Linda (Ph.D) Uchendu, Caroline Ifeoma (Ph.D) Yusuf, Hajara Oziohu (Ph.D) |
| contents | <p><span>Artificial Intelligence (AI) has emerged as a transformative tool in microbiology, enabling predictive diagnostics and operational optimisation in healthcare supply chains. This study investigates the influence of AI-enabled microbiology on supply chain reliability, transport performance, cold-chain integrity, and safety compliance across six Nigerian teaching hospitals representing all geopolitical zones. Using a quantitative design, data were collected from 502 respondents, comprising laboratory scientists, microbiologists, and supply chain and transport personnel. Analyses employing descriptive statistics, correlation, and multiple regression revealed that AI adoption significantly enhances all measured outcomes (r = 0.64–0.77, p < 0.01), explaining 48–56% of the variance. AI’s impact was particularly pronounced in predictive pathogen detection, real-time transport monitoring, and enhanced regulatory compliance. Challenges such as fragmented infrastructure and uneven human capacity moderated effectiveness. The findings indicate that AI-integrated microbiology is a strategic enabler of resilient healthcare supply chains in Nigeria, with implications for policy and institutional investment.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19683332 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Artificial Intelligence-Enabled Microbiology in Supply Chain and Transport Management: Evidence from Nigerian Teaching Hospitals Oboh, Christopher Ikhianosime Nwaononiwu, Ngozi Linda (Ph.D) Uchendu, Caroline Ifeoma (Ph.D) Yusuf, Hajara Oziohu (Ph.D) <p><span>Artificial Intelligence (AI) has emerged as a transformative tool in microbiology, enabling predictive diagnostics and operational optimisation in healthcare supply chains. This study investigates the influence of AI-enabled microbiology on supply chain reliability, transport performance, cold-chain integrity, and safety compliance across six Nigerian teaching hospitals representing all geopolitical zones. Using a quantitative design, data were collected from 502 respondents, comprising laboratory scientists, microbiologists, and supply chain and transport personnel. Analyses employing descriptive statistics, correlation, and multiple regression revealed that AI adoption significantly enhances all measured outcomes (r = 0.64–0.77, p < 0.01), explaining 48–56% of the variance. AI’s impact was particularly pronounced in predictive pathogen detection, real-time transport monitoring, and enhanced regulatory compliance. Challenges such as fragmented infrastructure and uneven human capacity moderated effectiveness. The findings indicate that AI-integrated microbiology is a strategic enabler of resilient healthcare supply chains in Nigeria, with implications for policy and institutional investment.</span></p> |
| title | Artificial Intelligence-Enabled Microbiology in Supply Chain and Transport Management: Evidence from Nigerian Teaching Hospitals |
| url | https://doi.org/10.5281/zenodo.19683332 |