AI-Based Scheduling and Performance of Tertiary Hospitals in Onitsha, Anambra State
DOI:
https://doi.org/10.58471/jds.v3i2.7389Keywords:
Artificial Intelligence, Scheduling Systems, Hospital Performance, Technology Adoption, Healthcare Delivery.Abstract
The study employed a descriptive survey research design to examine the impact of AI-based scheduling on hospital performance in Onitsha, focusing on Federal Medical Center Onitsha and Guinness Eye Clinic Onitsha. A stratified random sampling technique was used to select 60 respondents from doctors, nurses, administrative staff, patients, and security personnel. Data were collected using a four-cluster structured questionnaire rated on a four-point Likert scale. The instrument's validity was ensured through expert review, while reliability yielded Cronbach’s alpha values ranging from 0.79 to 0.86. Data collection lasted two weeks, and analysis involved descriptive statistics and ANOVA at a 0.05 significance level. This research found that there was a poor average rate of adoption of AI-based scheduling systems in tertiary hospitals in Onitsha (domain mean: 2.05-2.82 on a scale of 5). Mean of staff training was the highest (2.82) and replacement of manual systems with full replacement of manual systems showed the lowest (2.05). With respect to effect on service delivery, mean values ranged between 2.17 to 2.57 indicating little perceived effects. Some of the top challenges experienced by the participants included, data privacy (3.58), resistance to change (3.20), technical expertise (3.10). Nevertheless, solutions such as government funding (3.68) and staff training (3.50) had high levels of support, the means to increase AI adoption practically in a wide variety of ways. Finally, although the modern implementation and perceived efficiency of the AI-based scheduling of tertiary hospitals in Onitsha are minor, widespread awareness of its positive aspect has been realized. The ability to address highlighted barriers using specific policy interventions and institutional assistance may notably improve performance and efficiency in hospitals.
References
Adedinsewo, D.A., Onietan, D., Morales-Lara, A.C., Moideen Sheriff, S., Afolabi, B.B., Kushimo, O.A., Mbakwem, A.C., Ibiyemi, K.F., Ogunmodede, J.A., Raji, H.O. and Ringim, S.H. (2025). Contextual challenges in implementing artificial intelligence for healthcare in low-resource environments: insights from the SPEC-AI Nigeria trial. Frontiers in Cardiovascular Medicine, 12, 1516088.
Dai, J., Xu, H., Chen, T., Huang, T., Liang, W., Zhang, R., Xu, G., Zhang, Z., Xue, L., Gao, Y. and Zheng, M. (2025). Artificial intelligence for medicine 2025: Navigating the endless frontier. The Innovation Medicine, 3(1), 100120-1.
Fahlevi, M., Aljuaid, M., & Saniuk, S. (2022). Leadership style and hospital performance: empirical evidence from Indonesia. Frontiers in psychology, 13, 911640.
Iheme, O. C. (2023). Artificial intelligence-based medical device technologies implementation strategies in the Nigerian health care industry (Doctoral dissertation, Walden University).
Kumar, R., Singh, A., Kassar, A. S. A., Humaida, M. I., Joshi, S., & Sharma, M. (2025). Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors. Computer Modeling in Engineering & Sciences (CMES), 142(2).
Morah, D. N., & Brown, G. N. B. (2024). Knowledge and Adoption of Artificial Intelligence for Effective Communication by Healthcare Providers in Nigerian Teaching Hospitals. Pakistan Journal of Media Sciences, 5(1), 1-17.
Nwaomah, A. E. (2025). Utilization of Artificial Intelligence (AI) and Fear of Job Loss in the Banking Industry: Perspectives of Bank Employees. Pan-African Journal of Education and Social Sciences, 6(1), 88-103.
Ogolodom, M.P., Ochong, A.D., Egop, E.B., Jeremiah, C.U., Madume, A.K., Nyenke, C.U., Dambele, M.Y., Joseph, D.Z., Bakre, A.F.K., Balogun, E.O. and Alazigha, N., (2023). Knowledge and perception of healthcare workers towards the adoption of artificial intelligence in healthcare service delivery in Nigeria. AG Salud, 1, 16-16.
Oladipo, E.K., Adeyemo, S.F., Oluwasanya, G.J., Oyinloye, O.R., Oyeyiola, O.H., Akinrinmade, I.D., Elutade, O.A., Areo, D.O., Hamzat, I.O., Olakanmi, O.D. and Ayanronbi, I.I., 2024. Impact and challenges of artificial intelligence integration in the African health sector: a review. Trends Med Res, 19(1), pp.220-235.
Sarfo, J. O., Tachie-Donkor, G., Aggrey, E. K., & Mordi, P. (2024). Attitudes, Perceptions, and Challenges Towards Artificial Intelligence Adoption in Ghana and Nigeria: A Systematic Review with a Narrative Synthesis Attitudes, Perceptions, and Challenges Towards Artificial Intelligence. International Journal of Media and Information Literacy, 9(2), 437-452.
Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government information quarterly, 36(2), 368-383.
Wang, K., Zhao, Y., Gangadhari, R. K., & Li, Z. (2021). Analyzing the adoption challenges of the Internet of things (Iot) and artificial intelligence (ai) for smart cities in china. Sustainability, 13(19), 10983.
Xie, Y., Liang, D., Huang, J., & Jin, J. (2019). Hospital ownership and hospital institutional change: a qualitative study in Guizhou Province, China. International journal of environmental research and public health, 16(8), 1460.
Ogunbodede, K. F., & Atchrim, I. A. (2025). Artificial Intelligence in Nigeria: A Catalyst for Economic Growth, Innovation, and Technological Advancement. Faculty of Natural and Applied Sciences Journal of Computing and Applications, 2(2), 111-116.
Vaikosen, B., & Magini, G. U. (2025). Assessing the Impact of Artificial Intelligence on Productivity and Global Market Competitiveness: A Case Study of Nigeria. Faculty of Natural and Applied Sciences Journal of Computing and Applications, 2(2), 85-92.
Khaledian, N., Voelp, M., Azizi, S., & Shirvani, M. H. (2024). AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review. Cluster Computing, 27(8), 10265-10298.
Lua, C., Onwuchekwa, D., & Obermaisser, R. (2022, June). Ai-based scheduling for adaptive time-triggered networks. In 2022 11th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-7). IEEE.
Agbeyangi, A. O., & Lukose, J. M. (2025, March). Telemedicine adoption and prospects in sub-Sahara Africa: a systematic review with a focus on South Africa, Kenya, and Nigeria. In Healthcare (Vol. 13, No. 7, p. 762). MDPI.
IFEANYICHUKWU CHINYERE IFEYINWA (FIPMA) is a senior staff in Nwafor Orizu College of Education Nsugbe. She hails from Obinagu Nsukwu Abatete in Idemili North LGA Anambra, She’s the first daughter of Late Felix C. (Ezigbalike) & Mrs. Felicia Nwolisa. She’s to happily married to Amb Evang. Ifeanyichukwu Okoye who is from Ukpo in Dunukofia LGA Anambra State. Their Marriage is blessed with 5 children.
She holds her OND in Business Studies and HND in Business Administration and Management from Federal Polytechnic, OKO.
PGD in public Administration from Imo State University, Owerri.
PGDE from National Teacher’s Institute Kaduna affiliated to Nnamdi Azikiwe University, Awka.
M.Sc. from Enugu State University, Enugu State. She is a fellow member Institute of professional Managers and Administrators of Nigeria, IPMA. She has the ASCON (The Administrative Staff College Of Nigeria)
