Assessment of Perception and Utilization of Artificial Intelligence Tools Among Health Information Management Practioners in Tertiary Hospitals in Bayelsa State
Abstract:
Artificial Intelligence (AI) is increasingly transforming healthcare through automated data processing, intelligent information management, predictive analytics and decision support. However, effective integration of AI into Health Information Management (HIM) depends largely on healthcare workers’ awareness, perceptions, competencies and actual utilization of AI tools. This study assessed the perception and utilization of AI tools among healthcare workers in tertiary hospitals in Bayelsa State, Nigeria. A descriptive cross-sectional research design was adopted. The study was conducted among 112 healthcare workers in selected tertiary hospitals in Bayelsa State. Data were collected using a structured self-administered questionnaire and analysed using the Statistical Package for the Social Sciences (SPSS) Version 27. Descriptive statistics comprising frequency, percentage, mean and standard deviation were used, while inferential statistics, including independent samples t-test and Pearson Product Moment Correlation, were employed at the 0.05 level of significance. The findings revealed a low level of awareness of AI tools among respondents, with a grand mean of 2.43. Perception of the usefulness of AI tools in HIM practices were also low (x̄ = 2.48), indicating limited confidence in their overall usefulness. However, the level of AI utilization was moderate (x̄ = 2.52), suggesting that AI tools were being used to some extent but had not been fully integrated into routine HIM practices. Significant differences were found between healthcare workers in Federal and State hospitals in terms of awareness, perception, utilization and challenges, with Federal healthcare workers generally reporting more favourable outcomes. Furthermore, a strong positive and statistically significant relationship was found between perception of AI tools and utilization (r = 0.62, p < 0.001), resulting in the rejection of the null hypothesis. The study concluded that AI adoption in HIM practices in tertiary hospitals in Bayelsa State remained suboptimal due to gaps in awareness, skills, training, institutional support and infrastructure. It recommended sustained AI training and capacity building, improved digital infrastructure, institutional support, clear governance frameworks and interventions aimed at improving healthcare workers’ confidence and positive perception of AI.
KeyWords:
Artificial Intelligence, Perception, Utilization, Health Information Management, Health Information Management Practitioners, AI Tools, Tertiary Hospitals, Healthcare Technology
References:
- Adigwe, O. P., Onavbavba, G., & Sanyaolu, S. E. (2024). Exploring the matrix: Knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare. Frontiers in Artificial Intelligence, 6, 1293297. https://doi.org/10.3389/frai.2023.1293297
- Ayorinde, A., Mensah, D. O., Walsh, J., Ghosh, I., Ibrahim, S. A., Hogg, J., Peek, N., & Griffiths, F. (2024). Health care professionals' experience of using AI: Systematic review with narrative synthesis. Journal of Medical Internet Research, 26, e55766. https://doi.org/10.2196/55766
- Catalina, Q. M., Fuster-Casanovas, A., Vidal-Alaball, J., Escalé-Besa, A., Marin-Gomez, F. X., Femenia, J., & Solé-Casals, J. (2023). Knowledge and perception of primary care healthcare professionals on the use of artificial intelligence as a healthcare tool. Digital Health, 9, 20552076231180511. https://doi.org/10.1177/20552076231180511
- Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—Addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229
- Daniel, A. D., Asheku, A. N., Stephen, Y., Abraham, G. N., De-Kaa, N. L. P., Terrumun, S. L., Ohiozoje, O. B., Nwunuji, R. G., & Ocheifa, N. M. (2024). Assessment of knowledge, practice, perception, and expectations of artificial intelligence in medical care among staff of a tertiary hospital. Ethiopian Journal of Health Sciences, 34(4), 313–320. https://doi.org/10.4314/ejhs.v34i4.7
- Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
- Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
- Eguia, H., Sánchez-Bocanegra, C. L., Vinciarelli, F., Alvarez-Lopez, F., & Saigí-Rubió, F. (2024). Clinical decision support and natural language processing in medicine: Systematic literature review. Journal of Medical Internet Research, 26, e55315. https://doi.org/10.2196/55315
- Golinelli, D., Boetto, E., Carullo, G., Nuzzolese, A. G., Landini, M. P., & Fantini, M. P. (2020). Adoption of digital technologies in health care during the COVID-19 pandemic: Systematic review of early scientific literature. Journal of Medical Internet Research, 22(11), e22280. https://doi.org/10.2196/22280
- Lybarger, K., Dobbins, N. J., Long, R., Singh, A., Wedgeworth, P., Uzuner, Ö., & Yetisgen, M. (2023). Leveraging natural language processing to augment structured social determinants of health data in the electronic health record. Journal of the American Medical Informatics Association, 30(8), 1389–1397. https://doi.org/10.1093/jamia/ocad073
- Obiekwe, S. J., Omaga, I. B., Ukadike, M. M., Edeh, C. G., Iheanyi, C. E., Anisiobi, P. U., Obi, C. F., Ogenyi, S., Obi, E., et al. (2025). The integration of artificial intelligence in healthcare: A cross-sectional study on the knowledge, perception, and readiness of medical students at a tertiary institution in Nigeria. Journal of Education and Health Promotion, 22(4). https://doi.org/10.1177/09760016241287301
- Ogolodom, M. P., Mbaba, A. N., Johnson, J., Chiegwu, H. U., Ordu, K. S., Okeji, M. C., et al. (2023). Knowledge and perception of healthcare workers towards the adoption of artificial intelligence in healthcare service delivery in Nigeria. Salud Integral y Comunitaria, 1, 16.
- Owoyemi, A., Owoyemi, J., Osiyemi, A., & Boyd, A. (2020). Artificial intelligence for healthcare in Africa. Frontiers in Digital Health, 2, 6. https://doi.org/10.3389/fdgth.2020.00006
- Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenge. Journal of Medical Internet Research, 21(7), e13659. https://doi.org/10.2196/13659
- Shinners, L., Grace, S., Smith, S., Stephens, A., & Aggar, C. (2022). Exploring healthcare professionals’ perceptions of artificial intelligence: Piloting the Shinners Artificial Intelligence Perception tool. Digital Health, 8, 20552076221078110. https://doi.org/10.1177/20552076221078110
- Sim, J.-A., Huang, X., Horan, M. R., Stewart, C. M., Robison, L. L., Hudson, M. M., Baker, J. N., & Huang, I.-C. (2023). Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Artificial Intelligence in Medicine, 146, 102701. https://doi.org/10.1016/j.artmed.2023.102701
- Teibowei, M. T., & Agbai, E. (2023). Awareness and utilization of artificial intelligence-based systems in biomedical translation in Nigeria. International Journal of Medical Evaluation and Physical Report, 7(3), 72–81. https://doi.org/10.56201/ijmepr.v7.no3.2023.pg72.81
- Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
- Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689
- Wang, D., & Zhang, S. (2024). Large language models in medical and healthcare fields: Applications, advances, and challenges. Artificial Intelligence Review, 57, 299. https://doi.org/10.1007/s10462-024-10921-0
- World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization. https://doi.org/10.2471/9789240029200
- World Health Organization. (2025). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. World Health Organization.
- Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2, 719–731. https://doi.org/10.1038/s41551-018-0305-z
- Zhang, H., Jethani, N., Jones, S., Genes, N., Major, V. J., Jaffe, I. S., et al. (2024). Evaluating large language models in extracting cognitive exam dates and scores. PLOS Digital Health, 3(12), e0000685. https://doi.org/10.1371/journal.pdig.0000685