Effect of Artificial Intelligence Adoption on Performance of Healthcare Sector in Kenya: A Case of Nairobi Public Hospitals

Authors

  • Corretta Tira United States International University, Kenya
  • Prof. Allan Kihara United States International University, Kenya

DOI:

https://doi.org/10.47941/jbsm.3150

Keywords:

Adoption Rate of AI, Integration of AI, Utilization of AI in Predicting Emerging Diseases and Healthcare Performance

Abstract

Purpose: This study investigated the impact of Artificial Intelligence (AI) adoption on healthcare performance in public hospitals within Nairobi City County, Kenya. Despite AI's potential to transform healthcare through enhanced diagnostic accuracy, resource allocation, and operational efficiency, its adoption remained notably low in Nairobi's healthcare sector.

Methodology: The research adopted a descriptive correlational design to examine relationships between AI adoption and healthcare performance metrics, focusing on three key areas: AI adoption rates, system integration, and disease prediction capabilities. The study targeted healthcare professionals, administrators, and technical staff across public hospitals in Nairobi, with a population of 210 respondents and a sample size of 138, determined using Yamane's formula. Data collection utilized structured questionnaires administered to physicians, nurses, IT specialists, administrators, and policymakers between 2020 and 2024, examining adoption trends particularly in light of the COVID-19 pandemic's acceleration of digital healthcare transformation.

Findings: The findings revealed statistically significant positive relationships between all AI implementation dimensions and healthcare performance. Among them, AI utilization for disease prediction had the strongest impact explaining 44.6% of variance in performance followed closely by AI integration at 43.1%, and AI adoption rate contributed significantly at 41.7% of the variance. Healthcare performance metrics showed marked improvements, with service quality achieving the highest mean score (4.08), followed by treatment outcomes (3.98) and patient satisfaction (3.95). The study concludes that predictive AI applications provide the highest value for healthcare performance enhancement, while successful integration requires user-centered design and comprehensive technical support.

 Unique Contribution to Theory, Practice and Policy: Key recommendations include establishing mandatory AI training programs, developing robust data governance frameworks, implementing phased adoption strategies, and creating AI transparency initiatives to address staff trust issues and maximize the demonstrated performance improvements across all implementation dimensions.

Downloads

Download data is not yet available.

References

Aggarwal, R., & Ranganathan, P. (2019). Study designs: Part 2 – Descriptive studies. Perspectives in Clinical Research, 10(1), 34-36. https://doi.org/10.4103/picr.PICR_154_18

Agyemang, K., Mensah, Y., & Osei-Tutu, A. (2022). Barriers to artificial intelligence adoption in Sub-Saharan African healthcare systems: A systematic review. Digital Health, 8(2), 205-221. https://doi.org/10.1177/20552076221089654

Agyemang, O. O., Osei-Agyemang, M. S., & Mensah, E. K. (2022). The impact of artificial intelligence on healthcare delivery in Kenya: A systematic review. Journal of Medical Internet Research, 24(4), e28549. https://doi.org/10.2196/28549

Aldoseri, H., Al-Mansour, B., & Al-Said, M. (2023). Implementation of artificial intelligence in healthcare: A global perspective. Health Informatics Journal, 29(1), 146-159. https://doi.org/10.1177/14604582231156789

Auko, L. O., Otieno, L. O., & Nyamongo, M. O. (2021). The role of artificial intelligence in healthcare delivery in Kenya. International Journal of Medical Informatics, 147, 104444. https://doi.org/10.1016/j.ijmedinf.2021.104444

Behara, K., Bhero, E., & Gonela, V. (2022). Artificial intelligence in medical diagnostics: A review from a South African context. Scientific African, 16, e01360. https://doi.org/10.1016/j.sciaf.2022.e01360

Brand, D. (2022). Responsible artificial intelligence in government: Development of a legal framework for South Africa. JeDEM - eJournal of eDemocracy and Open Government, 14(1), 130-150. https://doi.org/10.29379/jedem.v14i1.678

Bukachi, F. (2024). Challenges of AI integration in Kenyan healthcare: A multi-site study. East African Medical Journal, 101(1), 45-58. https://doi.org/10.4314/eamj.v101i1.6

Chimole, O. N., & Owade, E. O. (2021). The potential of artificial intelligence in improving healthcare delivery in Kenya. Journal of Healthcare Engineering, 2021, 1-13. https://doi.org/10.1155/2021/6685617

Christie, M. (2020). Digital health transformation in developing countries: Evidence from East Africa. Journal of Global Health, 10(2), 020416. https://doi.org/10.7189/jogh.10.020416

De Almeida, P. G., Dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: A framework for governance. Ethics and Information Technology, 23(3), 505-525. https://doi.org/10.1007/s10676-021-09593-z

Donnelly, D. (2022). First, do no harm: Legal principles regulating the future of artificial intelligence in health care in South Africa. Potchefstroom Electronic Law Journal, 25, 1-43. https://doi.org/10.17159/1727-3781/2022/v25ia11118

Etori, N. A., Temesgen, E., & Gini, M. L. (2023). What we know so far: Artificial intelligence in African healthcare. African Healthcare Innovations Journal, 10(1), 56-78. https://doi.org/10.48550/arXiv.2305.18302

Federspiel, F., Mitchell, R., Asokan, A., Umana, C., & McCoy, D. (2023). Threats by artificial intelligence to human health and human existence. BMJ Global Health, 8(5), e010435. https://doi.org/10.1136/bmjgh-2022-010435

Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare (pp. 295-336). Academic Press. https://doi.org/10.1016/b978-0-12-818438-7.00012-5

Harasimiuk, D. E., & Braun, T. (2021). Re-defining of artificial intelligence. In Regulating artificial intelligence (pp. 6-14). Routledge. https://doi.org/10.4324/9781003134725-2-2

Holmström, O., Linder, N., & Lundin, J. (2021). Point-of-care digital cytology with artificial intelligence for cervical cancer screening in a resource-limited setting. JAMA Network Open, 4(3), e211740. https://doi.org/10.1001/jamanetworkopen.2021.1740

Jackline, A. (2022). Artificial intelligence in Kenya. Paradigm Initiative. https://paradigmhq.org/wp-content/uploads/2022/02/Artificial-Inteligence-in-Kenya-1.pdf

Jha, S., Topol, E. J., & Adashi, E. Y. (2019). Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 322(3), 219-220. https://doi.org/10.1001/jama.2019.9816

Karanja, M., & Otieno, L. O. (2020). The impact of artificial intelligence on healthcare costs in Kenya. International Journal of Healthcare Economics, 19(2), 1-12.

Kiplagat, R. K., & Mutuku, M. (2023). Artificial intelligence inclusion and performance of sensor management system in Nairobi-City Water and Sewerage Company, Kenya. Journal of Business and Management Sciences, 11(3), 98-106. https://doi.org/10.12691/jbms-11-3-3

Mbugua, A., & Namada, J. (2019). Information technology integration effect on operational performance of Kenya's public health sector. Integrated Journal of Business and Economics, 3(3), 45-62. https://doi.org/10.33019/ijbe.v3i3.236

Ministry of Health. (2014). Kenya health policy 2014–2030. Government of Kenya. https://publications.universalhealth2030.org/uploads/kenya_health_policy_2014_to_2030.pdf

Ministry of Health, Republic of Kenya. (2016). Kenya national e-health policy 2016-2030. Kenya Institute for Public Policy Research and Analysis.

Muinga, N., Magare, S., Paton, C., English, M., Kiragga, A., Ouma, P., Watkins, J., Wamae, A., Nyamai, R., Mwangi, A., Ayieko, P., Irimu, G., Tsofa, B., Molyneux, C., Agweyu, A., Bejon, P., & Snow, R. W. (2020). Digital health systems in Kenyan public hospitals: A mixed-methods survey. BMC Medical Informatics and Decision Making, 20(1), 5. https://doi.org/10.1186/s12911-019-1005-7

Naidoo, S., Bottomley, D., Naidoo, M., Donnelly, D., & Thaldar, D. W. (2022). Artificial intelligence in healthcare: Proposals for policy development in South Africa. South African Journal of Bioethics and Law, 15(1), 11-16. https://doi.org/10.7196/sajbl.2022.v15i1.797

Naitore, K. J., & Nyang'au, S. (2023). Effect of supply chain management relationship on the performance of public hospitals in Nairobi County. International Journal of Social Sciences and Humanities Research, 1(1), 23-38. https://doi.org/10.61108/ijsshr.v1i1.12

Ofori, I. K., Quaidoo, C., & Ofori, P. E. (2021). What drives financial sector development in Africa? Insights from machine learning. Applied Artificial Intelligence, 35(15), 2124-2156. https://doi.org/10.1080/08839514.2021.1999597

Okumu, J. O., & Owade, E. O. (2020). The use of artificial intelligence in disease diagnosis in Kenya: A focus on non-communicable diseases. The Lancet Digital Health, 2(8), e402-e410. https://doi.org/10.1016/S2589-7500(20)30139-4.

Owoyemi, A., Owoyemi, J., & Boyd, A. (2020). Artificial intelligence for healthcare in Africa. Frontiers in Digital Health, 2, 6. https://doi.org/10.3389/fdgth.2020.00006

Pantserev, K. (2020). States of sub-Saharan Africa on the way to the creation of artificial intelligence: The myth or the reality? Asia and Africa Today, 10, 29-35. https://doi.org/10.31857/s032150750011108-0

Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), 1-23. https://doi.org/10.1186/s12911-021-01478-2

Shiyyab, F. S., Alzoubi, A. B., Obidat, Q. M., & Alshurafat, H. (2023). The impact of artificial intelligence disclosure on financial performance. International Journal of Financial Studies, 11(3), 115. https://doi.org/10.3390/ijfs11030115

Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Udegbe, F. C., Ebulue, O. R., & Ekesiobi, C. S. (2024). The role of artificial intelligence in healthcare: A systematic review of applications and challenges. International Medical Science Research Journal, 4(4), 234-251. https://doi.org/10.51594/imsrj.v4i4.1052

United Nations General Assembly. (2015). Transforming our world: The 2030 agenda for sustainable development (A/RES/70/1). https://docs.un.org/en/A/RES/70/1

Wang, Z. (2020). Enhancing quality of patients care and improving patient experience in China with assistance of artificial intelligence. Chinese Medical Sciences Journal, 35(3), 286-288. https://doi.org/10.24920/003832

Downloads

Published

2025-09-04

How to Cite

Tira, C., & Kihara, A. (2025). Effect of Artificial Intelligence Adoption on Performance of Healthcare Sector in Kenya: A Case of Nairobi Public Hospitals. Journal of Business and Strategic Management, 10(13), 31–52. https://doi.org/10.47941/jbsm.3150

Issue

Vol. 10 No. 13 (2025)

Section

Articles

License

Copyright (c) 2025 Corretta Tira, Prof. Allan Kihara

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.