Bridging the Communication Chasm: Clinician Perspectives on AI-Augmented Patient Interactions in Kenyan Oncology - A Qualitative Study Highlighting Ethical and Empathetic Implementation Frameworks
DOI:
https://doi.org/10.70641/ajbds.v2i1.152Keywords:
Artificial intelligence, healthcare communication, doctor-patient interaction, oncology, public health, natural language processingAbstract
This study examines the potential of artificial intelligence (AI) to improve doctor-patient communication in resource-constrained public oncology settings in Kenya, addressing critical communication barriers that impact treatment outcomes and patient satisfaction. A qualitative exploratory study was conducted in three public oncology facilities in Nairobi County, employing semi-structured in-depth interviews with 10 clinical officers and physicians. Data were analysed using thematic analysis with NVivo 12 software to identify patterns and themes related to communication challenges and the potential for AI integration. Three major themes emerged from the analysis: significant communication barriers, including the complexity of technical language, time constraints, and cultural mismatches; high clinician interest in AI communication tools despite limited current awareness; and critical requirements for the ethical implementation of AI systems, including the need for explainable AI, cultural adaptation, and preservation of human empathy. A majority of participants (8 out of 10) identified technical language barriers as the most significant challenge, while all participants (10 out of 10) expressed high interest in AI applications such as medical jargon translation, patient distress detection, and real-time conversational support. This study provides the first empirical evidence for a clinician-endorsed, empathy-centric framework for AI integration in resource-constrained settings, positioning AI not as a replacement but as a tool for augmenting human connection. The core conceptual contribution demonstrates AI as an "empathy enhancer" that addresses systemic communication barriers while preserving the relational aspects of healthcare. The findings provide a culturally appropriate, phased implementation framework for AI communication tools in African healthcare contexts, emphasising comprehensive training and ethical oversight. AI-augmented communication has the potential to improve health outcomes, reduce healthcare disparities, and enhance patient satisfaction in resource-constrained settings, directly supporting Kenya's UHC goals by addressing a key social determinant of health: communication. This study offers crucial insights for the development of healthcare technology in similar resource-constrained environments.
References
Aboye, G. T., Simegn, G. L., & Aerts, J. M. (2024). Assessment of the Barriers and Enablers to the Use of mHealth Systems in Sub-Saharan Africa, as Perceived by Patients, Physicians, and Healthcare Executives in Ethiopia: A Qualitative Study. Journal of Medical Internet Research, 26, e50337. https://doi.org/10.2196/50337 DOI: https://doi.org/10.2196/50337
Awuor, Q. E. (2019). Language policy in the Kenyan education system: The practicality of its implementation and the way forward. Journal of Language, Technology & Entrepreneurship in Africa, 10(1), 94-109. https://doi.org/10.4314/jolte.v10i1.186558
Benary, M., Busch, H., Wickenhauser, C., Kunz, M., & Bullinger, L. (2023). Leveraging large language models for decision support in personalised oncology. JAMA Network Open, 6(11), e2343689. https://doi.org/10.1001/jamanetworkopen.2023.43689 DOI: https://doi.org/10.1001/jamanetworkopen.2023.43689
DeBoer, R. J., Wabl, C. A., Mushi, B. P., Uwamahoro, P., Athanas, R., Ndoli, D. A., Rugengamanzi, E., Ngoma, M., Tuyishime, H., Mtei, D., Nsabimana, N., Van Loon, K., Sanders, J. J., Sudore, R. L., & Cubaka, V. K. (2025). A scoping review of clinical communication in cancer care in Africa. The Oncologist, 30(4), oyaf039. https://doi.org/10.1093/oncolo/oyaf039 DOI: https://doi.org/10.1093/oncolo/oyaf039
Docus AI. (2025). AI in healthcare statistics 2025: Overview of trends. Retrieved from https://docus.ai/blog/ai-healthcare-statistics
Farasati, Far, B. (2023). Artificial intelligence ethics in precision oncology: Balancing advancements in technology with patient privacy and autonomy. Exploration of Targeted Anti-tumour Therapy, 4, 685-689. https://doi.org/10.37349/etat.2023.00147 DOI: https://doi.org/10.37349/etat.2023.00160
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017). Beyond adoption: A new framework for theorising and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), e8775. https://doi.org/10.2196/jmir.8775
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017). Beyond adoption: A new framework for theorising and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), e8775. https://doi.org/10.2196/jmir.8775 DOI: https://doi.org/10.2196/jmir.8775
Harsch, S., Weber, L. S., Kassaman, D., Kailemia, P. N., & Oria, V. O. (2025). Cancer health literacy in Kenya - A scoping review on evidence, concept and a situational analysis of interventions. Frontiers in Public Health, 13, 1527400. https://doi.org/10.3389/fpubh.2025.1527400 DOI: https://doi.org/10.3389/fpubh.2025.1527400
Jerfy, A., Selden, O., & Balkrishnan, R. (2024). The Growing Impact of Natural Language Processing in Healthcare and Public Health SAGE Open Medicine, 12. https://doi.org/10.1177/00469580241290095 DOI: https://doi.org/10.1177/00469580241290095
Kehl, K. L., Xu, W., Lepisto, E., Elmarakeby, H., Hassett, M. J., Van Allen, E. M., & Cerami, E. (2024). Artificial intelligence (AI) in oncology: Current landscape, challenges, and future directions. Cancer Discovery, 14(5), 711-726. https://doi.org/10.1158/2159-8290.CD-23-1199 DOI: https://doi.org/10.1158/2159-8290.CD-23-1199
Kohane, I. S. (2025, March 20). How AI is transforming medicine. Harvard Gazette. Retrieved from https://news.harvard.edu/gazette/story/2025/03/how-ai-is-transforming-medicine-healthcare/
Kouamou, G. K., Kitutu, F. E., Ameh, S., Sewankambo, N. K., & Ataguba, J. E. (2021). Digital health in oncology in Africa: A scoping review and cross-sectional survey. International Journal of Medical Informatics, 158, 104659. https://doi.org/10.1016/j.ijmedinf.2021.104659 DOI: https://doi.org/10.1016/j.ijmedinf.2021.104659
Longhurst, C. A., Tai-Seale, M., Shortliffe, E. H., Dunn, M., Hojecki, R., Furukawa, M. F., Boisvert, P., Cox, K., Kanter, M. H., & Sharp, C. (2024). AI-generated physician responses improve communication quality and efficiency. JAMA Network Open, 7(4), e247621. https://doi.org/10.1001/jamanetworkopen.2024.7621 DOI: https://doi.org/10.1001/jamanetworkopen.2024.6565
Manz, C. R., Chen, J., Liu, M., Chivers, C., Regli, S. H., Braun, J., Draugelis, M. E., Hannum, M., Hu, Z., Schapira, M. M., Shulman, L. N., Small, D. S., & Parikh, R. B. (2023). Validation of a machine learning algorithm to predict response to anticancer immunotherapy. Nature Medicine, 29(10), 2606-2615. https://doi.org/10.1038/s41591-023-02570-y
Ning, Y., He, S., Wu, J., Xiao, C., & Zhang, R. (2024). Generative artificial intelligence and ethical considerations in health care: A scoping review and ethics checklist. The Lancet Digital Health, 6(12), e848-e856. https://doi.org/10.1016/S2589-7500(24)00060-8 DOI: https://doi.org/10.1016/S2589-7500(24)00143-2
Petersson, L., Larsson, I., Nygren, J. M., Nilsen, P., Neher, M., Reed, J. E., Tyskbo, D., & Svedberg, P. (2022). Challenges to implementing artificial intelligence in healthcare: A qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research, 22, 850. https://doi.org/10.1186/s12913-022-08215-8 DOI: https://doi.org/10.1186/s12913-022-08215-8
Ragan, K. R., Lunsford, N. B., Smith, J. L., Saraiya, M., & Aketch, M. (2018). Improving access to cancer testing and treatment in Kenya. JCO Global Oncology, 4, 1-12. https://doi.org/10.1200/JGO.18.00016
Stefan, D. C. (2015). Cancer care in Africa: An overview of resources. Journal of Global Oncology, 1(1), 30-36. https://doi.org/10.1200/JGO.2015.000406 DOI: https://doi.org/10.1200/JGO.2015.000406
Tai-Seale, M., Baxter, S., Longhurst, C., Boisvert, P., Dunn, M., Hojecki, R., Cox, K., & Kanter, M. H. (2024). Technology-enabled care: Positions and perspectives of patients on the role of artificial intelligence in healthcare communication. Health Affairs, 43(2), 197-205. https://doi.org/10.1377/hlthaff.2023.01005
Verlingue, L., Boyer, C., Olgiati, L., Brutti Mairesse, C., Morel, D., & Blay, J. Y. (2024). Artificial intelligence in oncology: Ensuring safe and effective integration of language models in clinical practice. The Lancet Regional Health -- Europe, 46, 101064. https://doi.org/10.1016/j.lanepe.2024.101064 DOI: https://doi.org/10.1016/j.lanepe.2024.101064
Webster, P. (2023). Six ways large language models are changing healthcare. Nature Medicine, 29, 2969-2971. https://doi.org/10.1038/s41591-023-02661-w DOI: https://doi.org/10.1038/s41591-023-02700-1
Yim, W. W., Yetisgen, M., Harris, W. P., & Kwan, S. W. (2016). Natural language processing in oncology: A review. JAMA Oncology, 2(6), 797-804. https://doi.org/10.1001/jamaoncol.2016.0213 DOI: https://doi.org/10.1001/jamaoncol.2016.0213
Zhang, Y., Xu, Y., Chen, J., Xie, F., & Chen, H. (2025). Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. npj Digital Medicine, 8, 14. https://doi.org/10.1038/s41746-025-01471-y DOI: https://doi.org/10.1038/s41746-025-01471-y
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