Open Access
Comprehensive Study on the Use of Artificial Intelligence to Minimize Bias in Healthcare Succession Management
4
Independent Researcher, California-USA
Abstract
In healthcare, succession planning mitigates the risk of leadership vacancies during retirements, turnover, and staffing shortages. However, current practices to choose a next leader often rely on subjective judgment, which can reinforce bias and limit fairness in leadership decisions. This study examines whether Artificial Intelligence (AI) can improve succession planning by supporting more objective and data-informed assessment of talent. The findings suggest that AI may help organizations define clearer criteria, identify high-potential candidates, and expand opportunities for underrepresented groups. At the same time, the study shows that AI must be used carefully, with strong data governance and collaboration among human resources professionals, data scientists, and clinical leaders. Without these safeguards, AI may reproduce the same biases it is meant to reduce. Used responsibly, AI has the potential to make healthcare leadership selection more transparent, equitable, and inclusive.
Keywords
artificial intelligence,
healthcare succession planning,
bias reduction,
leadership selection,
algorithmic fairness,
AI governance,
talent management,
ethical decision making
References
Baduge, M. S. D. S. P., Garth, B., Boyd, L., Ward, K., Joseph, K., Proimos, J., & Teede, H. J. (2023). Barriers to advancing women nurses in healthcare leadership: A systematic review and meta-synthesis. eClinicalMedicine, 67, 102354. https://doi.org/10.1016/j.eclinm.2023.102354
Carriere, B. K., Muise, M., Cummings, G., & Newburn-Cook, C. (2009). Healthcare succession planning: An integrative review. Journal of Nursing Administration, 39(12), 548–555. https://doi.org/10.1097/NNA.0b013e3181c18010
Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G
Disch, J. (2020). Nursing leadership in policy formation. Nursing Forum, 55(1), 4–10. https://doi.org/10.1111/nuf.12375
Fasolo, B., Heard, C., & Scopelliti, I. (2024). Mitigating cognitive bias to improve organizational decisions: An integrative review, framework, and research agenda. Journal of Management. https://doi.org/10.1177/01492063241287188
Illinois General Assembly. (2021). An act concerning nursing (Public Act 102-0641). https://www.ilga.gov/documents/legislation/publicacts/102/PDF/102-0641.pdf
Nouis, S. C. E., Uren, V., & Jariwala, S. (2025). Evaluating accountability, transparency, and bias in AI-assisted healthcare decision-making: A qualitative study of healthcare professionals’ perspectives in the UK. BMC Medical Ethics, 26, 89. https://doi.org/10.1186/s12910-025-01243-z
Rahimi, S. A., Wang, T., Emami, E., Jafarpour, D., Tolentino, R., & Gore, G. (2024). EDAI framework for integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence. JMIR. https://www.jmir.org/2024/1/e63356/
Saposnik, G., Redelmeier, D. A., Ruff, C. C., & Tobler, P. N. (2016). Cognitive biases associated with medical decisions: A systematic review. BMC Medical Informatics and Decision Making, 16(1), 138. https://doi.org/10.1186/s12911-016-0377-1
Secinaro, S., Calandra, D., McLaughlin, L., & Mauri, M. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21, 125. https://doi.org/10.1186/s12911-021-01488-9
Soleimani, M., Intezari, A., Arrowsmith, J., Pauleen, D. J., & Taskin, N. (2025). Reducing AI bias in recruitment and selection: An integrative grounded approach. The International Journal of Human Resource Management. https://doi.org/10.1080/09585192.2025.2480617
Stanford HAI. (2026, May 25). AI hiring tools can yield racial bias and systemic rejection. https://hai.stanford.edu/news/ai-hiring-tools-can-yield-racial-bias-and-systemic-rejection
Wang, T., Emami, E., Jafarpour, D., Tolentino, R., Gore, G., et al. (2025). Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: A scoping review. PLOS Digital Health, 4(7), e0000941. https://doi.org/10.1371/journal.pdig.0000941
White, T., Obregon, M., Resnick, A. F., & Farrell, C. (2025). Succession planning for community-engaged leaders: Culture, context and continuity in equity-oriented leadership. Frontiers in Education, 10, 1527211. https://doi.org/10.3389/feduc.2025.1527211
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