Paper Infomation
The Role of Artificial Intelligence in Enhancing Service Quality in Public Hospitals
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Author: Liang Zhou, Bei-bei Hu, Chao Liang, Yi Jin, Zhou Zhou, Qiong Ni
Abstract: This investigation examines how Artificial Intelligence (AI) can transform service quality in public hospitals, with particular attention to current applications in operational domains and their associated benefits. Yet the deployment of AI technologies encounters notable challenges, such as constraints in technological infrastructure and data governance, limitations in financial and resource allocation, and clinician reluctance frequently arising from trust deficits and inadequate AI literacy. Moreover, technical capabilities by themselves prove inadequate; comprehensive governance structures are indispensable for ethical and effective implementation. Consequently, core principles encompassing accountability, transparency, equity, safety, and adaptability must guide AI governance frameworks within public hospitals. Successful implementation necessitates interdisciplinary oversight committees, standardized assessment protocols, and ongoing surveillance to guarantee that AI systems bolster, rather than compromise, service quality and health equity.
Keywords: Artificial Intelligence, Public Hospitals, Service Quality, AI Governance
References:
[1] ASTP. (2025). Hospital trends in the use, evaluation, and governance of predictive AI, 2023–2024 (ASTP Data Brief No. 80). U.S. Department of Health and Human Services, HealthIT.gov. https://www.healthit.gov/data/data-briefs/hospital-trends-use-evaluation-and-governance-predictive-ai-2023-2024
[2] Laurent, A. (2025). AI in hospital operations: 2025 trends, efficiency & data. IntuitionLabs. https://intuitionlabs.ai/articles/ai-hospital-operations-2025-trends
[3] Daniel Nasef, Demarcus Nasef, Viola Sawiris. et al. (2025). Integrating artificial intelligence in clinical practice, hospital management, and health policy: literature review. Journal of Hospital Management and Health Policy. doi: 10.21037/jhmhp-24-138
[4] Hassan, M., Kushniruk, A., & Borycki, E. (2024). Barriers to and facilitators of artificial intelligence adoption in health care: Scoping review. JMIR Human Factors, 11, e48633. https://doi.org/10.2196/48633
[5] Alghareeb, E., & Aljehani, N. (2025). AI in health care service quality: Systematic review. JMIR AI, 4, e69209. https://doi.org/10.2196/69209
[6] Alzghoul, B. (2024). Impact of artificial intelligence on healthcare quality: A systematic review and meta-analysis. The Open Public Health Journal, 17, e18749445181059. https://doi.org/10.2174/0118749445181059240201054546
[7] Tribe AI. (2025, March 10). How AI is improving hospital management and patient care. https://www.tribe.ai/applied-ai/ai-in-healthcare-administration
[8] S&P Global. (n.d.). The rise of GenAI: A new era of customer-centric healthcare. https://www.spsglobal.com/en/news/generative-ai-health
[9] Putty, C. (2025). The impact of AI on healthcare administrative costs: 2025 benchmark report. Thoughtful AI Blog. https://www.thoughtful.ai/blog/the-impact-of-ai-on-healthcare-administrative-costs--2025-benchmark-report
[10] Nawaz, S. (2025, August 3). AI agents in healthcare administration: From patient onboarding to claims. Ampcome. https://www.ampcome.com/post/ai-agents-in-healthcare-administration
[11] Abdelwanis, M., Simsekler, M. C. E., Gabor, A. F., et al. (2025). Artificial intelligence adoption challenges from healthcare providers' perspectives: A comprehensive review and future directions. Safety Science, 107028. https://doi.org/10.1016/j.ssci.2025.107028
[12] Clark, S. E., Mathur, S., Barrado-Martin, Y., et al. (2025). An umbrella review of the facilitators and barriers to implementing artificial intelligence solutions within hospital settings: through the lens of the NAsss framework (spread, scale-up and sustainability). medRxiv. https://doi.org/10.1101/2025.06.19.25329916
[13] Kumar, R., Singh, A., Kassar, A. S. A., et al. (2025). Adoption challenges to artificial intelligence literacy in public healthcare: an evidence-based study in Saudi Arabia. Frontiers in Public Health, 13, 1558772. https://doi.org/10.3389/fpubh.2025.1558772
[14] World Health Organization. (2025). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. https://www.who.int/publications/i/item/9789240084759
[15] Wells, B. J., Nguyen, H. M., McWilliams, A., et al. (2025). A practical framework for appropriate implementation and review of artificial intelligence (FAlR-AI) in healthcare. npj Digital Medicine, 8, 1900. https://doi.org/10.1038/s41746-025-01900-y
[16] UC Davis Health. (2025, April 10). UC Davis Health uses AI models to leave no patient behind. https://health.ucdavis.edu/news/headlines/uc-davis-health-uses-ai-models-to-leave-no-patient-behind/2025/04
[17] Freeman, S., Wang, A., Saraf, S., et al. (2025). Developing an AI governance framework for safe and responsible AI in health care organizations: Protocol for a multimethod study. JMIR Research Protocols, 14, e75702. https://doi.org/10.2196/75702
[18] Olsen, E. (2025, January 14). Hospital evaluation of AI predictive tools for bias is inconsistent, study finds. Healthcare Dive. https://www.healthcaredive.com/news/ai-bias-hospital-evaluation-predictive-tools/722462/
[19] McDill, V. (n.d.). New study analyzes hospitals' use of AI-assisted predictive tools for accuracy and biases. University of Minnesota School of Public Health News. https://www.sph.umn.edu/news/new-study-analyzes-hospitals-use-of-ai-assisted-predictive-tools-for-accuracy-and-biases