AI in healthcare literature review

Use of Chatbots in Healthcare: Operations, Patient Engagement, and Cost Savings

Chatbots powered by artificial intelligence (AI) have emerged as transformative tools in healthcare, enhancing operational efficiency, patient engagement, and cost-effectiveness. This brief summary synthesizes evidence from recent systematic reviews and studies to highlight their impact in these domains.

Healthcare Operations

 AI-powered chatbots streamline healthcare operations by automating administrative tasks and supporting clinical workflows. Mohamed Jasim et al. (2025) found that chatbots reduce administrative burdens by handling repetitive inquiries, appointment scheduling, patient triage, and medical record management, allowing healthcare providers to focus on clinical care. Similarly, Wah (2025) reported that hybrid chatbots integrate with electronic health records to provide real-time data access, improving operational efficiency in hospitals. Milne-Ives et al. (2020) noted that conversational agents enhance workflow automation, reducing errors in patient data management and information. These findings indicate that chatbots optimize resource allocation and operational processes in healthcare settings.

Patient Engagement

Chatbots significantly improve patient engagement by delivering personalized health interventions and facilitating continuous communication. Kurniawan et al. (2024) reported that chatbots for chronic illness management enhance patient self-efficacy by providing 24/7 support and education, fostering active participation in care.  Aggarwal et al. (2023) demonstrated that AI chatbots promote health behavior changes, such as smoking cessation and medication adherence, through tailored motivational messaging. Wang et al. (2024) highlighted that conversational large language models improve patient satisfaction by offering empathetic responses and accessible health information, particularly for underserved populations. These studies underscore chatbots’ role in strengthening patient-provider interactions and engagement.

Cost Savings

The implementation of chatbots in healthcare is associated with substantial cost reductions. Sallam (2023) noted that chatbots decrease operational costs by automating routine tasks, reducing the need for human staff in administrative roles. Milne-Ives et al. (2020) reported that chatbot-driven triage systems lower emergency department visits by guiding patients to appropriate care levels, saving healthcare systems significant expenses. Additionally, Wah (2025) found that hybrid chatbots reduce hospital readmissions for chronic conditions by improving patient monitoring, leading to long-term cost savings. These findings suggest that chatbots offer a cost-effective solution for healthcare delivery.

Conclusion

AI-powered chatbots are revolutionizing healthcare by enhancing operational efficiency, improving patient engagement, and generating cost savings. By automating administrative tasks, delivering personalized interventions, and reducing unnecessary healthcare utilization, chatbots address critical challenges in healthcare delivery. However, further research is needed to address ethical concerns, such as data privacy and equitable access, to ensure their widespread adoption.

 

 

References

Abd-Alrazaq, A. A., Rababeh, A., Alajlani, M., Bewick, B. M., & Househ, M. (2020). Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis. Journal of Medical Internet Research, 22(7), e16021. https://doi.org/10.2196/16021

Aggarwal, A., Tam, C. C., Wu, D., Li, X., & Qiao, S. (2023). Artificial intelligence-based chatbots for promoting health behavioral changes: systematic review. Journal of Medical Internet Research, 25, e40789. https://doi.org/10.2196/40789

Kurniawan, M. H., Handiyani, H., Nuraini, T., Hariyati, R. T. S., & Sutrisno, S. (2024). A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Annals of Medicine, 56(1), 2302980. https://doi.org/10.1080/07853890.2024.2302980

Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de Pennington, N., Mole, G., Normando, E., & Meinert, E. (2020). The effectiveness of artificial intelligence conversational agents in health care: systematic review. Journal of Medical Internet Research, 22(10), e20346. https://doi.org/10.2196/20346

Mohamed Jasim, K., Malathi, A., Bhardwaj, S., & Aw, E. C. (2025). A systematic review of AI-based chatbot usages in healthcare services. Journal of Health Organization and Management.  http://dx.doi.org/10.1108/JHOM-12-2023-0376

Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887

Wah, J. N. K. (2025). Revolutionizing e-health: The transformative role of AI-powered hybrid chatbots in healthcare solutions. Frontiers in Public Health, 13, 1530799. https://doi.org/10.3389/fpubh.2025.1530799

Wang, L., Wan, Z., Ni, C., Song, Q., Li, Y., Clayton, E. W., Malin, B. A., & Yin, Z. (2024). A systematic review of ChatGPT and other conversational large language models in healthcare. medRxiv. https://doi.org/10.1101/2024.04.26.24306390

Notes

  • Quantitative Metrics: Where specific percentages or figures were not provided in the original document, estimates are derived from aligned findings in the referenced studies (e.g., Mohamed Jasim et al., 2025, for administrative workload reduction).

  • Qualitative Insights: The "Key Outcomes" column summarizes broader impacts, while "Data at a Glance" prioritizes concise, measurable results for quick reference.

  • The evidence presented in this summary was systematically searched and compiled by Judy Lindsay PT, DPT, CHCQM and TheraAI Solutions, a leader in AI-driven healthcare innovations, to ensure comprehensive and reliable high quality insights into chatbot applications. For more information:  www.theraAIchat.com.