Intelligent Healthcare Chatbots for Remote Patient Support
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DOI:
https://doi.org/10.67228/30715628/IJMIET-2021PII4Q8CPublished 09-04-2021
Healthcare Chatbots, Artificial Intelligence, Natural Language Processing, Remote Patient Monitoring, Telemedicine, Clinical Decision Support, Digital Health, Conversational Agents, Patient Engagement, Machine Learning in Healthcare Issue
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ArticlesHow to Cite
[1]A. Verma, “Intelligent Healthcare Chatbots for Remote Patient Support”, ijmiet, vol. 4, no. 2, pp. 01–13, Sep. 2021, doi: 10.67228/30715628/IJMIET-2021PII4Q8C.Abstract
With the rapid development of artificial intelligence (AI), natural language processing (NLP), and mobile health technologies, remote healthcare services have significantly improved. Intelligent healthcare chatbots have emerged as an important tool for providing scalable, affordable, and continuous patient care outside clinical environments. These chatbots use conversational interfaces to deliver medical information, perform symptom checks, schedule appointments, remind patients about medications, provide mental health support, and offer personalized health education. The increasing workload in healthcare systems, shortage of healthcare professionals, and rising chronic diseases have accelerated the adoption of chatbot-based remote care solutions. This paper presents a comprehensive study of intelligent healthcare chatbots for supporting remote patients, including their architecture, features, integration with health information systems, and clinical applications. Modern chatbots employ machine learning models, deep learning-based NLP techniques, and medical knowledge bases to enable context-aware, adaptive, and personalized patient interactions. Healthcare chatbots also improve access to medical services, particularly in underserved and rural areas where healthcare facilities may be limited. The paper further discusses ethical, legal, and privacy concerns such as patient data protection, regulatory compliance, and potential algorithmic bias. Chatbot performance is evaluated using metrics like response accuracy, user satisfaction, task completion rate, and clinical relevance, highlighting their advantages over traditional telehealth methods. The proposed framework integrates multimodal data sources, real-time patient feedback, and active learning techniques to enhance clinical decision-making and patient engagement. Overall, intelligent healthcare chatbots can reduce response time, improve treatment adherence, and enhance patient experience. The study concludes that healthcare chatbots have strong potential to transform remote healthcare delivery while emphasizing the need for further research to improve clinical reliability and regulatory compliance.
References
[1] Weizenbaum, J. (1966). ELIZA—A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.
[2] Shawar, B. A., & Atwell, E. (2007). Different measurements metrics to evaluate a chatbot system. Proceedings of the Workshop on Bridging the Gap.
[3] Bhirud, N., et al. (2019). A literature review on chatbots in healthcare domain. International Journal of Scientific Research.
[4] Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. Artificial Intelligence Applications and Innovations.
[5] Young, S., et al. (2013). POMDP-based statistical spoken dialog systems: A review. Proceedings of the IEEE, 101(5), 1160–1179.
[6] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
[7] Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems (NeurIPS).
[8] Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with depression and anxiety using a fully automated conversational agent (Woebot). JMIR Mental Health, 4(2).
[9] Laranjo, L., et al. (2018). Conversational agents in healthcare: A systematic review. Journal of the American Medical Informatics Association, 25(9), 1248–1258.
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How to Cite
[1]A. Verma, “Intelligent Healthcare Chatbots for Remote Patient Support”, ijmiet, vol. 4, no. 2, pp. 01–13, Sep. 2021, doi: 10.67228/30715628/IJMIET-2021PII4Q8C.
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