Generative-Ai Solutions for Connecting Seniors and Healthcare Providers

As the aging population grows, effective healthcare communication becomes increasingly critical, particularly for older adults managing multimorbidity (multiple chronic conditions). Traditional methods often fail to engage this demographic, leading to misunderstandings, inefficient care coordination, and increased provider workload. This paper presents a generative AI-driven solution integrating a multimodal chatbot and an AIenhanced provider dashboard to bridge this gap. The chatbot employs a hybrid architecture combining intent-driven logic with large language model (LLM)-powered natural language understanding (NLU) for safe, context-aware interactions, while the dashboard synthesizes patient-chatbot dialogues, extracting key insights like sentiment trends, discussion topics, and tone analysis to aid clinical decision-making. Additionally, an LLM-assisted virtual meeting room enables real-time transcription, patient history summarization, interactive querying of past interactions, and streamlining consultations. By leveraging conversational AI, real-time analytics, and AI-assisted care coordination, this scalable solution enhances accessibility, promotes independent living, and improves provider efficiency, offering a transformative approach to patient-centered healthcare for aging populations.

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Publication Details

Published
2025-06-18
DOI
https://doi.org/10.1109/cbms65348.2025.00092
Primary Topic
Interprofessional Education and Collaboration
Type
article
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article

Generative-Ai Solutions for Connecting Seniors and Healthcare Providers

Yufei Yuan, Kamran Sartipi, Emad Deilam Salehi
Interprofessional Education and Collaboration
article

Generative-Ai Solutions for Connecting Seniors and Healthcare Providers

Yufei Yuan, Kamran Sartipi, Emad Deilam Salehi
article en

Abstract

As the aging population grows, effective healthcare communication becomes increasingly critical, particularly for older adults managing multimorbidity (multiple chronic conditions). Traditional methods often fail to engage this demographic, leading to misunderstandings, inefficient care coordination, and increased provider workload. This paper presents a generative AI-driven solution integrating a multimodal chatbot and an AIenhanced provider dashboard to bridge this gap. The chatbot employs a hybrid architecture combining intent-driven logic with large language model (LLM)-powered natural language understanding (NLU) for safe, context-aware interactions, while the dashboard synthesizes patient-chatbot dialogues, extracting key insights like sentiment trends, discussion topics, and tone analysis to aid clinical decision-making. Additionally, an LLM-assisted virtual meeting room enables real-time transcription, patient history summarization, interactive querying of past interactions, and streamlining consultations. By leveraging conversational AI, real-time analytics, and AI-assisted care coordination, this scalable solution enhances accessibility, promotes independent living, and improves provider efficiency, offering a transformative approach to patient-centered healthcare for aging populations.

East Carolina University (US), McMaster University (CA)
Industry, innovation and infrastructure
Openalex Percentile: Top 73%
Interprofessional Education and Collaboration
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Generative-Ai Solutions for Connecting Seniors and Healthcare Providers — Yufei Yuan, Kamran Sartipi, et al. · (2025) | TGRS Research Map | TGRS