Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health

TL;DR

An overview of the current landscape of LLMs in healthcare, specifically focusing on their applications in analyzing and generating conversations for improved patient engagement, and discusses best practices and guidelines for the responsible development and deployment of LLMs in healthcare settings.

Abstract

The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI. This paper presents an overview of the current landscape of LLMs in healthcare, specifically focusing on their applications in analyzing and generating conversations for improved patient engagement. We showcase the power of LLMs in handling unstructured conversational data through four case studies: (1) analyzing mental health discussions on Reddit, (2) developing a personalized chatbot for cognitive engagement in seniors, (3) summarizing medical conversation datasets, and (4) designing an AI-powered patient engagement system. These case studies demonstrate how LLMs can effectively extract insights and summarizations from unstructured dialogues and engage patients in guided, goal-oriented conversations. Leveraging LLMs for conversational analysis and generation opens new doors for many patient-centered outcomes research opportunities. However, integrating LLMs into healthcare raises important ethical considerations regarding data privacy, bias, transparency, and regulatory compliance. We discuss best practices and guidelines for the responsible development and deployment of LLMs in healthcare settings. Realizing the full potential of LLMs in digital health will require close collaboration between the AI and healthcare professionals communities to address technical challenges and ensure these powerful tools' safety, efficacy, and equity.

Figures (6)

• Figure 1: Overview of study methodology. The sub-images were created with the help of DALL$\cdot$E 3.
• Figure 2: Architecture of the bookclub host chatbot. For more technical details, please visit the open-source repo: https://github.com/Twilight-Tales/Twilight-Chat and the technical report zhou2024bookclub
• Figure 3: Clinician dashboard displaying current patients with their upcoming call schedules color-coded by status (completed, scheduled, failed), allowing easy monitoring of patient engagement.
• Figure 4: Patient profile editing screen enabling clinicians to update patient information, view upcoming scheduled calls, and add new calls with specific instruments like quality of life assessments.
• Figure 5: UI for reviewing of the patient chat history, showing a transcription of the chatbot conversing naturally with the patient to ask follow-up questions about their well-being, symptoms, and quality of life.