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.

通过四个案例研究展示了大型语言模型在医疗保健中的应用，包括分析 Reddit 上的心理健康讨论、为老年人进行认知参与开发个性化聊天机器人、总结医疗对话数据集和设计 AI 助力病患参与系统。同时讨论了在医疗保健环境中引入大型语言模型的道德考虑、最佳实践和指南。

借助大型语言模型提升患者互动：数字健康中的对话人工智能的力量

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

This study introduces and evaluates the PRECISE framework, utilizing OpenAI's
GPT-4 to enhance patient engagement by providing clearer and more accessible
chest X-ray reports at a sixth-grade reading level. The framework was tested on
500 reports, demonstrating significant improvements in readability,
reliability, and understandability. Statistical analyses confirmed the
effectiveness of the PRECISE approach, highlighting its potential to foster
patient-centric care delivery in healthcare decision-making.

使用 OpenAI 的 GPT-4 改进病人参与度，提供更易读和易理解的六年级读物水平的胸部 X 光报告的 PRECISE 框架，在 500 份报告上进行测试，证明了在可读性、可靠性和可理解性方面的显著改进。统计分析结果确认了 PRECISE 方法的有效性，并强调其在医疗决策中促进以患者为中心的护理交付的潜力。

PRECISE 框架：基于 GPT 的文本，改进了放射学报告的可读性、可靠性和可理解性，以实现以患者为中心的护理

PRECISE Framework: GPT-based Text For Improved Readability, Reliability,  and Understandability of Radiology Reports For Patient-Centered Care

Patients who effectively manage their symptoms often demonstrate higher
levels of engagement in conversations and interventions with healthcare
practitioners. This engagement is multifaceted, encompassing cognitive and
socio-affective dimensions. Consequently, it is crucial for AI systems to
understand the engagement in natural conversations between patients and
practitioners to better contribute toward patient care. In this paper, we
present a novel dataset (MedNgage), which consists of patient-nurse
conversations about cancer symptom management. We manually annotate the dataset
with a novel framework of categories of patient engagement from two different
angles, namely: i) socio-affective engagement (3.1K spans), and ii) cognitive
engagement (1.8K spans). Through statistical analysis of the data that is
annotated using our framework, we show a positive correlation between patient
symptom management outcomes and their engagement in conversations.
Additionally, we demonstrate that pre-trained transformer models fine-tuned on
our dataset can reliably predict engagement categories in patient-nurse
conversations. Lastly, we use LIME (Ribeiro et al., 2016) to analyze the
underlying challenges of the tasks that state-of-the-art transformer models
encounter. The de-identified data is available for research purposes upon
request.

本文提出了一个包含患者 - 护士之间有关癌症症状管理的对话的新数据集（MedNgage）及其注释框架。研究发现，患者的症状管理结果与其在对话中的参与度呈正相关，使用该数据集 fine-tuned 的预训练 transformer 模型可可靠地预测患者 - 护士对话的参与类别，并使用 LIME 分析了现有的 transformer 模型可能遇到的一些挑战。