Schmiedmayer Paul, Rao Adrit, Zagar Philipp, Aalami Lauren, Ravi Vishnu, Zahedivash Aydin, Yao Dong-Han, Fereydooni Arash, Aalami Oliver
Stanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA.
Stanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA.
JACC Adv. 2025 May 14;4(6 Pt 1):101780. doi: 10.1016/j.jacadv.2025.101780.
To improve healthcare quality and empower patients, federal legislation requires nationwide interoperability of electronic health records (EHRs) through Fast Healthcare Interoperability Resources (FHIR) application programming interfaces. Nevertheless, key barriers to patient EHR access-limited functionality, English, and health literacy-persist, impeding equitable access to these benefits.
This study aimed to develop and evaluate a digital health solution to address barriers preventing patient engagement with personal health information, focusing on individuals managing chronic cardiovascular conditions.
We present LLMonFHIR, an open-source mobile application that uses large language models (LLMs) to allow users to "interact" with their health records at any degree of complexity, in various languages, and with bidirectional text-to-speech functionality. In a pilot evaluation, physicians assessed LLMonFHIR responses to queries on 6 SyntheticMass FHIR patient datasets, rating accuracy, understandability, and relevance on a 5-point Likert scale.
A total of 210 LLMonFHIR responses were evaluated by physicians, receiving high median scores for accuracy (5/5), understandability (5/5), and relevance (5/5). Challenges summarizing health conditions and retrieving lab results were noted, with variability in responses and occasional omissions underscoring the need for precise preprocessing of data.
LLMonFHIR's ability to generate responses in multiple languages and at varying levels of complexity, along with its bidirectional text-to-speech functionality, give it the potential to empower individuals with limited functionality, English, and health literacy to access the benefits of patient-accessible EHRs.
为了提高医疗质量并赋予患者权力，联邦立法要求通过快速医疗互操作性资源（FHIR）应用程序编程接口实现全国范围内电子健康记录（EHR）的互操作性。然而，患者访问电子健康记录的关键障碍——功能有限、英语能力以及健康素养——仍然存在，阻碍了公平获取这些益处。
本研究旨在开发和评估一种数字健康解决方案，以解决阻碍患者参与个人健康信息管理的障碍，重点关注管理慢性心血管疾病的个体。
我们展示了LLMonFHIR，这是一款开源移动应用程序，它使用大语言模型（LLM），允许用户以任何复杂程度、使用多种语言并具备双向文本转语音功能与他们的健康记录“交互”。在一项试点评估中，医生评估了LLMonFHIR对6个SyntheticMass FHIR患者数据集查询的回答，在5点李克特量表上对准确性、可理解性和相关性进行评分。
医生共评估了210条LLMonFHIR的回答，在准确性（5/5）、可理解性（5/5）和相关性（5/5）方面获得了较高的中位数分数。注意到在总结健康状况和检索实验室结果方面存在挑战，回答的变异性和偶尔的遗漏凸显了对数据进行精确预处理的必要性。
LLMonFHIR能够以多种语言和不同复杂程度生成回答，以及其双向文本转语音功能，使其有潜力让功能有限、英语能力有限和健康素养有限的个体能够获取患者可访问电子健康记录的益处。
