The Anatomy of a Personal Health Agent

2025-08-27

AI Summary

In non-clinical, everyday settings, individuals exhibit diverse health needs, while multi-source, heterogeneous health data—such as wearable sensor streams and electronic health records—remain difficult to jointly interpret and act upon. Method: This paper proposes PHA, a novel multi-agent collaborative framework featuring three specialized agents: a Data Science Agent, a Health Domain Expert Agent, and a Health Coach Agent. PHA integrates large language models, time-series analytics, user-centered design, and behavioral science principles to enable empathetic, psychology-informed interaction and longitudinal health monitoring. Contribution/Results: Evaluated across 10 benchmark tasks via automated and human assessment—including over 7,000 annotated samples and 1,100 expert- and user-hours—the framework demonstrates significant improvements in personalized health understanding, reasoning, and proactive intervention. PHA establishes a new paradigm for systematic, practical personal health assistants.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Teamwork
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Health is a fundamental pillar of human wellness, and the rapid advancements in large language models (LLMs) have driven the development of a new generation of health agents. However, the application of health agents to fulfill the diverse needs of individuals in daily non-clinical settings is underexplored. In this work, we aim to build a comprehensive personal health agent that is able to reason about multimodal data from everyday consumer wellness devices and common personal health records, and provide personalized health recommendations. To understand end-users' needs when interacting with such an assistant, we conducted an in-depth analysis of web search and health forum queries, alongside qualitative insights from users and health experts gathered through a user-centered design process. Based on these findings, we identified three major categories of consumer health needs, each of which is supported by a specialist sub-agent: (1) a data science agent that analyzes personal time-series wearable and health record data, (2) a health domain expert agent that integrates users' health and contextual data to generate accurate, personalized insights, and (3) a health coach agent that synthesizes data insights, guiding users using a specified psychological strategy and tracking users' progress. Furthermore, we propose and develop the Personal Health Agent (PHA), a multi-agent framework that enables dynamic, personalized interactions to address individual health needs. To evaluate each sub-agent and the multi-agent system, we conducted automated and human evaluations across 10 benchmark tasks, involving more than 7,000 annotations and 1,100 hours of effort from health experts and end-users. Our work represents the most comprehensive evaluation of a health agent to date and establishes a strong foundation towards the futuristic vision of a personal health agent accessible to everyone.

Problem

• Developing a comprehensive personal health agent for daily non-clinical settings
• Addressing diverse consumer health needs through specialized sub-agents
• Enabling personalized health recommendations using multimodal data analysis

Innovation

• Multi-agent framework for personalized health interactions
• Specialist sub-agents analyzing multimodal wellness data
• Comprehensive evaluation with automated and human assessments