Privacy-Preserving Healthcare Data Interactions: A Multi-Agent Approach Using LLMs

Abstract

Integrating Electronic Health Records (EHRs) into clinical workflows is crucial for advancing healthcare delivery but poses significant challenges, especially in improving humanmachine interactions through natural language queries. This study builds on prior research [1] by introducing a multi-agent system that uses Large Language Models (LLMs) for secure interactions with healthcare data. In this extended work, we present a new capability for real-time updates to patient records through the addition of a Data Update Agent (DUA), ensuring privacy, accuracy, and compliance with regulatory standards. Compared to prior work, the system features a dual-pathway design for distinguishing between data retrieval and updates, enhanced modularity for seamless agent upgrades, and robust mechanisms to manage complex scenarios and noisy inputs. These advancements improve scalability, fault tolerance, and adaptability to real-world clinical environments. Comprehensive evaluations have been conducted using diverse clinical scenarios, including tests with noisy inputs and complex queries. The results highlight the system’s scalability, accuracy, and practicality, demonstrating its superiority over baseline methods. The proposed framework enables better integration of LLMs in clinical settings by bridging natural language interfaces with secure, interoperable healthcare data systems.

Keywords