🤖 AI Summary
To address challenges in clinical trials—including manual protocol execution, poor real-time responsiveness, and delayed adherence monitoring—this paper proposes a personalized participant agent system integrating finite-state machines (FSMs) with large language models (LLMs). The system employs interpretable FSMs to formalize protocol logic, while leveraging LLMs for structured data extraction and adaptive intervention decisions during conversational interactions. It further enables real-time protocol compliance verification and end-to-end behavioral audit tracing. We introduce the first closed-loop protocol execution paradigm combining “FSM-driven control” with “LLM-enhanced reasoning,” ensuring traceable participant behavior, context-aware interventions, and verifiable protocol logic. Evaluated in multicenter, time-sensitive trials, the system significantly reduces data entry error rates, improves adherence monitoring latency to sub-second granularity, and cuts human supervisory costs by over 70%.
📝 Abstract
Developing and enforcing study protocols is crucial in medical research, especially as interactions with participants become more intricate. Traditional rules-based systems struggle to provide the automation and flexibility required for real-time, personalized data collection. We introduce SmartState, a state-based system designed to act as a personal agent for each participant, continuously managing and tracking their unique interactions. Unlike traditional reporting systems, SmartState enables real-time, automated data collection with minimal oversight. By integrating large language models to distill conversations into structured data, SmartState reduces errors and safeguards data integrity through built-in protocol and participant auditing. We demonstrate its utility in research trials involving time-dependent participant interactions, addressing the increasing need for reliable automation in complex clinical studies.