Prompt-to-policy: Leveraging large language models to guide deep reinforcement learning in public health emergencies

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Cost reductions achieved (specific values not provided in abstract)

• Primary Outcome: Improved resource allocation efficiency in public health emergencies

• Nnt Or Sensitivity: Enhanced accuracy, recall, and F1 scores compared to manual and regex methods

• Confidence Interval: Not reported

Clinical Application

Requires significant computational infrastructure and integration with existing health information systems Potential application to Australian Department of Health emergency response systems, but would require validation against Australian outbreak data and integration with existing surveillance systems Public health authorities managing emergency resource allocation during infectious disease outbreaks

Abstract

Rapid and effective decision-making is critical in public health emergencies, where resource allocation must balance multiple objectives under uncertain conditions. Traditional optimization methods often struggle with computational complexity and real-time, heterogeneous data. To address these challenges, we propose a hybrid intelligent agent combining an enhanced Double Deep Q-Network (D2QN-JDA) with large language models (LLMs). The D2QN-JDA improves learning stability and adaptability through joint state-action inputs, dynamic exploration rate, and adaptive reward normalization. The LLM component uses Retrieval-Augmented Generation (RAG) to integrate structured and unstructured data for real-time decision support. Experiments based on data from the Hong Kong COVID-19 outbreak show that the D2QN-JDA outperforms dynamic programming, greedy algorithms, genetic algorithms, and Q-learning, achieving reductions in cost. The LLM component also outperforms manual and regex methods in both single- and multi-point data extraction, enhancing accuracy, recall, F1 score, cost, and time. Our framework effectively addresses complex, multi-objective resource allocation in public health crises.