Re-Thinking the Nature of Planning for Safe and Personalized Treatment Management Planning Using Large Language Models

Abstract

While large language models have advanced di-agnostic reasoning in clinical domains, but to fully support the patient care, accurate and personalized treatment (illness) management plans are also needed. Unlike conventional planning tasks with defined goals and constraints, illness management planning navigates through uncertainty, incomplete data, and nonlinear, cross-disease effects. As a result, existing methods (e.g., Chain-of- Thought and Reflexion), which rely on assumptions of defined goals and linear reasoning, fall short in the complexities and ambiguities inherent in illness management planning. Another major challenge is the lack of a high-quality dataset that pairs real-world patient narratives with actionable illness management plans, which are crucial for evaluating the capabilities of LLMs in illness management planning. To address these challenges, we propose a novel planning method that reconceives treatment planning as the modulation of a patient's current illness state toward a healthy state without any defined goal. The approach introduces Attractive Tendencies, a latent, directional vectors that define desirable shifts toward healthier states, then uses field mapping to identify modifiable life domains that define personalized illness management goals, and applies field sculpting to generate safe, individualized, and actionable interventions. To enable standardized evaluation, we release an evaluation dataset comprising 1,015 patient cases, each paired with a real-world, complex narrative and a personalized treatment plan. Our method outperforms baseline approaches, achieving improvements of up to +12 BLEU-4 and +11 METEOR, while maintaining strong clinical relevance and computational efficiency.

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