Personalized Healthcare Recommendation via LLM-Based Patient Memory Modeling and Medical Query Understanding

Abstract

The integration of large language models into healthcare recommender systems represents a paradigm shift in the delivery of personalized medical information and decision support. Traditional clinical recommendation engines often rely on structured electronic health records and static rule-based logic, failing to capture the fluid, longitudinal narrative of a patient’s lived health experience. This paper presents a system-level investigation into a novel architecture that constructs a dynamic patient memory model using the generative and contextual capabilities of large language models, coupled with advanced medical query understanding. Rather than proposing a single algorithm, we examine the structural trade-offs, integration pathways, and socio-technical implications of embedding such memory-augmented models within real-world healthcare infrastructures. The discussion extends to the architecture’s handling of temporal dynamics, multi-modal health signals, and the disambiguation of clinically ambiguous language in patient queries. A central theme is the governance of such systems, addressing fairness across heterogeneous populations, interpretability for clinical stakeholders, and the sustainability of large-scale deployment under computational and energy constraints. Through conceptual analysis and cross-domain comparison with consumer personalization systems, the paper identifies unique challenges in the medical domain, including the irreversibility of harmful recommendations and the imperative of value alignment between patient preferences and evidence-based medicine. We argue that the memory model must not be a passive log but an active, ethically-aware construct that supports continuity of care while respecting privacy boundaries and evolving patient identities. The analysis further explores federated learning paradigms to distribute model training across institutions without sharing sensitive data, and examines policy implications for regulatory approval of continuously learning recommendation agents. The paper concludes with a forward-looking perspective on how patient-centric memory architectures could reshape chronic disease management, remote monitoring, and shared clinical decision-making, ultimately contributing to more resilient and equitable health systems.