Agentic LLM for anonymizing healthcare data with contextual awareness

Abstract

Large language models (LLMs) are transforming healthcare applications by enhancing data analysis, yet their adoption remains constrained by stringent privacy requirements that limit access to sensitive medical data. Anonymization offers a pathway to address this challenge; however, existing techniques often lack contextual understanding and exhibit low accuracy, compromising either patient privacy or the utility of clinical content. In this paper, we propose an end-to-end, modular agentic LLM system for processing sensitive healthcare data with contextual awareness. The system is orchestrated by a central agent coordinating specialized components for structured data retrieval, clinical narrative generation, anonymization, public LLM querying, and secure deanonymization. Locally hosted LLMs handle all privacy-sensitive steps, including context generation and anonymization, while public LLMs are used exclusively for reasoning on pre-anonymized inputs. We evaluate our system on a synthetic clinical dataset and benchmark it against five state-of-the-art named entity recognition (NER) techniques. Our approach achieves high precision and a recall of 93.6%, significantly outperforming baselines such as spaCy (33% precision, 89% recall) and Presidio (41% precision, 90% recall). Additional evaluation on real-world clinical notes from the MIMIC-III dataset demonstrates strong generalization to unstructured narratives, achieving a clean transformation rate of 98.6%. We further evaluate medical richness, showing that anonymized outputs retain clinically relevant information and semantic structure, preserving downstream utility. Adversarial re-identification experiments confirm that no true identifiers can be reconstructed, highlighting the framework’s effectiveness in balancing privacy, robustness, and clinical usefulness.

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