Intelligent Detection and Protection of Personally Identifiable Information in Clinical Text: An Advanced NLP Approach with Optimized Attention Mechanisms

Abstract

The protection of Personally Identifiable Information (PII) in clinical text is a critical challenge in healthcare data management, particularly as medical institutions increasingly adopt digital health records and data-sharing initiatives. This paper presents a novel natural language processing framework that leverages optimized attention mechanisms and context-aware tokenization strategies to achieve high accuracy in detecting and protecting sensitive information within clinical documents. Our approach integrates transformer-based architectures with domain-specific enhancements, achieving a 95.3% F1-score on standard benchmarks while satisfying HIPAA Safe Harbor requirements through a combination of deep learning and rule-based processing. The proposed method introduces a hierarchical detection system that processes clinical text at multiple granularity levels, employing specialized attention heads for different PII categories. Experimental results on three large-scale clinical datasets demonstrate that our framework outperforms existing state-of-the-art methods by 8.7% in detection accuracy and reduces false positives by 59% compared to ClinicalBERT (from 12.8% to 5.2%). Furthermore, our intelligent redaction strategy preserves the semantic integrity of clinical content, enabling secure data sharing while maintaining the utility of medical information.