A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments

Abstract

Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely heavily on direct identifiers such as names, emails, or Social Security Numbers (SSNs), making them ineffective under strict privacy regulations like GDPR and HIPAA, where such personally identifiable information (PII) is restricted or masked. In this research, I propose a novel, scalable, multimodal AI framework for detecting duplicates without depending on sensitive information. This system leverages three distinct modalities: semantic embeddings derived from textual fields (names, cities) using pre-trained DistilBERT models, behavioral patterns extracted from user login timestamps, and device metadata encoded through categorical embeddings. These heterogeneous modalities are combined using a late fusion approach and clustered via DBSCAN, an unsupervised density-based algorithm. This proposed model is evaluated against a traditional string-matching baseline on a synthetic CRM dataset specifically designed to reflect privacy-preserving constraints. The multimodal framework demonstrated good performance, achieving a good F1-score by effectively identifying duplicates despite variations and noise inherent in the data. This approach offers a privacy-compliant solution to entity resolution and supports secure digital infrastructure, enhances the reliability of public health analytics, and promotes ethical AI adoption across government and enterprise settings. It is well-suited for integration into national health data modernization efforts, aligning with broader goals of privacy-first innovation.