Tokenization techniques for privacy-preserving healthcare data: tokenization nuts and bolts
Year: 2025
Type: article
Abstract: Tokenization is a crucial technology for ensuring the security and privacy of patient data in clinical research, pharmacovigilance, and drug safety monitoring. As healthcare increasingly integrates diverse data sources-ranging from clinical records to non-clinical data such as social determinants of health (SDOH)-it is essential to protect sensitive patient information while improving data quality and analysis (National Institutes of Health, 2006). This article emphasizes tokenization's critical role in safeguarding privacy, particularly in pharmacovigilance activities including safety monitoring, risk assessment, and post-market surveillance. Beyond security, tokenization enriches research datasets by enabling integration of external information, thereby enhancing the rigor and reliability of pharmacovigilance outcomes. With effective tokenization, researchers can better protect patients while gaining deeper insights into clinical and pharmacological research (Cruz et al., 2024). Recent global applications validate tokenization as a foundational privacy-preserving technology in pharmacovigilance. An applied example from a psoriasis clinical trial demonstrated referential tokenization's capacity to securely link electronic health records (EHRs) and claims data across systems with greater than 99% linkage precision while maintaining privacy standards (D'Andrea et al., 2024). These capabilities align with emerging international frameworks, including the European Health Data Space (2025), reinforcing tokenization's value in generating regulatory-grade evidence for pharmacovigilance across national and multinational research environments. In many jurisdictions, tokenization that meets de-identification or pseudonymization standards may not require individual patient consent, though this varies based on data sensitivity, jurisdictional law, and the study's intent (Office for Civil Rights, 2023; EDPB, 2021).
Author: Camille V. Cook
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W4417449054