Sistema anonimizado de dados a partir de prontuários de saúde.

Abstract

The increasing digitalization of healthcare services and the adoption of electronic health records by healthcare institutions have generated a significant volume of clinical data, representing a valuable resource for academic research and the advancement of evidence-based medicine. The enactment of the General Data Protection Law (GDPL) established a fundamental regulatory framework for protecting citizens' privacy, setting guidelines for the processing of sensitive personal data, including health-related information. In this scenario, it is essential to develop technological solutions that allow for the secondary use of clinical data for research purposes while ensuring full compliance with the data protection principles established by legislation. In this context, this study aims to develop a system for the anonymization of health records, enabling their safe and regulated use in academic research in compliance with the LGPD. The adopted methodology combined a systematic literature review, following the PRISMA method, with the implementation of a system based on Large Language Models (LLMs) and Natural Language Processing (NLP) techniques. The selection of the language model considered requirements for Portuguese language support, data privacy, and local processing capacity, resulting in the adoption of the Gemma 3 4B model. The system implements a two-stage anonymization pipeline, using Named Entity Recognition (NER) techniques to identify and suppress five categories of personal identifiers present in medical records: personal names, ages, service dates, geographic locations, and organizations. For structured data, deterministic encryption (BLAKE2b) is applied for the pseudonymization of identifiers and date transformation, preserving temporal relationships and data consistency. Additionally, an LGPD-based audit prompt acts as a verification layer to mitigate false negatives. The result is an anonymized database that protects patient privacy while preserving data utility for academic analysis, contributing to the advancement of healthcare research by facilitating the secure sharing of information between institutions and researchers.