OntoDrug: Enhancing Brazilian Health System Interoperability with a National Medication Ontology

This paper presents OntoDrug, an ontology designed to enhance medicine management in Brazil by integrating regulatory frameworks and standardizing terminologies. OntoDrug improves patient safety and treatment efficacy by accurately identifying and classifying medications and supporting interoperability with health information systems. A proof-of-concept application integrated into the Hospital das Clínicas de Marília’s hospital EHR system demonstrated OntoDrug’s utility, achieving high precision and recall. An experimental study using large language models grounded on the ontology achieved, using GPT-4 turbo, 0.97 precision, 1.0 recall and an F1-score of 0.99. We also evaluated open-source models llama3-8b, llama3-70b, and gemma-7b-it. Their performance was close to GPT-4’s. The significant effectiveness is primarily due to the utilization of large language models (LLMs). While using these large language models enhanced performance, challenges related to cost, privacy, and service availability were identified. OntoDrug represents a significant advancement in Brazil’s medication information standardization and optimization.