AUTHOR=Gomes Wesckley , Colaço Júnior Methanias , Alves Luca Pareja Credidio Freire , Fontes Raphael , Silva Rodrigo , Nunes Bruno , Silva Caldeira , Valentim Ricardo 

TITLE=A smart classifier of orthoses, prostheses and special materials (OPMEs) in invoices

JOURNAL=Frontiers in the Internet of Things

VOLUME=Volume 4 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/the-internet-of-things/articles/10.3389/friot.2025.1436757

DOI=10.3389/friot.2025.1436757

ISSN=2813-3110

ABSTRACT=ContextThe OPME (Órteses, Próteses e Materias Especiais or Orthoses, Prosthetics and Special Materials) Brazilian sector presents a wide variety of products and technologies, involving both multinational and local companies in healthcare. Despite technological advances, many services and information systems, especially in the public sphere, still use unstructured natural language descriptions of products, services or events, making their classification and analysis difficult. However, for efficient audits, it is necessary to classify and totalize invoices issued for product purchases automatically. In this way, the standardization lacking regarding nomenclature in the OPME marketing not only makes it difficult to compare products, whether for price standardization or standardization of use but also opens up space for possible acts of corruption.ObjectiveTo mitigate the problem of ineffective standardization and coding, develop and assess the effectiveness and efficiency of an OPME classifier, in the context of electronic invoice descriptions, from the point of view of auditors, healthcare professionals, and data scientists.MethodControlled Experiment, to evaluate scientifically mapped Artificial Intelligence (AI) algorithms and compare accuracy measures, F1-Score, sensitivity, precision, average training time, and classification.ResultsWith an accuracy of 99%, the Linear Support Vector algorithm stood out among the others in terms of accuracy, while Naïve Bayes in terms of efficiency, had the fastest average training time.ConclusionThe results showed that it is possible to identify and classify OPMEs in invoices automatically. This allows for a more precise and effective analysis of signs such as anomalously high prices and quantities of OPMEs purchased per inhabitant, which are analyzed by the Audit of Brazil’s Unified Health System (AudSUS), Ministry of Health -Brazil, for identification of potential irregularities and contribution to transparency and efficiency in the management of health resources.