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TECHNOLOGY AND CODE article

Front. Internet Things
Sec. IoT Services and Applications
Volume 4 - 2025 | doi: 10.3389/friot.2025.1436757

A Smart Classifier of Orthoses, Prostheses and Special Materials (OPMEs) in Invoices

Provisionally accepted
Wesckley Gomes Wesckley Gomes 1*Methanias Colaço Júnior Methanias Colaço Júnior 1*Luca Pareja Credidio Freire Alves Luca Pareja Credidio Freire Alves 2Raphael Fontes Raphael Fontes 2Rodrigo Silva Rodrigo Silva 2Bruno Nunes Bruno Nunes 2Caldeira Silva Caldeira Silva 2Ricardo Valentim Ricardo Valentim 2
  • 1 Federal University of Sergipe, São Cristóvão, Brazil
  • 2 Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil

The final, formatted version of the article will be published soon.

    The 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. Objective: To 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. Method: Controlled Experiment, to evaluate scientifically mapped Artificial Intelligence (AI) algorithms and compare accuracy measures, F1-Score, sensitivity, precision, average training time, and classification. Results: With 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. Conclusion: The 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.

    Keywords: orthoses, Prostheses and Special Materials (OPME), Health, audit, Classification, Artificial intelligence (AI), electronic invoices, Corruption

    Received: 29 Jul 2024; Accepted: 14 Jan 2025.

    Copyright: © 2025 Gomes, Colaço Júnior, Alves, Fontes, Silva, Nunes, Silva and Valentim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Wesckley Gomes, Federal University of Sergipe, São Cristóvão, Brazil
    Methanias Colaço Júnior, Federal University of Sergipe, São Cristóvão, Brazil

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.