Uma arquitetura big data inteligente para auditoria na saúde

Abstract

Context: The auditing of public health procurement, particularly for Orthotics, Prosthetics, and Special Materials (OPME), is a critical process hindered by data inconsistency in documents such as invoices. This lack of standardization leads to inefficiencies and creates vulnerabilities for irregularities. Although Artificial Intelligence (AI) solutions have already demonstrated effectiveness in small-scale item classification, their nationwide application is impeded by the absence of an infrastructure capable of processing a massive volume of data in a scalable, robust, and economically viable manner. Objective: This work aims to overcome this barrier on two fronts: first, to characterize the state of the art in Big Data architectures for healthcare, identifying the fundamental approaches, tools, and concepts for their construction; second, to present ALIAS (Architecture for Large-scale Intelligent Auditing of Healthcare Systems), a detailed, replicable, and open-source technical blueprint designed to democratize the development of data platforms and support the complete lifecycle of AI solutions (MLOps). Method: The methodology adopted a two-phase approach. First, a Systematic Literature Mapping consolidated the state of the art. These findings then guided the design, implementation, and evaluation of the ALIAS blueprint. Its effectiveness and applicability were investigated through a concrete case study: the large-scale classification of OPME items from invoices, scaling a pre-existing AI solution to a nationwide data volume and seeking evidence of its performance in a real-world auditing scenario. Results: The systematic mapping analyzed 219 articles and selected 16 primary studies, which guided the design of ALIAS. The practical application of the architecture overcame previous processing barriers, enabling large-scale analysis that had previously failed due to memory exhaustion. The adoption of the Parquet format reduced storage by approximately 80%, and data partitioning accelerated queries by orders of magnitude. Crucially, the architecture established an efficient MLOps workflow, which democratized access to distributed analysis and drastically reduced the cycle between experimentation and production. Conclusion: The research presents evidence that the ALIAS blueprint constitutes a robust and financially viable solution for public institutions to implement their own data analysis and AI platforms. By promoting technological sovereignty and offering a practical guide whose effectiveness has been demonstrated, this work empowers organizations to optimize complex processes such as OPME auditing, establishing the foundation for future innovations and ensuring greater transparency, efficiency, and quality in public health management.