Scientific and technological mapping of big data architectures in healthcare

The growth of data in healthcare brings both challenges and opportunities. The term Big Data refers to the handling of large volumes of data using advanced techniques and scalable infrastructure. Distributed processing and parallel computing accelerate data processing and necessitate a robust architecture. A Big Data architecture requires distributed systems, security, data storage, processing, analysis, and visualization. Crucially, in a Public Health context, such architectures must also incorporate strict data integrity and epidemiological validation to prevent the rapid processing of biased data.

This work aims to identify and characterize the approaches, concepts, and software tools used in constructing a general Big Data architecture for healthcare, exploring how these generalized frameworks can be adapted for specific unmet needs, such as health auditing environments, with a focus on performance, scalability, and structural data validity.

A systematic mapping was conducted to identify primary studies in the literature and collect evidence to guide future research and technological adaptations.

A total of 234 articles were analyzed, with Scopus and ACM Digital Library being the most relevant databases. A total of 22 articles were selected after applying inclusion and exclusion criteria and conducting a quality assessment. The most addressed layers were data storage, processing, ingestion, analysis, and visualization. However, a notable gap regarding automated statistical consistency layers was identified, highlighting the need for a post-hoc theoretical proposition of a validation layer. Various software tools were cataloged and grouped by their architectural function. Additionally, comparisons were made between similar software tools, and essential concepts for creating a Big Data architecture were discussed.

The research presented a comprehensive catalog of relevant information for creating a Big Data architecture for healthcare. The study highlights that while foundational building blocks are available, they must be adapted to develop a scalable, high-performance, and analytically rigorous architecture specifically for healthcare auditing, aligning technical infrastructure with methodological validation.