SynthesisFrontiers in public health2026
Scientific and technological mapping of big data architectures in healthcare.
Synthesis in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Context: 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. Objective: 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. Method: A systematic mapping was conducted to identify primary studies in the literature and collect evidence to guide future research and technological adaptations. Results: 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. Conclusions: 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.