ReviewHealthcare (Basel, Switzerland)2026
A Review of Data Engineering in United States Healthcare Infrastructure.
Review in Healthcare (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid advancements in artificial intelligence (AI) and machine learning (ML), the role of data engineering has become increasingly critical due to the growing demands for high-quality, large-scale, and well-structured datasets required to train reliable predictive models. Healthcare is one of the most data-intensive industries and has demonstrated strong potential for AI-driven automation in clinical decision support, diagnostics, and operational efficiency. However, healthcare data is often fragmented across multiple systems, inconsistently formatted, and constrained by privacy and regulatory requirements, creating significant barriers to scalable AI adoption. In this review, we examine recent research on healthcare data engineering and AI applications, focusing on how data pipelines, interoperability, and governance frameworks support or limit real-world deployment. This review examined 68 peer-reviewed studies published between 2018 and 2026 across multiple clinical domains, including oncology, cardiovascular disease, infectious disease, neurological disorders, medical imaging, and algorithmic frameworks for explainability and fairness. The reviewed literature shows that while AI models achieve promising performance across these domains, the lack of standardized data architectures and interoperable infrastructure remains a primary bottleneck. The purpose of this study is to highlight key challenges and emerging solutions in healthcare data engineering and outline the future directions needed to support safe, scalable, and trustworthy AI integration in the United States healthcare system. The intended core contributions of this article are to: (i) identify the need for reliable AI systems for healthcare, (ii) explore challenges associated with implementing AI systems in healthcare from a data engineer's perspective, and (iii) analyze key limitations of data engineering as it applies to the implementation of AI systems in healthcare. It must be noted that one of the key limitations of this narrative review is that the authors mostly used citations from MDPI journals.
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Identifiers
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.