ReviewFrontiers in digital health2025
Building a healthcare data warehouse: considerations, opportunities, and challenges.
Review in Frontiers in digital health, 2025. 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
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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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing digitalization of health systems is accelerating the transition towards a new era of data-driven, evidence-based care. This has profound implications for clinical practice, performance evaluation, policy making and biomedical research. At the heart of this transformation lies a healthcare data warehouse (DW), which functions as a critical infrastructure for aggregating, standardizing, and analyzing diverse clinical and administrative data. When well-designed and implemented, DWs provide clinicians with timely access to comprehensive, longitudinal patient data, enabling more informed decision-making, enhancing care quality, and improving outcomes. For researchers, these repositories offer opportunities for population-level analytics, predictive modeling, and large-scale health service research, enabling insights into disease patterns, healthcare utilization, and system inefficiencies. Centralizing clinical and administrative data in a DW allows for more frequent, nuanced analyses, increasing the precision and responsiveness of care. However, developing an effective DW requires careful consideration of system architecture, data governance, and interoperability. These foundational components support the robust ETL/ELT frameworks that ensure data quality, consistency, and readiness for analysis across diverse and evolving data streams. Beyond supporting individual patient care, DWs act as essential drivers of scalable research, operational efficiency, and evidence-based health policy. Their successful implementation marks a pivotal step toward achieving personalized, high-quality, and cost-effective healthcare in the digital transformation age. This paper reviews the existing literature to outline the process of building and implementing a data warehouse, introducing real-world disease-specific applications. BiotherDW connects theoretical frameworks with practical healthcare applications by demonstrating how traditional data warehouse design can be adapted for national-scale digital health infrastructures.
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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 Socratic graph.