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Clinical Data Warehousing: A Scoping Review

Wang, Zhan, Craven, Catherine, Syed, Mahanaz, Greer, Melody, Seker, Emel, Syed, Shorab, Zozus, Meredith Nahm·2024·Journal of the Society for Clinical Data Management
DOI 10.47912/jscdm.320PMC PMC12834563
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Abstract

INTRODUCTION: A clinical data warehouse (CDW) is a powerfulresource that supports clinical decision-making and secondary data use byintegrating and presenting heterogeneous data sources. Despite considerableeffort within healthcare organizations (HCOs) to develop CDWs, scientific literaturesurrounding clinical data warehousing methods is limited.OBJECTIVES: The scoping review aims to characterize thecurrent state of CDW methods within HCOs, to identify extant evidence forpractice recommendations, and ultimately to advance the design, implementation,and use of CDWs. METHODS: The review encompasses CDW articles publishedfrom 2011 through 2021 identified through a systematic PubMed search. Articleabstracts were systematically screened by two authors. Full-text articles werereviewed and abstracted independently by two authors with discrepanciesresolved through consensus. RESULTS: 137 articles, from 55 journals and 3conference proceedings, were categorized and analyzed. Areas for increased CDW focus include dataintegration of increased data types and sources; extract-transform-load (ETL)optimization; data quality improvement processes; semantic data representation;support tools/documentation and data literacy efforts for staff and end-users;data governance; business model/financial support for CDWs including staffing. CONCLUSION: Thestudy indicates the topics that have been significantly developed and theaspects needing additional focus and reporting in CDW between existing generaldata management best practices and recently articulated requirements forresearch data. Also, more multi-site and multi-aspect studies are needed tofoster maturity at CDWs.

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  • The Data Architecture Team uses ETL methods to load diverse data sources, including hospital EMR-sourced demographic, behavioral, and clinical data, geospatial data linked to patients' geographic identifiers, and census-tract level SDoH data.

    “The Data Architecture Team uses Extract, Transform, Load (ETL) methods29 to load diverse data sources, including hospital EMR-sourced demographic, behavioral, and clinical data, geospatial data linked to patients’ geographic identifiers, and census-tract level SDoH data.”

    From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [29]

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Cited by

Sorted by citations

Authors
Wang, Z., Craven, C., Syed, M., Greer, M., Seker, E., Syed, S., & Zozus, M. N.
Affiliations
  • Population Health Sciences, uthscsa
  • Population Health Science and Policy, Icahn School of Medicine
  • Population Health Sciences, University of Texas Health Science Center at San Antonio
  • UAMS
  • UALR
  • Department of Population Health Sciences, University of Texas for Health Sciences San Antonio
  • The Joe R. and Teresa Lozano Long School of Medicine, University of Texas Health Science Center San Antonio
ORCID
Venue
Journal of the Society for Clinical Data Management
Publisher
Society for Clinical Data Management
Published
2024-08-28
Volume
5
Issue
2
ISSN
2694-1473
DOI
10.47912/jscdm.320
PMC
PMC12834563
Cited by
14
References
2
Licence
creativecommons.org/licenses/by-nc-sa/4.0/ (unspecified)

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