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The Analytic Information Warehouse (AIW): A platform for analytics using electronic health record data
Journal article   Open access   Peer reviewed

The Analytic Information Warehouse (AIW): A platform for analytics using electronic health record data

Andrew R. Post, Tahsin Kurc, Sharath Cholleti, Jingjing Gao, Xia Lin, William Bornstein, Dedra Cantrell, David Levine, Sam Hohmann and Joel H. Saltz
Journal of biomedical informatics, v 46(3), pp 410-424
01 Jun 2013
DOI: https://doi.org/10.1016/j.jbi.2013.01.005
PMID: 23402960
url
https://doi.org/10.1016/j.jbi.2013.01.005View
Published, Version of Record (VoR)Open Access (Publisher-Specific),  Open

Abstract

Clinical data warehousing Comparative effectiveness Healthcare analytics Quality improvement Temporal abstraction
► We propose a temporal abstraction-based methodology for healthcare analytics. ► We implemented this methodology in software and deployed it in production. ► We identified in EHR data clinical phenotypes associated with hospital readmissions. ► Temporal abstraction is a scalable and flexible method for clinical phenotyping. ► Clinical phenotyping helps leverage EHR data in quality improvement analyses. To create an analytics platform for specifying and detecting clinical phenotypes and other derived variables in electronic health record (EHR) data for quality improvement investigations. We have developed an architecture for an Analytic Information Warehouse (AIW). It supports transforming data represented in different physical schemas into a common data model, specifying derived variables in terms of the common model to enable their reuse, computing derived variables while enforcing invariants and ensuring correctness and consistency of data transformations, long-term curation of derived data, and export of derived data into standard analysis tools. It includes software that implements these features and a computing environment that enables secure high-performance access to and processing of large datasets extracted from EHRs. We have implemented and deployed the architecture in production locally. The software is available as open source. We have used it as part of hospital operations in a project to reduce rates of hospital readmission within 30days. The project examined the association of over 100 derived variables representing disease and co-morbidity phenotypes with readmissions in 5years of data from our institution’s clinical data warehouse and the UHC Clinical Database (CDB). The CDB contains administrative data from over 200 hospitals that are in academic medical centers or affiliated with such centers. A widely available platform for managing and detecting phenotypes in EHR data could accelerate the use of such data in quality improvement and comparative effectiveness studies.

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26 citations in Web of Science
30 citations in Scopus

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Web of Science research areas
Computer Science, Interdisciplinary Applications
Medical Informatics
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