The Analytic Information Warehouse (AIW): a Platform for Analytics using Electronic Health Record Data

• Type of Material: Article
• Authors: Andrew Post, Emory University; Tahsin Kurc, Emory University; Sharath Cholleti, Emory University; Jingjing Gao, Emory University; Xia Lin, Emory University; William A Bornstein, Emory University; Dedra Cantrell, Emory Healthcare; David Levine, UHC; Sam Hohmann, UHC; Joel H Saltz, Emory University
• Language: English
• Date: 2013-06
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2013 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1016/j.jbi.2013.01.005
• Title of Journal or Parent Work: Journal of Biomedical Informatics
• ISSN: 1532-0464
• Volume: 46
• Issue: 3
• Start Page: 410
• End Page: 424
• Grant/Funding Information: Other than the named authors, the funders of this work had no role in study design, collection, analysis or interpretation of data, writing this manuscript, or in the decision to submit the manuscript for publication.; This work was supported in part by PHS Grant UL1 RR025008, KL2 RR025009 and TL1 RR025010 from the CTSA program, NIH, NCRR; NHLBI grant R24 HL085343; NIH/ARRA grant 325011.300001.80022; M01 RR-00039 from the GCRC program, NIH, NCRR; and Emory Healthcare.
• Keywords: Healthcare analytics; Comparative effectiveness; Temporal abstraction; Clinical data warehousing; Quality improvement
• Research Categories: Health Sciences, Pathology; Engineering, Biomedical

Abstract

Objective 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. Materials and Methods 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. Results 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 30 days. The project examined the association of over 100 derived variables representing disease and co-morbidity phenotypes with readmissions in five years 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. Discussion and Conclusion 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.