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Case study

Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project

Authors:

Emily Beth Devine ,

University of Washington - Seattle Campus
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Daniel Capurro,

University of Washington
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Erik van Eaton,

University of Washington
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Rafae Alfonso-Cristancho,

University of Washington
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Allison Devlin,

University of Washington
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N David Yanez,

University of Washington
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Meliha Yetisgen-Yildiz,

University of Washington
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David R. Flum,

University of Washington
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Peter Tarczy-Hornoch

University of Washington
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Abstract

Background: The field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems.

Objective: Investigators from Washington State’s Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure.

Methods: SCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm’s Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill ‘go-live’ at each site.

Progress to Date: A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15-18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted – 25% from structured data; 25% from text mining.

Conclusion: In semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.

 

How to Cite: Beth Devine E, Capurro D, van Eaton E, Alfonso-Cristancho R, Devlin A, Yanez ND, et al.. Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2013;1(1):16. DOI: http://doi.org/10.13063/2327-9214.1025
Published on 09 Oct 2013.
Peer Reviewed

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