• Rebecca Brundin-Mather, Andrea Soo, Danny J Zuege, Daniel J Niven, Kirsten Fiest, Christopher J Doig, David Zygun, Jamie M Boyd, Jeanna Parsons Leigh, Sean M Bagshaw, and Henry T Stelfox.
• W21C Research & Innovation Centre, Cumming School of Medicine, University of Calgary, GD01-TRW Building, 3280 Hospital Dr NW, Calgary, AB T2N 4Z6, Canada.
• J Crit Care. 2018 Oct 1; 47: 295-301.

PurposeThis study measured the quality of data extracted from a clinical information system widely used for critical care quality improvement and research.Materials And MethodsWe abstracted data from 30 fields in a random sample of 207 patients admitted to nine adult, medical-surgical intensive care units. We assessed concordance between data collected: (1) manually from the bedside system (eCritical MetaVision) by trained auditors, and (2) electronically from the system data warehouse (eCritical TRACER). Agreement was assessed using Cohen's Kappa for categorical variables and intraclass correlation coefficient (ICC) for continuous variables.ResultsConcordance between data sets was excellent. There was perfect agreement for 11/30 variables (35%). The median Kappa score for the 16 categorical variables was 0.99 (IQR 0.92-1.00). APACHE II had an ICC of 0.936 (0.898-0.960). The lowest concordance was observed for SOFA renal and respiratory components (ICC 0.804 and 0.846, respectively). Score translation errors by the manual auditor were the most common source of data discrepancies.ConclusionsManual validation processes of electronic data are complex in comparison to validation of traditional clinical documentation. This study represents a straightforward approach to validate the use of data repositories to support reliable and efficient use of high quality secondary use data.Copyright © 2018 Elsevier Inc. All rights reserved.

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