Secondary EMR data for quality improvement and research: A comparison of manual and electronic data collection from an integrated critical care electronic medical record system

J Crit Care. 2018 Oct;47:295-301. doi: 10.1016/j.jcrc.2018.07.021. Epub 2018 Jul 22.

Abstract

Purpose: This study measured the quality of data extracted from a clinical information system widely used for critical care quality improvement and research.

Materials and methods: We 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.

Results: Concordance 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.

Conclusions: Manual 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.

Keywords: Data accuracy; Data concordance; Data quality; Electronic medical records; Medical record abstraction; Secondary data use.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • APACHE
  • Adult
  • Critical Care / methods*
  • Electronic Health Records / standards*
  • Female
  • Humans
  • Intensive Care Units*
  • Male
  • Medical Informatics / methods*
  • Middle Aged
  • Quality Assurance, Health Care
  • Quality Improvement*
  • Research Design
  • Retrospective Studies