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Medical Care:
January 2007 - Volume 45 - Issue 1 - pp 73-79
doi: 10.1097/01.mlr.0000244510.09001.e5
Original Article

Measuring Performance Directly Using the Veterans Health Administration Electronic Medical Record: A Comparison With External Peer Review

Goulet, Joseph L. PhD, MS; Erdos, Joseph MD; Kancir, Sue RN; Levin, Forrest L. MS; Wright, Steven M. PhD; Daniels, Stanlie M. RN; Nilan, Lynnette RN, EdD; Justice, Amy C. MD, PhD

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Abstract

Background: Electronic medical records systems (EMR) contain many directly analyzable data fields that may reduce the need for extensive chart review, thus allowing for performance measures to be assessed on a larger proportion of patients in care.

Objective: This study sought to determine the extent to which selected chart review-based clinical performance measures could be accurately replicated using readily available and directly analyzable EMR data.

Methods: A cross-sectional study using full chart review results from the Veterans Health Administration's External Peer Review Program (EPRP) was merged to EMR data.

Results: Over 80% of the data on these selected measures found in chart review was available in a directly analyzable form in the EMR. The extent of missing EMR data varied by site of care (P < 0.01). Among patients on whom both sources of data were available, we found a high degree of correlation between the 2 sources in the measures assessed (correlations of 0.89-0.98) and in the concordance between the measures using performance cut points (kappa: 0.86-0.99). Furthermore, there was little evidence of bias; the differences in values were not clinically meaningful (difference of 0.9 mg/dL for low-density lipoprotein cholesterol, 1.2 mm Hg for systolic blood pressure, 0.3 mm Hg for diastolic, and no difference for HgbA1c).

Conclusions: Directly analyzable data fields in the EMR can accurately reproduce selected EPRP measures on most patients. We found no evidence of systematic differences in performance values among these with and without directly analyzable data in the EMR.

© 2007 Lippincott Williams & Wilkins, Inc.

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