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Appl Clin Inform 2015; 06(02): 318-333
DOI: 10.4338/ACI-2014-12-RA-0116
Research Article
Schattauer GmbH

The accuracy of an electronic Pulmonary Embolism Severity Index auto-populated from the electronic health record

Setting the stage for computerized clinical decision support
D.R. Vinson
1   The Permanente Medical Group, Oakland, California
2   Department of Emergency Medicine, Kaiser Permanente Roseville Medical Center, Roseville, California
3   Kaiser Permanente Division of Research, Oakland, California
,
J.E. Morley
4   Department of Emergency Medicine, University of California Davis School of Medicine, Sacramento, California
,
J. Huang
3   Kaiser Permanente Division of Research, Oakland, California
,
V. Liu
1   The Permanente Medical Group, Oakland, California
3   Kaiser Permanente Division of Research, Oakland, California
5   Department of Pulmonary and Critical Care Medicine, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California
,
M.L. Anderson
1   The Permanente Medical Group, Oakland, California
2   Department of Emergency Medicine, Kaiser Permanente Roseville Medical Center, Roseville, California
,
C. E. Drenten
6   Department of Emergency Medicine, Sutter General Medical Center, Sacramento, California
,
R.P. Radecki
7   Department of Emergency Medicine, The University of Texas Medical School, Houston, Texas
,
D.K. Nishijima
4   Department of Emergency Medicine, University of California Davis School of Medicine, Sacramento, California
,
M.E. Reed
3   Kaiser Permanente Division of Research, Oakland, California
,
the Kaisers Permanente CREST Network › Author Affiliations
Further Information

Publication History

received: 16 December 2014

accepted: 27 March 2015

Publication Date:
19 December 2017 (online)

Also available at
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Summary

Background: The Pulmonary Embolism (PE) Severity Index identifies emergency department (ED) patients with acute PE that can be safely managed without hospitalization. However, the Index comprises 11 weighted variables, complexity that can impede its integration into contextual work-flow.

Objective: We designed a computerized version of the PE Severity Index (e-Index) to automatically extract the required variables from discrete fields in the electronic health record (EHR). We tested the e-Index on the study population to determine its accuracy compared with a gold standard generated by physician abstraction of the EHR on manual chart review.

Methods: This retrospective cohort study included adults with objectively-confirmed acute PE in four community EDs from 2010–2012. Outcomes included performance characteristics of the e-Index for individual values, the number of cases requiring physician editing, and the accuracy of the e-Index risk category (low vs. higher).

Results: For the 593 eligible patients, there were 6,523 values automatically extracted. Fifty one of these needed physician editing, yielding an accuracy at the value-level of 99.2% (95% confidence interval [CI], 99.0%-99.4%). Sensitivity was 96.9% (95% CI, 96.0%-97.9%) and specificity was 99.8% (95% CI, 99.7%-99.9%). The 51 corrected values were distributed among 47 cases: 43 cases required the correction of one variable and four cases required the correction of two. At the risk-category level, the e-Index had an accuracy of 96.8% (95% CI, 95.0%-98.0%), under-classifying 16 higher-risk cases (2.7%) and over-classifying 3 low-risk cases (0.5%).

Conclusion: Our automated extraction of variables from the EHR for the e-Index demonstrates substantial accuracy, requiring a minimum of physician editing. This should increase user acceptability and implementation success of a computerized clinical decision support system built around the e-Index, and may serve as a model to automate other complex risk stratification instruments.

Citation: Vinson DR, Morley JE, Huang J, Liu V, Anderson ML, Drenten CE, Radecki RP, Nishijima DK, Reed ME. The accuracy of an electronic pulmonary embolism severity index auto-populated from the electronic health record. Appl Clin Inf 2015; 6: 318–333

http://dx.doi.org/10.4338/ACI-2014-12-RA-0116

Keywords

Clinical decision support systems - electronic health record - risk assessment - pulmonary embolism - emergency medicine - data completeness
 

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