@Article{info:doi/10.2196/32230,
author="Zhang, Nasen Jonathan
and Rameau, Philippe
and Julemis, Marsophia
and Liu, Yan
and Solomon, Jeffrey
and Khan, Sundas
and McGinn, Thomas
and Richardson, Safiya",
title="Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study",
journal="JMIR Form Res",
year="2022",
month="Feb",
day="28",
volume="6",
number="2",
pages="e32230",
keywords="health informatics; pulmonary embolism; electronic health record; quality improvement; clinical decision support systems",
abstract="Background: Computed tomography pulmonary angiography (CTPA) is frequently used in the emergency department (ED) for the diagnosis of pulmonary embolism (PE), while posing risk for contrast-induced nephropathy and radiation-induced malignancy. Objective: We aimed to create an automated process to calculate the Wells score for pulmonary embolism for patients in the ED, which could potentially reduce unnecessary CTPA testing. Methods: We designed an automated process using electronic health records data elements, including using a combinatorial keyword search method to query free-text fields, and calculated automated Wells scores for a sample of all adult ED encounters that resulted in a CTPA study for PE at 2 tertiary care hospitals in New York, over a 2-month period. To validate the automated process, the scores were compared to those derived from a 2-clinician chart review. Results: A total of 202 ED encounters resulted in a completed CTPA to form the retrospective study cohort. Patients classified as ``PE likely'' by the automated process (126/202, 62{\%}) had a PE prevalence of 15.9{\%}, whereas those classified as ``PE unlikely'' (76/202, 38{\%}; Wells score >4) had a PE prevalence of 7.9{\%}. With respect to classification of the patient as ``PE likely,'' the automated process achieved an accuracy of 92.1{\%} when compared with the chart review, with sensitivity, specificity, positive predictive value, and negative predictive value of 93{\%}, 90.5{\%}, 94.4{\%}, and 88.2{\%}, respectively. Conclusions: This was a successful development and validation of an automated process using electronic health records data elements, including free-text fields, to classify risk for PE in ED visits. ",
issn="2561-326X",
doi="10.2196/32230",
url="https://formative.jmir.org/2022/2/e32230",
url="https://doi.org/10.2196/32230",
url="http://www.ncbi.nlm.nih.gov/pubmed/35225812"
}