%0 Journal Article
%@ 2561-326X
%I JMIR Publications
%V 6
%N 2
%P e32230
%T Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study
%A Zhang,Nasen Jonathan
%A Rameau,Philippe
%A Julemis,Marsophia
%A Liu,Yan
%A Solomon,Jeffrey
%A Khan,Sundas
%A McGinn,Thomas
%A Richardson,Safiya
%+ Northwell Health, 600 Community Dr, Manhasset, NY, 11020, United States, 1 (516) 470 3377, nasenz@gmail.com
%K health informatics
%K pulmonary embolism
%K electronic health record
%K quality improvement
%K clinical decision support systems
%D 2022
%7 28.2.2022
%9 Original Paper
%J JMIR Form Res
%G English
%X 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. 
%M 35225812
%R 10.2196/32230
%U https://formative.jmir.org/2022/2/e32230
%U https://doi.org/10.2196/32230
%U http://www.ncbi.nlm.nih.gov/pubmed/35225812