ArticleApplied clinical informatics2015
The Accuracy of an Electronic Pulmonary Embolism Severity Index Auto-Populated from the Electronic Health Record: Setting the stage for computerized clinical decision support.
Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- A stepped wedge cluster randomized implementation trial to increase outpatient management of low-risk pulmonary embolism from the emergency department - the MEDIC ALERT PE study.Implementation science communications · 2025Article
- Performance of an Electronic Health Record-Based Automated Pulmonary Embolism Severity Index Score Calculator: Cohort Study in the Emergency Department.JMIR medical informatics · 2025Article
- Bilateral Emboli and Highest Heart Rate Predict Hospitalization of Emergency Department Patients With Acute, Low-Risk Pulmonary Embolism.Annals of emergency medicine · 2023Article
- Article
- Sustainability of a Clinical Decision Support Intervention for Outpatient Care for Emergency Department Patients With Acute Pulmonary Embolism.JAMA network open · 2022Article
- Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study.JMIR formative research · 2022Article
- Presyncope Is Associated with Intensive Care Unit Admission in Emergency Department Patients with Acute Pulmonary Embolism.The western journal of emergency medicine · 2020Article
- Text message alerts to emergency physicians identifying potential study candidates increase clinical trial enrollment.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Automated Pulmonary Embolism Risk Classification and Guideline Adherence for Computed Tomography Pulmonary Angiography Ordering.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2018Article
- Automated versus Manual Data Extraction of the Padua Prediction Score for Venous Thromboembolism Risk in Hospitalized Older Adults.Applied clinical informatics · 2018Article
- Thromboprophylaxis for Patients with High-risk Atrial Fibrillation and Flutter Discharged from the Emergency Department.The western journal of emergency medicine · 2018Article
- Automating Clinical Score Calculation within the Electronic Health Record. A Feasibility Assessment.Applied clinical informatics · 2017Article
- Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.American journal of preventive medicine · 2016Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe 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 workflow.
objectiveWe 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.
methodsThis 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).
resultsFor 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%).
conclusionOur 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.
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