Publications by authors named "K Grauer"
Article Synopsis
- ECG interpretation skills among healthcare professionals are often lacking, creating a need for effective learning resources.
- A study involving 1206 professionals tested the effectiveness of web-based training methods, with participants divided into four groups: question bank, lecture, hybrid, and control (no resources).
- Results showed significant improvements in ECG interpretation among those using learning resources, with the question bank and hybrid groups performing especially well, while the control group saw minimal gains.
Background: Electrocardiogram (ECG) interpretation training is a fundamental component of medical education across disciplines. However, the skill of interpreting ECGs is not universal among medical graduates, and numerous barriers and challenges exist in medical training and clinical practice. An evidence-based and widely accessible learning solution is needed.
View Article and Find Full Text PDFECG interpretation is essential in modern medicine, yet achieving and maintaining competency can be challenging for healthcare professionals. Quantifying proficiency gaps can inform educational interventions for addressing these challenges. Medical professionals from diverse disciplines and training levels interpreted 30 12-lead ECGs with common urgent and nonurgent findings.
View Article and Find Full Text PDFBackground: Cardiologs® has developed the first electrocardiogram (ECG) algorithm that uses a deep neural network (DNN) for full 12‑lead ECG analysis, including rhythm, QRS and ST-T-U waves. We compared the accuracy of the first version of Cardiologs® DNN algorithm to the Mortara/Veritas® conventional algorithm in emergency department (ED) ECGs.
Methods: Individual ECG diagnoses were prospectively mapped to one of 16 pre-specified groups of ECG diagnoses, which were further classified as "major" ECG abnormality or not.