Geisinger Improves Ultrasound Documentation Time and Billed Volume
In addition to streamlining the use of POCUS at their facilities, Geisinger has also seen an accelerated rate of completed POCUS documentation.
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Detect B-lines, effusions, consolidations, and lung pathology — in real time, at the bedside. FDA-cleared lung insights help distinguish edema from effusions and monitor progression without delay.
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Assessment of an Artificial Intelligence Tool for Estimating LVEF in Echocardiograms
Published in the healthcare journal, Diagnotics, the authors of this article shared the following abstract: This work aims to evaluate the performance of a new artificial intelligence tool (Exo AI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan).
Read MoreWellstar MCG Health Increases Revenue Capture by 47%
Wellstar MCG Health implemented Exo Works®, Exo’s point-of-care ultrasound (POCUS) workflow solution, to accelerate patient care and streamline exam documentation, quality assurance (QA), regulatory compliance, and revenue capture at the medical facility.
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