Data / Health Information Management
A standardized, evidence-based framework for managing CIED remote monitoring alerts in outpatient device clinics. Includes a color-coded triage algorithm (RED/YELLOW/GREEN), complete SOP, staff training materials, and interactive metrics dashboard that…
This presentation was developed for the cardiology nurses and medical assistants in our cardiology office to better understand how our deparment runs and the daily tasks of each role. Engagement…
This algorithm is intended to provide a standardized framework for in-clinic and remote follow-up of permanent pacemakers. It supports systematic assessment of patient symptoms, device function, programming optimization, safety surveillance,…
Journal Articles: Atrial fibrillation (AF) is the most common cardiac arrhythmia diagnosed and treated in the world. The treatment of patients’ symptoms as well as the prevention of stroke and heart failure is dependent on accurate detection and characterization of AF. A variety of electrocardiographic (ECG) monitoring techniques are being used for these purposes. However, these intermittent ECG monitoring techniques have been shown to underdiagnose AF events while having limited ability to characterize AF burden and density. Continuous long-term implantable loop recorder (ILR)–based ECG monitoring has been designed to overcome these limitations. This technology is being increasingly used to diagnose episodes of AF in high-risk patients and to improve characterization of AF episodes in patients with known AF. This review aims to review the potential clinical utility of ILR-based ECG monitoring while highlighting some inherent limitations of the current technology. An understanding of these limitations is important when considering the use of ILR-based ECG monitoring and clinical decision making based on the information being stored within these devices.
Journal Articles: Quality improvement (QI) is based on a methodology of baseline measurement, intervention, and remeasurement/analysis, with iterative improvement cycles along the way. The accuracy of the data used for measuring and remeasuring quality is therefore paramount, which leads to the question: What if the data are not accurate?