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On Demand: Taking the Device Clinic Into the 21st ...
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The presentation argues that the modern device clinic has evolved from a simple remote-monitoring inbox into a high-volume, enterprise-level clinical operation. What began as scheduled transmissions reviewed manually in a single vendor portal has become a constant stream of planned and unplanned data from multiple manufacturers, creating a workflow challenge focused on finding what matters and acting reliably.<br /><br />The speakers describe a shift from reactive, transmission-focused volume management to a structured, team-based, data-driven model centered on patients. Using analytics and a shared platform, clinics can identify which patients and alert patterns generate the most non-actionable work, reduce alert burden, and free clinician time for higher-value care. Examples include reducing non-actionable alerts, improving EP visit readiness, and managing advisory populations and CRT optimization pathways more systematically.<br /><br />A major theme is operational sustainability. The clinic must be built around governance, standardized protocols, automation, performance management, and scalable workflows. The same data used for patient care should also guide management decisions such as safety, capacity, connectivity, revenue integrity, workforce risk, and enterprise adoption. Standardization is presented as essential for both patient safety and financial performance.<br /><br />The talk also emphasizes that geography should not limit access to specialized device expertise. A scalable enterprise model can extend consistent care across many campuses and markets while preserving local relationships. The future device clinic is framed as a strategic asset that combines clinical intelligence, operational discipline, and technology to ensure the right information reaches the right team in the right workflow at the right time.
Keywords
device clinic
remote monitoring
clinical workflow
alert burden
analytics
standardized protocols
enterprise care
patient-centered model
operational sustainability
workforce risk
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