Enabling Scalable Predictive Monitoring and Alarm Analytics via a Real-Time Platform for Processing Continuous Cardiorespiratory Monitoring Data

Presentation Time: 05:30 PM - 06:30 PM

Abstract Keywords: Data Transformation/ETL, Information Visualization, Administrative Systems, Informatics Implementation
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

A significant challenge in using cardiorespiratory monitoring data for AI and machine learning (ML) applications is the development of platforms capable of ingesting, normalizing, and delivering live patient-centric data to analytics applications in real time. We integrated a real-time, ML-enhanced alarm and vital sign application—into a scalable, vendor-agnostic digital health platform to enable hospital-wide deployment. The system is used to evaluate the clinical workflow impacts of alarms and reduce the number of false alarms.

Speaker(s):
Delgersuren Bold, MS
Nell Hodgson Woodruff School of Nursing, Emory University