Abstract

HIGH RESOLUTION PHYSIOLOGICAL DATA ACQUISITION IN THE CCU

Author: Joseph McCullagh

Utilization of the continuously-generated data from physiological monitoring medical devices has been predicted to advance medical research and lead to improvements in patient care [1-3]; however, there is a lack of literature documenting the acquisition of high-resolution physiological data in live clinical settings. Medical Device Integration (MDI) solutions and Clinical Information Systems (CIS) typically implemented into healthcare facilities are designed for integration into Electronic Health Records (EHRs) and provide limited numeric snapshots of complex physiological data [1, 2]. This presentation demonstrates the successful acquisition of high-resolution physiological data in a live clinical setting. We captured high-resolution physiological data from 44 Phillips IntelliVue patient monitors in a Critical Care Unit using ViNES®, a software-based Biomedical Device Integration tool. Waveforms (up to 500Hz) and numeric values were captured for 62 distinct parameters provided by the Phillips IntelliVue patient monitors, including: arterial blood pressure, respiratory impedance, plethysmography, electrocardiogram (ECG), heartrate, and peripheral oxygen saturation (Sp02), resulting in over 3.7 gigabytes of data generated per subject per 24 hour day. The data streams were integrated into analytic software applications for analysis and stored for retrospective investigation. High-resolution physiological data acquisition is being used in CCU research and offers biomedical researchers additional visibility into subjects’ physiological status. REFERENCES: [1] Belle, A. et al. “Big Data Analytics in Healthcare,” BioMed Research International,” vol. 2015, Article ID 370194, 16 pages, 2015. doi:10.1155/2015/370194 [2] De Georgia, M.A. et al. “Information Technology in Critical Care: Review of Monitoring and Data Acquisition Systems for Patient Care and Research,” The Scientific World Journal, vol. 2015, Article ID 727694, 9 pages, 2015. doi:10.1155/2015/727694 [3] Rumsfeld, J. et al. “Big data analytics to improve cardiovascular care: promise and challenges,” Nat Rev Cardiol. 2016 Jun; 13(6):350-9. doi: 10.1038/nrcardio.2016.42