High Resolution Physiological Data Capture in the CCU
e-Health ePoster Library. McCullagh J. Jun 6, 2017; 167104; EP05.01
Mr. Joe McCullagh
Mr. Joe McCullagh
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Abstract
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Purpose/Objectives: 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 information 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]. Tools claiming the ability to support high resolution data stream access often provide little or no information on in practice use; data quality, frequency limits, parameter profile, scalability, and system impact are unknown. This presentation demonstrates the acquisition of high-resolution physiological data in a live clinical setting and provides in practice information on data encountered. 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

Methodology/Approach: We simultaneously captured high-resolution physiological data streams from 44 Phillips IntelliVue patient monitors in a Critical Care Unit (CCU) using a software-based Biomedical Device Integration tool. Patient parameters, waveform and numeric, were captured including: arterial blood pressure, respiratory impedance, plethysmography, electrocardiogram (ECG), heartrate, and peripheral oxygen saturation (Sp02). Data and parameter information was captured from high-resolution data streams encountered in a live CCU, from 44 patients monitors simultaneously, and averaged over a 24hr period.

Finding/Results: 1) 62 distinct numeric and waveform parameters encountered; 2) 1,400 average data points per second per patient; and 3) 3.7 gigabytes of data generation per patient per 24 hour day.

Conclusion/Implications/Recommendations: High-resolution physiological data is being captured in a live clinical environment and is providing biomedical researchers additional visibility into subjects’ physiological status. This demonstration shows the feasibility of capturing high-resolution physiological data in intensive care units using a biomedical device integration tool. High-resolution physiological data acquisition is a viable option to support biomedical investigation in live clinical environments.

140 Character Summary: This presentation demonstrates the acquisition of high-resolution physiological data from biomedical devices in a live Clinical Care Unit.
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