| Literature DB >> 31582102 |
Ashish K Khanna1, Sanchit Ahuja2, Robert S Weller3, Timothy N Harwood4.
Abstract
The postoperative ward is considered an ideal nursing environment for stable patients transitioning out of the hospital. However, approximately half of all in-hospital cardiorespiratory arrests occur here and are associated with poor outcomes. Current monitoring practices on the hospital ward mandate intermittent vital sign checks. Subtle changes in vital signs often occur at least 8-12 h before an acute event, and continuous monitoring of vital signs would allow for effective therapeutic interventions and potentially avoid an imminent cardiorespiratory arrest event. It seems tempting to apply continuous monitoring to every patient on the ward, but inherent challenges such as artifacts and alarm fatigue need to be considered. This review looks to the future where a continuous, smarter, and portable platform for monitoring of vital signs on the hospital ward will be accompanied with a central monitoring platform and machine learning-based pattern detection solutions to improve safety for hospitalized patients.Entities:
Keywords: artificial intelligence; continuous monitoring; machine learning; postoperative; vital signs; ward
Mesh:
Year: 2019 PMID: 31582102 DOI: 10.1016/j.bpa.2019.06.005
Source DB: PubMed Journal: Best Pract Res Clin Anaesthesiol ISSN: 1521-6896