Title | ||
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Quantifying the Impact of Resuscitation-Team Activation in Hospital Emergency Departments |
Abstract | ||
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Hospital emergency department (ED) operations are affected when critically ill or injured patients arrive. Such events often lead to the initiation of specific protocols, referred to as Resuscitation-team Activation (RA), in the ED of Mayo Clinic, Rochester, MN where this study was conducted. RA events lead to the diversion of resources from other patients in the ED to provide care to critically ill patients; therefore, it has an impact on the entire ED system. This paper presents a data-driven and flexible statistical learning model to quantify the impact of RA on the ED. The model learns the pattern of operations in the ED from historical patient arrival and departure timestamps and quantifies the impact of RA by measuring the deviation of the departure of patients during RA from normal processes. The proposed method significantly outperforms baseline methods based on measuring the average time patients spend in the ED. |
Year | DOI | Venue |
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2020 | 10.1109/JBHI.2020.2997562 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Critical Illness,Emergency Service, Hospital,Hospital Rapid Response Team,Humans,Models, Statistical,Resuscitation,Time Factors | Journal | 24 |
Issue | ISSN | Citations |
10 | 2168-2194 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Xiaochen Xian | 1 | 0 | 0.68 |
Devashish Das | 2 | 5 | 1.92 |
Kalyan Pasupathy | 3 | 0 | 0.34 |
Eric Boie | 4 | 0 | 0.34 |
Mustafa Sir | 5 | 42 | 9.57 |