Measuring Nursing Workload in an Intensive Care Unit Using NGOMSL Model

被引:0
|
作者
Sreeramakavacham, Sivamanoj [1 ]
Kim, Jung Hyup [1 ]
Despins, Laurel [2 ]
机构
[1] Univ Missouri, Ind & Mfg Syst Engn Dept, Columbia, MO 65211 USA
[2] Univ Missouri, Sinclair Sch Nursing, Columbia, MO 65211 USA
来源
HCI INTERNATIONAL 2019 - LATE BREAKING PAPERS, HCII 2019 | 2019年 / 11786卷
关键词
Nursing workload; RTLS; Nursing workflow; Intensive care unit;
D O I
10.1007/978-3-030-30033-3_32
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The purpose of this study is to understand the impact on nursing workload due to patient severity and nurse experience by using Natural GOMS Language (NGOMSL) model. To develop the model, a time and motion study was conducted using a real-time location system. After that, the nurse's location and time data were used to develop multiple hierarchical task analysis charts. These HTA charts were used to create the NGOMSL model and identified the ICU nurse's workflow patterns. The analysis results from the NGOMSL model showed that the patient severity and the nurse's experience had significant impacts on nurse's workload for in-room patient care activities, out of room activities, peer support, and talking to other healthcare professionals. This research empirically demonstrates that patient severity and nurse's experience significantly influenced the nursing workload in an intensive care unit, and the NGOMSL model could measure the workload.
引用
收藏
页码:412 / 421
页数:10
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