Modeling and analysis of triage nurse ordering in emergency departments

Abstract

International audienceEmergency departments are facing a worldwide problem that affects their performance, namely Overcrowding. Triage Nurse Ordering appears to be a promising approach in addition to be cost effective. This paper proposes a process-based triage nurse ordering model and assesses its efficiency on the ED performance through simulation while considering the length of stay as the key indicator. The study examines the impact of triage nurse ability, system load and triage time extension on the benefits that might be derived from triage nurse ordering

Enclosing rail capacity/punctuality in accessibility measures: a two-step approach by using Data Envelopment Analysis and Analytic Hierarchy Process

“…The adopted movement monitoring system [26] is composed of three layers: (1) a Pebble smartwatch [27], which contains a tri-axis accelerometer and Bluetooth 4.0, for recording the user's arm movement data; (2) an Android smartphone for receiving data from Pebble and sending them to remote server after integrated with data collected from the GPS sensor and accelerometer inside the smartphone; (3) a NoSQL data search and analytic engine [28] on remote server for data storage and analysis. The structure of the collected data is as shown in Table I.…”

“…Depending on the countries, the triage operation is produced before or after the administrative registration one [2] and constitutes the first care operation in the care process. Lately, physicians and healthcare managers have raised attention on the possible positive effects of the implementation of a Physician Triage to reduce patients LOS and general overcrowding in the ED [26]. The triage and the first medical assessment operations, which are two successive care operations in the care process are then merged into one (see figure 1).…”

“…A review of related work shows that there is no single prediction model which is optimal for all the considered CPU time series [21,15,4,26,25]. Consequently, due to the diversity of the world-wide distributed resources, the precision is not always ensured using a single predictor.…”

“…In fact, with the fast development of mobile computing and wearable technology, more information can be obtained to better analyze tremor. In this paper, a human movement monitoring system based on smart devices and NoSQL databases [26] is used to collect data from individuals with tremor. Besides the tremor data, more related information is also collected with this system, such as the time, the location and the arm angle and so on.…”

“…A literature review of the last 30 years researches on material handling equipment selection has identified 27 articles on this subject: Bookbinder and Gervais [3], Chakraborty and Banik [4], Chan, Ip and Lau [5], Cho and Egbelu [6], Chu, Egbelu and Wu [7], Fisher, Farber and Kay [8], Fonseca, Uppal and Greene [9], Gabbert and Brown [10], Hassan [11], Hassan [12], Hassan, Hogg and Smith [13], Kim and Eom [14], Kulak [15], Malmborg, Krishnakumar, Simons and Agee [16], Maniya and Bhatt [17], Matson, Mellichamph and Swaminathan [18], Mirhosseyni and Webb [19], Onut, Kara and Mert [20], Park [21], Raman, Nagalingam, Gurd and Lin [22], Sharp, et al [23], Telek [24], Trevino, Hurley, Clincy and Jang [25], Tuzkaya, Gülsün, Kahraman and Özgen [26], Velury and Kennedy [27], Welgama and Gibson [28], and Yaman [29].…”