Australian private emergency departments can assist ambulance services by taking public emergency patients during surge and disasters

2019 ◽  
Vol 31 (5) ◽  
pp. 886-888 ◽  
Author(s):  
Katie Walker ◽  
Michael Stephenson ◽  
William A Dunlop ◽  
Edward M Cheong ◽  
Michael Ben‐Meir
Author(s):  
Zeynel Abidin Çil ◽  
Abdullah Caliskan

Emergency departments of hospitals are busy. In recent years, patient arrivals have significantly risen at emergency departments in Turkey like other countries in the world. The main important features of emergency services are uninterrupted service, providing services in a short time, and priority to emergency patients. However, patients who do not need immediate treatment can sometimes apply to this department due to several reasons like working time and short waiting time. This situation can reduce efficiency and effectiveness at emergency departments. On the other hand, computers solve complex classification problems by using machine learning methods. The methods have a wide range of applications, such as computational biology and computer vision. Therefore, classification of emergency and non-emergency patients is vital to increase productivity of the department. This chapter tries to find the best classifier for detection of emergency patients by utilizing a data set.


1998 ◽  
Vol 32 (2) ◽  
pp. 144-147 ◽  
Author(s):  
Steven Asch ◽  
Barbara Leake ◽  
Laura Knowles ◽  
Lillian Gelberg

2020 ◽  
Vol 19 (06) ◽  
pp. 1485-1548
Author(s):  
Miguel Ortiz-Barrios ◽  
Juan-Jose Alfaro-Saiz

Performance evaluation is relevant for supporting managerial decisions related to the improvement of public emergency departments (EDs). As different criteria from ED context and several alternatives need to be considered, selecting a suitable Multicriteria Decision-Making (MCDM) approach has become a crucial step for ED performance evaluation. Although some methodologies have been proposed to address this challenge, a more complete approach is still lacking. This paper bridges this gap by integrating three potent MCDM methods. First, the Fuzzy Analytic Hierarchy Process (FAHP) is used to determine the criteria and sub-criteria weights under uncertainty, followed by the interdependence evaluation via fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL). The fuzzy logic is merged with AHP and DEMATEL to illustrate vague judgments. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used for ranking EDs. This approach is validated in a real 3-ED cluster. The results revealed the critical role of Infrastructure (21.5%) in ED performance and the interactive nature of Patient safety ([Formula: see text]). Furthermore, this paper evidences the weaknesses to be tackled for upgrading the performance of each ED.


2011 ◽  
Vol 29 (9) ◽  
pp. 725-731 ◽  
Author(s):  
Justin Boyle ◽  
Julia Crilly ◽  
Gerben Keijzers ◽  
Marianne Wallis ◽  
James Lind ◽  
...  

Public Health ◽  
2019 ◽  
Vol 167 ◽  
pp. 16-20
Author(s):  
C. Koutserimpas ◽  
P. Agouridakis ◽  
G. Velimezis ◽  
G. Papagiannakis ◽  
I. Keramidis ◽  
...  

Crisis ◽  
2010 ◽  
Vol 31 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Gregory Luke Larkin ◽  
Annette L. Beautrais

Crisis ◽  
2018 ◽  
Vol 39 (5) ◽  
pp. 318-325 ◽  
Author(s):  
Barbara Stanley ◽  
Glenn W. Currier ◽  
Megan Chesin ◽  
Sadia Chaudhury ◽  
Shari Jager-Hyman ◽  
...  

Abstract. Background: External causes of injury codes (E-codes) are used in administrative and claims databases for billing and often employed to estimate the number of self-injury visits to emergency departments (EDs). Aims: This study assessed the accuracy of E-codes using standardized, independently administered research assessments at the time of ED visits. Method: We recruited 254 patients at three psychiatric emergency departments in the United States between 2007 and 2011, who completed research assessments after presenting for suicide-related concerns and were classified as suicide attempters (50.4%, n = 128), nonsuicidal self-injurers (11.8%, n = 30), psychiatric controls (29.9%, n = 76), or interrupted suicide attempters (7.8%, n = 20). These classifications were compared with their E-code classifications. Results: Of the participants, 21.7% (55/254) received an E-code. In all, 36.7% of research-classified suicide attempters and 26.7% of research-classified nonsuicidal self-injurers received self-inflicted injury E-codes. Those who did not receive an E-code but should have based on the research assessments had more severe psychopathology, more Axis I diagnoses, more suicide attempts, and greater suicidal ideation. Limitations: The sample came from three large academic medical centers and these findings may not be generalizable to all EDs. Conclusion: The frequency of ED visits for self-inflicted injury is much greater than current figures indicate and should be increased threefold.


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