scholarly journals Comparing Utilization and Costs of Care in Freestanding Emergency Departments, Hospital Emergency Departments, and Urgent Care Centers

2017 ◽  
Vol 70 (6) ◽  
pp. 846-857.e3 ◽  
Author(s):  
Vivian Ho ◽  
Leanne Metcalfe ◽  
Cedric Dark ◽  
Lan Vu ◽  
Ellerie Weber ◽  
...  
Author(s):  
Daniela Gonçalves-Bradley ◽  
Jaspreet K Khangura ◽  
Gerd Flodgren ◽  
Rafael Perera ◽  
Brian H Rowe ◽  
...  

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
Paula Simões ◽  
Sérgio Gomes ◽  
Isabel Natário

Hospital emergency departments are often overused by patients that do not really need urgent care. These admissions are one of the major factors contributing to hospital costs, which should not be allowed to compromise the response and effectiveness of the National Health Services (SNS). The aim of this study is to perform a detailed spatial health econometrics analysis of the non-urgent emergency situations (classified by Manchester triage) by area, linking them with the efficient use of the national health line, the Saude24 line (S24 line). This is evaluated through the S24 savings calls, using a savings index and its spatial effectiveness in solving the non-urgent emergency situations. A savings call is a call by a user whose initial intention was to go to an urgency department, but who. after calling the S24 line. changed his/her mind. Given the spatial nature of the data, and resorting to INLA in a Bayesian paradigm, the number of non-urgent cases in the Portuguese urgency hospital departments is modeled in an autoregressive way. The spatial structure is accounted for by a set of random effects. The model additionally includes regular covariates and a spatially lagged covariate savings index, related with the S24 savings calls. Therefore, the response in a given area depends not only on the (weighted) values of the response in its neighborhood and of the considered covariates, but also on the (weighted) values of the covariate savings index measured in each neighbor, by means of a Bayesian Poisson spatial Durbin model.


Author(s):  
Joanne Huang ◽  
Zahra Kassamali Escobar ◽  
Todd S. Bouchard ◽  
Jose Mari G. Lansang ◽  
Rupali Jain ◽  
...  

Abstract The MITIGATE toolkit was developed to assist urgent care and emergency departments in the development of antimicrobial stewardship programs. At the University of Washington, we adopted the MITIGATE toolkit in 10 urgent care centers, 9 primary care clinics, and 1 emergency department. We encountered and overcame challenges: a complex data build, choosing feasible outcomes to measure, issues with accurate coding, and maintaining positive stewardship relationships. Herein, we discuss solutions to challenges we encountered to provide guidance for those considering using this toolkit.


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