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Strategies to convert hospital beds for COVID-19 patients to minimize emergency department overcrowding.
- Source :
- Health Services Management Research; Nov2024, Vol. 37 Issue 4, p209-218, 10p
- Publication Year :
- 2024
-
Abstract
- Background: The shortage of hospital beds for COVID-19 patients has been one critical cause of Emergency Department (ED) overcrowding. Purpose: We aimed at elaborating a strategy of conversion of hospital beds, from non-COVID-19 to COVID-19 care, minimizing both ED overcrowding and the number of beds eventually converted. Research Design: Observational retrospective study. Study Sample: We considered the centralized database of all ED admissions in the Lombardy region of Italy during the second "COVID-19 wave" (October to December 2020). Data collection and Analysis: We analyzed all admissions to 82 EDs. We devised a family of Monte Carlo simulations to evaluate the performance of hospital beds' conversion strategies triggered by ED crowding of COVID-19 patients, determining a critical number of beds to be converted when passing an ED-specific crowding threshold. Results: Our results suggest that the maximum number of patients waiting for hospitalization could have been decreased by 70% with the proposed strategy. Such a reduction would have been achieved by converting 30% more hospital beds than the total number converted in the region. Conclusions: The disproportion between reduction in ED crowding and additionally converted beds suggests that a wide margin to improve the efficiency of the conversions exists. The proposed simulation apparatus can be easily generalized to study management policies synchronizing ED output and in-hospital bed availability. [ABSTRACT FROM AUTHOR]
- Subjects :
- HOSPITAL utilization
POISSON distribution
COMPUTER simulation
PATIENTS
HOSPITAL building design & construction
RESEARCH funding
PREDICTION models
HOSPITAL admission & discharge
SCIENTIFIC observation
HOSPITAL care
HOSPITAL emergency services
RETROSPECTIVE studies
REVERSE transcriptase polymerase chain reaction
DESCRIPTIVE statistics
CROWDS
DATA analysis software
COVID-19
REGRESSION analysis
Subjects
Details
- Language :
- English
- ISSN :
- 09514848
- Volume :
- 37
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Health Services Management Research
- Publication Type :
- Academic Journal
- Accession number :
- 180764405
- Full Text :
- https://doi.org/10.1177/09514848231218648