In recent years, a growing proportion of patients flowing through inpatient hospital wards come from Emergency Departments (EDs). Because of ED overcrowding and the reduction of hospital beds, it is becoming crucial to improve the management of emergent patient flows to be admitted into inpatient wards. This study evaluates the impact and potential of introducing the so-called Bed Management function in a large city’s health district. Thanks to the collaboration with the Local Health Authority of the Liguria region, an observational analysis was conducted based on data collected over a 1-year period to develop a discrete event simulation model. The model has been utilised to evaluate several bed management strategies. Two scenarios at a tactical level, i.e. the opening of a discharge room and blocking elective arrivals, have also been simulated. The effects of such scenarios have been compared with respect to a set of performance metrics, such as waiting times, misallocated patients, trolleys in EDs, and inpatient bed occupancy rates.
Managing emergent patient flow to inpatient wards: A discrete event simulation approach
LANDA, PAOLO;SONNESSA, MICHELE;TANFANI, ELENA;TESTI, ANGELA
2015-01-01
Abstract
In recent years, a growing proportion of patients flowing through inpatient hospital wards come from Emergency Departments (EDs). Because of ED overcrowding and the reduction of hospital beds, it is becoming crucial to improve the management of emergent patient flows to be admitted into inpatient wards. This study evaluates the impact and potential of introducing the so-called Bed Management function in a large city’s health district. Thanks to the collaboration with the Local Health Authority of the Liguria region, an observational analysis was conducted based on data collected over a 1-year period to develop a discrete event simulation model. The model has been utilised to evaluate several bed management strategies. Two scenarios at a tactical level, i.e. the opening of a discharge room and blocking elective arrivals, have also been simulated. The effects of such scenarios have been compared with respect to a set of performance metrics, such as waiting times, misallocated patients, trolleys in EDs, and inpatient bed occupancy rates.File | Dimensione | Formato | |
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