Consequences for overcrowding in the emergency room of a change in bed management policy on available in-hospital beds
Pierre-Géraud Claret A B E , Thierry Boudemaghe B C , Xavier Bobbia A , Andrew Stowell A , Élodie Miard A , Mustapha Sebbane D , Paul Landais B C and Jean-Emmanuel De La Coussaye AA Department of Anesthesia Resuscitation Pain Emergency Medicine, Nîmes University Hospital, 4 Rue du Professeur Robert Debré, 30029 Nîmes, France. Email: xavier.bobbia@gmail.com; andrew_stowell@yahoo.fr; elodie.mi@free.fr; jean.emmanuel.delacoussaye@chu-nimes.fr
B EA 2415, Clinical Research University Institute, Montpellier University, 641 Avenue du Doyen Gaston Giraud, 34093 Montpellier, France.
C Département de Biostatistique Épidémiologie Santé Publique et d’Information Médicale, Nîmes University Hospital, 4 Rue du Professeur Robert Debré, 30029 Nîmes, France. Email: thierry.boudemaghe@chu-nimes.fr; paul.landais@chu-nimes.fr
D Emergency Department, Montpellier University Hospital, Montpellier, France. Email: m-sebbane@chu-montpellier.fr
E Corresponding author. Email: pierre.geraud.claret@gmail.com
Australian Health Review 40(4) 466-472 https://doi.org/10.1071/AH15088
Submitted: 12 May 2015 Accepted: 7 September 2015 Published: 19 October 2015
Abstract
Objective Emergency rooms play an important role by providing continuous access to healthcare 24 h a day, 7 days a week, but the lack of available hospital beds has become a major difficulty. Changing bed management policy could improve patient flow. The aim of the present study was to evaluate the consequences of a change in patient prioritisation on available beds.
Methods The study consisted of a computerised bed management simulation based on day-by-day data collected from 1 to 31 January 2013 in a teaching hospital. Real hospital data were used to power the computer simulation. The scenarios tested were: (1) priority for emergency and surgery; (2) priority for emergency and medicine; (3) priority for planned admissions and surgery; and (4) priority for planned admissions and medicine. The results of these scenarios were compared with each other and to actual data.
Results This study included 2347 patients. The scenario that proved to be the least efficient was the one that gave priority to emergency patients presenting with a medical condition. The scenario that exhibited the best efficiency was the one that gave priority to planned admissions and surgery.
Conclusions Changing policies for hospital bed management is worth exploring to improve hospital patient flow and length of stay.
What is known about the topic? The lack of available hospital beds is a major difficulty in managing patient flow in emergency rooms (ERs). The ER patient flow competes against a flow of planned hospital admissions for the same beds and the lack of a clearly defined policy on either prioritising ER patient flow over planned admissions or vice versa contributes to a disordered system.
What does this paper add? We compared several simulated scenarios corresponding to different bed management policies. The scenario that gave priority to planned admissions and surgery gave the most suitable results.
What are the implications for practitioners? Postponing scheduled surgical patients was not an efficient procedure to solve hospital overcrowding.
Additional keywords: emergency service, hospital organisation and administration, humans, simulation, time factors.
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