title: Consequences for overcrowding in the emergency room of a change in bed management policy on available in-hospital beds.
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.
authors.affiliation: Department of Anesthesia Resuscitation Pain Emergency Medicine, Nîmes University Hospital, 4 Rue du Professeur Robert Debré, 30029 Nîmes, France. Email: ; ;
authors.forename: Pierre-Géraud
authors.initials: PG
authors.lastname: Claret
authors.affiliation: EA 2415, Clinical Research University Institute, Montpellier University, 641 Avenue du Doyen Gaston Giraud, 34093 Montpellier, France.
authors.forename: Thierry
authors.initials: T
authors.lastname: Boudemaghe
authors.affiliation: Department of Anesthesia Resuscitation Pain Emergency Medicine, Nîmes University Hospital, 4 Rue du Professeur Robert Debré, 30029 Nîmes, France. Email: ; ;
authors.forename: Xavier
authors.initials: X
authors.lastname: Bobbia
authors.affiliation: Department of Anesthesia Resuscitation Pain Emergency Medicine, Nîmes University Hospital, 4 Rue du Professeur Robert Debré, 30029 Nîmes, France. Email: ; ;
authors.forename: Andrew
authors.initials: A
authors.lastname: Stowell
authors.affiliation: Department of Anesthesia Resuscitation Pain Emergency Medicine, Nîmes University Hospital, 4 Rue du Professeur Robert Debré, 30029 Nîmes, France. Email: ; ;
authors.forename: Élodie
authors.initials: É
authors.lastname: Miard
authors.affiliation: Emergency Department, Montpellier University Hospital, Montpellier, France. Email.
authors.forename: Mustapha
authors.initials: M
authors.lastname: Sebbane
authors.affiliation: EA 2415, Clinical Research University Institute, Montpellier University, 641 Avenue du Doyen Gaston Giraud, 34093 Montpellier, France.
authors.forename: Paul
authors.initials: P
authors.lastname: Landais
authors.affiliation: Department of Anesthesia Resuscitation Pain Emergency Medicine, Nîmes University Hospital, 4 Rue du Professeur Robert Debré, 30029 Nîmes, France. Email: ; ;
authors.forename: Jean-Emmanuel
authors.initials: JE
authors.lastname: De La Coussaye
mesh_terms: D000368:Aged; D001509:Bed Occupancy / Q000706:statistics & numerical data*; D001513:Beds / Q000706:statistics & numerical data; D003198:Computer Simulation; D003441:Crowding; D017598:Efficiency, Organizational; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006752:Hospital Planning; D006784:Hospitals, Teaching; D006801:Humans; D008297:Male; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data