Multiobjective bed management considering emergency and elective patient flows. (8th November 2016)

• Record Type: Journal Article
• Authors: Landa, Paolo; Sonnessa, Michele; Tànfani, Elena; Testi, Angela
• Other Names: Dias Joana Matos guestEditor.; Rocha Humberto guestEditor.; Viana Ana guestEditor.
• Is Part Of: International transactions in operational research. Volume 25:Number 1(2018)
• Page Start: 91
• Page End: 110
• Publication Date: 2016-11-08
• Subjects: capacity planning -- bed management -- discrete event simulation -- multiobjective optimization -- decision support system; Operations research -- Periodicals; 003
• DOI: 10.1111/itor.12360
• Languages: English
• ISSNs: 0969-6016

Abstract

Abstract: In recent years, hospitals have increasingly been faced with a growing proportion of their inpatient work coming from the fluctuating and unpredictable demand of emergency admissions. The opportunity to move emergency patients who have been selected for admission out of the emergency department (ED) is linked to the ability of the hospital network to actually admit them. The latter is, in turn, correlated to the availability of inpatient beds in the hospital wards, which are shared resources between elective and emergency patients. Due to the overcrowding of EDs and the growing concern regarding reducing the number of inpatient ward beds, it is thus becoming crucial to improve the bed capacity planning and the management of emergency and elective patient admissions. In this direction, greater coordination and communication among the different healthcare providers involved in the pathway flows is required, and the so‐called "bed management" function plays a key role. This study starts with collaboration with the local health government (LHG) of the Liguria region aimed at studying the hospital bed management function. A large quantity of data records have been collected during one year of activity to obtain information related to the flow of emergency and elective patient pathways. A medium‐sized hospital located in the city of Genova has been chosen as a case study, and a discrete event simulation model has been developed to reproduce the multiple patient flows involved in the system. Multiobjective optimization analysis has been performed to choose the best bed allocations considering both operational and tactical decisions characterized by various trade‐offs among alternative conflicting objectives. The model can be used to help decision makers find a representative set of Pareto‐optimal solutions and quantify trade‐offs when satisfying different objectives.