City Research Online

Abstract

Healthcare systems around the world are facing an inpatient bed crisis. This crises has been highlighted, more than ever before, during the recent Covid-19 pandemic. Our aim in this doctoral dissertation is to propose a cost-effective solution to the ongoing bed-crisis with a focus on reconfiguration of inpatient services. The configuration of inpatient services, which identifies the set of specialties and bed numbers allocated to each ward, has a substantial impact on performance, which we measure by the cost of patients waiting for services or abandoning the services plus the cost of nursing teams. Reviewing the existing configurations proposed in the literature, we choose the clustered overflow configuration as the basis for our study due to its versatility. Given a set of specialties, a total number of beds, and a (potentially infinite) waiting time threshold for patients, we then propose a heuristic methodology for finding a good allocation of beds and specialties for this configuration. This methodology relies on a novel performance evaluation model for overflow delays systems, i.e., hierarchical queueing systems involving several dedicated pools and a single overflow pool. We illustrate the application of our methodology by applying it on a comprehensive inpatient dataset obtained from a UK hospital. A simulation study shows substantial savings can potentially be made by using the configurations proposed by our methodology as compared to the existing configuration of the hospital or other major configurations proposed in the literature.

Publication Type:	Thesis (Doctoral)
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Departments:	Bayes Business School > Bayes Business School Doctoral Theses Doctoral Theses Bayes Business School > Management

Export

Downloads