Singapore Public Health Hospital: Bed management system - CRAMA, Pascale (Institutional Knowledge at Singapore Management University, 2024)
Set in 2022, this case describes the challenges faced by Singapore General Hospital (SGH), one of Singapore’s leading public hospitals, in managing its bed occupancy rate (BOR) to best suit its patient workload pattern and adhere to the government’s guidelines, and describes the evolution of its bed management system (BMS) in response to those challenges. Bed management, although a background activity, plays a central role in the effective running of a hospital. While SGH has adopted new technology and best practices in its BMS over the years, there are still frequent mismatches between the available bed supply and incoming patient demand. One of the reasons is Singapore’s growing healthcare demand due to its ageing population coupled with a greater awareness of regular healthcare monitoring in the society, which requires capacity expansion. In addition, given that poor bed management in a hospital incurs heavy costs and adversely impacts everyone from patients to nursing staff, doctors and administrators, there is a need for more effective management of the existing bed supply and implementation of new measures to optimise the burden on hospitals. The Bed Management Unit (BMU) at SGH, recognises that the analysis of the workload of the hospital over the previous five years is critical for making many key decisions, such as capacity expansion and class configuration, patient-bed assignment protocols, and the adoption of innovative healthcare processes for effective bed management and healthcare delivery. Students will gain practical insights into the complex nature of capacity management at hospitals and the pivotal role BMS plays. They will learn about the role of operations and logistics, specialities and wards, emergency preparedness, resource allocation, technology integration, financial considerations, quality of care, and patient experience during bed allocation. Students will analyse data to understand the irregular pattern of patient workload and develop strategies to achieve optimal BOR.