No abstract

This paper describes the use of operational research techniques to analyze the wait list for the Division of General Surgery at the Capital District Health Authority in Halifax, Nova Scotia, Canada. A discrete event simulation model was developed to aid capacity planning decisions and to analyze the performance of the division. The analysis examined the consequences of redistributing beds between sites, and achieving standard patient lengths of stay, while contrasting them to current and additional resource options. From the results, multiple independent and combined options for stabilizing and decreasing waits for elective procedures were proposed.

No other department influences the workload of a hospital more than the Department of Surgery and in particular, the activities in the operating room. These activities are governed by the master surgical schedule (MSS), which states which patient types receive surgery on which day. In this paper we describe an analytical approach to project the workload for downstream departments based on this MSS. Specifically the ward occupancy distributions, patient admission/discharge distributions, and the distributions for ongoing interventions/treatments is computed. Recovering after surgery requires the support of multiple departments, such as nursing, physiotherapy, rehabilitation and long term care. With our model, managers from these departments can determine their workload by aggregating tasks associated with recovering surgical patients. The model, which supported the development of a new MSS at the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, provides the foundation for a decision support tool to relate downstream hospital departments to the operating room.

Hospitals traditionally segregate resources into centralized functional departments such as diagnostic departments, ambulatory care centers, and nursing wards. In recent years this organizational model has been challenged by the idea that higher quality of care and efficiency in service delivery can be achieved when services are organized around patient groups. Examples include specialized clinics for breast cancer patients and clinical pathways for diabetes patients. Hospitals are struggling with the question of whether to become more centralized to achieve economies of scale or more decentralized to achieve economies of focus. In this paper we examine service and patient group characteristics to study the conditions where a centralized model is more efficient, and conversely, where a decentralized model is more efficient. This relationship is examined analytically with a queuing model to determine the most influential factors and then with simulation to fine-tune the results. The tradeoffs between economies of scale and economies of focus measured by these models are used to derive general management guidelines.