Strategic and tactical decision-making for inpatient admission and hospital bed allocation: an application to neurology wards

The healthcare sector has received constant appeals from different stakeholders over the past decades to increase operational efficiency and enhance quality of care for patients. Hospital managers and health authorities confront serious challenges in identifying areas for improvement and designing plans to boost healthcare delivery processes, all while maintaining the operational costs aligned with their planned budget. The difficulty of this task is amplified by budget cuts and insufficient resources in the healthcare system. Operations Research models can be used to assist healthcare managers in making informed and evidence-based decisions. This thesis aims at developing patient admission and bed allocation policies in acute care wards, where acquiring extra resources is extremely expensive for hospitals and a delay in treatment is highly undesirable from a patient health perspective. The problem of patient admission and inpatient bed allocation in acute care wards recognizing multiple patient types with different medical characteristics is considered in this thesis. Recent studies have shown that in the event of an acute episode patients are more effectively treated in specialized inpatient settings. The benefits of such specialized care, however, might be offset by long wait times at the emergency department due to bed unavailability in the ward. This research is inspired by the managerial challenges at the neurology ward of the Montreal Neurological Hospital, where the optimal care pathway for patients with neurological diseases is particularly time-sensitive. Failure in matching the hospital's service capacity and patient demand for certain levels of care can be problematic. Moreover, day-to-day fluctuations in demand affect the efficient utilization of hospital capacity. The key issue for matching the demand and service capacity and improving the performance of the hospital is intelligently designed capacity-related policies; both at the strategic and tactical levels. At the tactical level, the admission process of patients to a neurology ward is modeled using an average cost dynamic programming framework. By solving the dynamic program model, we are essentially looking for the dynamic admission policy that provides the best care for all patients in light of limited bed availability. In terms of solution methodology, an integrated approach that combines queuing models and approximate dynamic programming is presented. Furthermore, the performance of the proposed approach is compared with the performance of other heuristic policies that can be suggested for such types of problems. It is shown that the dynamic admission policy that can adjust allocations of the beds based on the state of the ward performs better compared with other static policies. In particular, the dynamic admission policy reduces the average ED boarding time that patients experience before they are transferred to the ward.At the strategic level, the problem of multi-site resource allocation and system configuration in response to the pending merger of two existing sites, i.e., the stroke wards at Montreal Neurological Hospital and Montreal General Hospital, is studied. Designing an appropriate admission policy for patients at the hospital level along with an optimal bed allocation policy between the two sites are the major concerns of hospital managers in this process. Two possible settings for admission of patients to the hospitals are examined to determine which setting would be preferred in terms of minimizing the patient admission refusal rate. Meanwhile, the multi-site bed allocation problem is formulated so that resources are optimally distributed in accordance with the patient flow at each site. It is found that the decision of system configuration for a multi-hospital network requires careful consideration of patient mix in the arrivals, relative length of stay of patients, and the distribution of patient load between hospitals.

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