Les problèmes intégrés de case-mix et de planification tactique des capacités des hôpitaux avec la possibilité de l'hospitalisation à domicile

Abstract

In recent years, hospitals are facing several challenges: demographic growth, an aging population, and an increase in chronic diseases. As demand for hospital services rises while the number of beds per capita declines, hospitals confront the challenge of dealing with overcrowding. However, hospital resources are not unlimited. Increasingly, healthcare is under continued economic pressure from the government. The major challenge for hospitals is to be able to deliver ever more services without reducing their quality, and therefore manage their resources as efficiently as possible. Within this context of change, the renewal of hospital operations is inevitable. Current alternatives are emerging, such as hospital at home (HAH), which shorten hospital stays and free up beds. Although HAH has yet to be developed and implemented in all hospitals and throughout the world, it is a promising opportunity. The importance of hospital management using mathematical decision-support models is also essential. Integrated case-mix and capacity planning issues are important tools in hospital planning. Their aim is to determine the optimal combination and volume of patients to manage, and the allocation of resources. Due to its recent nature and development, HAH has not been considered in current studies of these problems. However, an HAH patient’s resource consumption is very different in many ways from a conventional patient. The scope of this thesis is therefore the study of integrated case-mix and capacity planning issues for HAH. More specifically, the main objective is to answer the question: "What are the impacts and possible modifications of integrated case-mix and capacity planning problems for hospitals with possibility of hospital at home?". To deal with this topic, a deterministic model, integrating resource allocation into the decision making of a case-mix problems was adapted. Once the concepts are understood, the impact of HAH on the issue is considered. The major change introduced is the integration of the nursing staff as a limitation on the number of HAH patients who can be admitted. The model is then implemented using a modeling and optimization software platform. Several tests were then carried out to discuss the implications of certain parameters for HAH within the model. In particular, the allocation of beds according to a certain periodicity specific to HAH patients or conventional patients is confirmed. The results obtained in this study highlight the potential of integrated case-mix and capacity planning problems for hospitals. In the context of HAH, hospitals can use them to assess which types of patients are more likely to be hospitalized at home or within the hospital walls. This thesis represents the beginning of reflexion on the topic and may serve as a basis for many further studies. The application of the proposed model to the reality of a hospital, or the integration of variable factors encountered during a patient's hospital stay, are important avenues of development that deserve to be explored. The fields of HAH and hospital operational management are integral parts of current operational research.