Methods and analytical tools for patient flow optimization

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The patient flow problem in clinical settings involves determining for each patient the best sequence of activities as well as their appointment(s). Patients arrive in the system one at a time, typically because they are referred to the treatment centre or clinic, and wait for an appointment. This appointment usually defines the start time of their visit (or the start day of their treatment). Each patient has a profile that is not known in advance. The profile is defined to be a set of activities and a duration time for each activity. One or more resources may perform each activity, and some activities have precedence constraints. Each day one or more activities may occur. The arrival times, care needs, and service times of the patients are usually unknown.**Two different settings are considered in this proposal: multiple appointments each involving only one activity but different resources, and single appointments requiring different activities to be performed by different resources. Activities may occur in the same day or over a longer period of time. For example, all appointments for radiotherapy treatment on the linear accelerators (the machines used for radiotherapy) are booked in advance at the same time. Earlier appointments are set up with the physician, for the CT scan, etc. In other outpatient settings, such as preoperative clinics, a single appointment is made and all activities such as the blood test, the physician consultation, and the electrocardiogram must be scheduled on the same day. **An effective health system must efficiently coordinate the necessary resources and activities. While this is the long-term goal of my research program, this research proposal specifically addresses the patient flow problem and its integration with the patient booking problem and the resource planning and scheduling problems. **Different tools and methods will be used to tackle these problems. The main challenges are related to the uncertainty in the healthcare context and the complexity of the problems. We will develop a methodological framework based on online optimization algorithms and efficient metaheuristics. Seven projects will be assigned to MSc and PhD students. Two will develop online algorithms for patient booking (assigned to two PhD students, each considering a different setting), and two others will explore large-neighbourhood metaheuristics for resource planning (MSc #1) and scheduling (MSc #2). Projects five and six will focus on integration methods to link the methods and solutions of the first four projects; these coordination projects are the keystone of my research program. Finally project 7 will ensure that a technological transfer will occur.*The expected impact is to reduce patient delays and bottlenecks created by a lack of coordination of the limited resources.