Distributed planning of clinical pathways
There is a growing pressure on health care due to demographic changes and due to more and more cure and care becoming available. In contrast, the capacity available for cure and care in the Netherlands is not increasing. Furthermore, there is a growing awareness of and focus on the patient and his waiting time in the healthcare process.
In healthcare settings in which patients typically have lengthy clinical pathways involving multiple steps with different providers, waiting time of patients occurs at different phases: before the start of the process the clinical pathway must be scheduled such that all steps (at least in the first part of the pathway) are available, and during the trajectory due to unexpected changes in patient condition or in the effect of the treatment. Due to unexpected (and random) arrivals of patients and changes in the treatment plan resource conflicts occur resulting in interacting clinical pathways and patients waiting, which may have a serious negative impact on the condition of a patient.
We aim to develop distributed planning approaches, which are robust against the inherent uncertainties of the care process and the demand, and which operate at both strategic and tactical level of control. The resulting problems are mathematically hard, and therefore require extensive theoretical analysis.
The project focuses at the long term level of control. Theoretic queueing models will be developed to analyze the logistical performance. Two viewpoints are dealt with: (1) the clinical pathway viewpoint, and (2) the patient viewpoint. Ad (1): For a highly complex set of interacting clinical pathways, a suitable approach comes from theoretical and numerical results of queuing networks. A typical performance measure to be optimized is the trade-off between costs (staff, capacity), and delay of patients. Ad (2): Here the aim is to adequately represent and optimize patient flows, and the interaction between practitioners and resources. A typical trade-off is between the optimal end-to-end sojourn time of a patient, and flexibility of staffing at providers.