Optimization of laboratory workflowsThis project aims to improve the reliability and timeliness of medical test results by developing data-driven methods for planning and controlling laboratory processes. Using tools such as reinforcement learning, queueing models, and online algorithms, we will design models that predict demand, manage uncertainty, and adapt laboratory operations in real time. In collaboration with Labmicta, the developed methods will be implemented in practice to improve laboratory management and support more efficient healthcare delivery.

Prioritize IIPeriods of acute or structural pressure on surgical care require optimal use of operating capacity. During COVID-19, a lack of management information led to ad hoc decisions about surgical scheduling, resulting in practice variation, avoidable health loss, and inefficient resource use. The PRIORITIZE II project develops a dashboard that links health gains from surgeries to capacity and costs, using real hospital data and standardized data exchange. The project will also add an optimization component to support the optimal allocation of scarce surgical capacity.

CARE-FLOWThe project’s objective is to develop and evaluate healthcare software products and algorithms that improve the streamlining of patient flows and capacities in the border region between the Netherlands and Germany, ensuring access to and efficiency of healthcare.

Sequential decision-makingThe SRI focuses on establishing a community of researchers with different mathematical backgrounds to advance the development of new solution methods for sequential decision-making problems and prove performance guarantees of those methods, with a special focus on problems that exhibit real-world characteristics such as those found in finance, agriculture, and healthcare. The initiative seeks to address gaps in research and collaboration. It emphasizes methodological development with regard to uncertainty, large-scale systems, and non-stationarity, e.g., due to multiple agents, aiming to bridge theoretical advancements and practical implementations.

Patient transfers during pandemicsThe project aims to improve inter-regional patient transfers during pandemics by developing predictive and optimization models to ensure efficient and equitable care allocation across regions. Combining data-driven approaches like statistical methods, queueing theory, and simulation with interdisciplinary collaboration, the initiative addresses the challenges of both infectious and non-infectious care during crises. Strategic partnerships with hospitals and national coordination bodies ensure practical implementation.

EU Horizon Project RAPIDERAPIDE aims to develop, validate, and demonstrate a portfolio of powerful tools that enable healthcare systems to increase the robustness of decisions, the resilience of healthcare professionals and patients, and the flexibility in the modalities of care delivery, thereby maintaining access to regular care during health emergencies.

Optimal Logistic Design of Multidisciplinary CareDriven by public opinion, increased health expenditures, an ageing population, and long waiting lists, a flood of changes in the healthcare system has been set in motion to try to make the Dutch hospitals more efficient. In this research, we will investigate how the capacity planning and control in a multidisciplinary care pathway should be organized, such that timely treatment for all patients can be ensured while an efficient care pathway is maintained.