Project Aim
SUPPORT-HH aims to develop generic and scalable decision support tools for comprehensive management of Home Hospitalization (HH) services. Grounded in Operations Research and Management Science methodologies, these tools address strategic, tactical and operational planning decisions and will be developed and validated in collaboration with partner institutions using real HH patient data.
Decision Framework
The planning decisions occur at three interconnected levels:
Strategic → Tactical → Operational

Strategic level: Demand Prediction & Capacity Planning
Objective: To develop a generic framework for predicting HH demand using historical patient data, relevant contextual variables and expert knowledge.
Methods: Different forecasting approaches, including machine learning, Markov models, regression and time-series methods, will be explored and benchmarked. The resulting demand forecasts will serve as input to a multi-objective optimization model under uncertainty designed to support capacity planning decisions while balancing demand satisfaction, operational costs and patient-centered outcomes.
Purpose: This framework will support long-term planning decisions regarding the allocation of human and material resources and overall service capacity levels in HH services.

Tactical level: Staff Shift Design
Objective: To develop a multi-objective optimization model under uncertainty to support the design of staff shifts in HH services
Methods:The model will incorporate multiple objectives, including fairness in shift allocation and balanced workload distribution among staff members. To address the computational complexity of the problem, exact and heuristic algorithms will be developed to generate efficient solutions while ensuring scalability and applicability to different contexts.
Purpose: The resulting framework will support medium-term planning decisions related to workforce organization, ensuring that staff shifts are consistent with the service capacity levels established previously.

Operational level: Routing & Scheduling of Patient Visits
Objective: To develop a multi-objective optimization model to support the routing, scheduling and assignment of patient visits in HH services.
Methods: A deterministic optimization model will first be developed to support routing, scheduling and team assignment decisions, incorporating operational constraints such as visit time windows, precedence rules and continuity of care. In a second stage, the model will be extended to incorporate uncertainty. Scalable exact and heuristic algorithms will be developed.
Purpose: This framework will support daily operational planning decisions related to the assignment of healthcare teams and the routing and scheduling of patient visits in HH services.
Strategic decisions provide input to tactical decisions, which in turn provide input to operational decisions, forming a hierarchical and integrated planning framework.
Images: Designed by macrovector/Freepik
