An optimisation framework for resource allocation in palliative and end-of-life care.

Area of Science:

• Health Services Research
• Operations Research
• Geriatric Medicine

Background:

• End-of-life care for frail elderly patients often involves high healthcare use, fragmented services, and poor coordination, leading to variable quality and increased costs.
• Existing planning approaches rarely integrate patient-level care pathways into operational models that balance efficiency with patient-centered outcomes.

Purpose of the Study:

• To present a proof-of-concept optimization framework for strategic resource planning in end-of-life care for frail, elderly, and palliative patients.
• To illustrate the framework's application using synthetic data for anticipatory planning and service configuration comparison.

Main Methods:

• Developed two optimization model formulations: one minimizing cost, the other aligning demand with capacity.
• Stratified patients into ten categories with varying resource needs across hospital beds, palliative beds, community nursing, and virtual wards.
• Utilized a synthetic dataset of twelve-month service trajectories to assess model performance.

Main Results:

• Both optimization models yielded feasible resource allocations that met expected demand within capacity limits.
• Patient groups were generally assigned to consistent pathways, with some shifts based on the optimization objective, highlighting cost-efficiency vs. utilization trade-offs.
• Demand increased in the final months of life but was manageable under the planning assumptions.

Conclusions:

• The modeling framework demonstrates the feasibility of using optimization for anticipatory planning in end-of-life care.
• This approach supports more coordinated, efficient, and patient-centered care by enabling comparisons of different service configurations.