Better home care through data-driven capacity planning
How can we optimise capacity planning in home care? Mathematician Yoram Clapper investigated how we can balance the needs of healthcare facilities, practitioners and clients. He argues that more efficient planning is possible.
Home care requires careful planning to ensure efficient use of resources and client satisfaction. Clapper examined various algorithmic and analytical methods to determine and efficiently use the capacity of caregivers to meet client demand. The results are data-driven and provide healthcare organisations with insights to use capacity more efficiently for better planning.
Balance
The models are inspired by actual practices in home care. They are formulated as optimisation problems that balance efficiency, client-centred care and a manageable workload for caregivers.
According to Clapper, efficient planning is possible without reducing the quality of care or significantly increasing the workload of practitioners. Within his research, Clapper and his colleagues developed several algorithmic and mathematical methods to provide insights and solutions in capacity planning. For instance, they created a model for the optimal size and composition of teams, taking into account avoiding broken shifts.
Efficiency gains
They also developed an evolutionary algorithm for routes and scheduling in home care. This can optimise performance in terms of travel time, overtime and waiting times. Clapper: 'The algorithm shows near-optimal performance for small cases and achieves 41% efficiency gains over schedules from a case study.' Furthermore, they created models that take into account continuity of care and handling unexpected client visits more efficiently.
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