Improving community health care logistics using Operational Research

Community nursing teams in England are part of an NHS Community Trust. Everyday each Trust sends nurses out to visit hundreds of citizens in their own home to provide care to meet their needs. The process of planning home visits is largely manual (paper and pencil) and planned a day in advance. Nurses of different bands and even within the same band have different skills, for example some nurses are able to deliver wound care and some nurses are not. Home visits are allocated to nurses in a way that makes sure that patients care needs are met (for example, an insulin injection must be administered between 9am and 11am ), that the cost of delivering care is minimised (for example, by clustering patients by geographic location) and that nurses and patient preferences are met (for example, variation in care activity for nurses, and continuity of care for patients). Much of community nursing work is planned, however, it is common for teams to receive urgent referrals during a working day. This type of planning problem is well known to OR: it is very difficult for manual planning of home visits to achieve all its aims. To make this problem simpler in practice large regions are broken down into smaller regions and planning takes place only for the next day. Such an approach works but is likely to be missing benefits for patients and efficiency savings for the NHS offered by looking at the problem as a whole.
In 2018, Dr Carlos Lamas-Fernandez and Dr Thomas Monks from NIHR CLAHRC Wessex’s Data Science Hub worked with Solent NHS Trust to learn from both academic research and the practice of community nursing to develop a novel decision support tool (DST) to support community teams to improve patient care and efficiency. This project builds on that work and provides extensions to its algorithmic tools, like extending it to social care settings.