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COMPLETED: Improving community health care planning

Improving community health care logistics using Operational Research

Principal Investigator: Dr Carlos Lamas-Fernandez

Team members: Dr Carlos Lamas-Fernandez, (Research Fellow in Operational Research, University of Southampton), Professor Peter Griffiths (Chair of Health Services Research. University of Southampton), Dr Antonio Martinez-Sykora (Associate Prof of Business Analytics. Southampton Business School, University of Southampton), Dr Tom Monks (Associate Professor of Health Data Science, University of Exeter)

Start: 1 October 2019

Ended: 30 September 2021

Partners: University of Southampton, Solent NHS Trust and Abicare

Lay summary

Operational Research (OR) is the application of computer and mathematical modelling to support decision making. In health services research, OR aims to improve patient outcomes, increase efficiency and enhance health professionals and citizens understanding of how an NHS service achieves good performance. In this study, we will use OR to improve the quality of patient care by supporting community nursing teams organise how they visit people in their own home.

What did we acheive?

We found that it is possible to develop algorithms that create routes and schedules automatically for district nurses. These algorithms can incorporate many practical constraints that nurses encounter during planning, and produce solutions that are optimised to use their time efficiently.              

We found out that, with minor modifications, the algorithm can also support social care workers in a similar manner.

What difference can this new knowledge make?

These algorithms are a stepping stone that bring closer the academic research (typically on idealized problems that do not work on practice) to the reality of nurses that currently organise their workload on a laborious manual process. Both their planning time and the extra time spent on the road (e.g. by doing a route larger than it could have been) can be saved and utilized to care for patients.

Why is this important?

  • Patients will benefit from a more efficient workforce, who can as a result have more time to care for them. Further, they might also benefit from better planned visits which might include their preferences.

  • Care providers can use these kind of tools to plan their workload more efficiently, save costs on their operations and reduce the burnout of the nurses in charge of doing manual planning.

  • Policy makers can run these kind of tools to test hypothetical scenarios (e.g. how does service delivery change with an increase of the demand, when we hire more district nurses or if we train part of our staff?). Care providers can also assess

What's next?

  • We continue working to improve our algorithms and liaise with social care companies to explore how they can be used in practice.

  • We are looking into integrating them as demand estimation tools in other relevant problems, such as complex discharge from hospital.

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