- Run by University College London (UCL), in partnership with Care City, North East London Foundation Trust, the Bayswater Institute and UCL Partners.
- Focusing on community health care at two sites in North East London.
- Aiming to identify the scope for transformational efficiency gains in home-based NHS care.
- Will use operational research, mathematical modelling and systems analysis to identify workforce operations used by other industries that could deliver improved efficiency.
NHS staff who provide home-based care spend a lot of time travelling, and the care provided by different professionals and provider organisations is often poorly coordinated. More efficient ways of delivering this care could reduce costs while also improving patient and staff experience.
This round two Efficiency Research project will explore whether workforce operations used by other industries can improve the efficiency of home-based NHS care, and if so, how. For example, online supermarkets use operational research algorithms to design delivery routes that incorporate customer preferences on delivery times, while minimising journey times. The project will be led by operational researchers at University College London (UCL).
The team will first investigate what efficiency means to patients, professionals and health care commissioners, exploring agreements and differences in their perspectives. They will also build shared understanding by developing mathematical expressions linking the different aspects of efficiency. This information will form the basis for modelling work to identify innovations and strategies in the research literature that could improve efficiency. The work will include assessing the potential for some of the tasks currently performed by NHS staff to be carried out by the social care or voluntary sectors.
The project will look at the current delivery of home-based NHS services at two sites (Barking and Dagenham, and Redbridge) and analyse the capacity for change in these systems. The team will identify strategies that offer the best prospects of major efficiency gains at the two sites, as well as generalisable lessons for other sites.