Case Studies

Simulation-modelling led review of district nursing services for a large community and mental health provider in North West England

Simulation-modelling led review of district nursing services for a large community and mental health provider in North West England

Simulation-modelling led review of district nursing services for a large community and mental health provider in North West England banner icon

Context

Niche were commissioned to produce a capacity and demand model, along with a baseline review, of the district nursing service of a large community and mental health Trust in North West England. The Trust provides district nursing services across a large geographical area, covering both rural and urban areas, and the socioeconomic and cultural conditions of its populations vary significantly. Most patients on caseload are housebound and many have been discharged from an acute setting.

Working closely with GPs, social workers, therapists and secondary care, the service delivers a wide range of interventions. These range from the initial assessment of needs, taking observations, administering treatments, through to highly complex interventions including the provision of palliative care. Effective running of the service is critical to ensure patient safety and flow across the health and care system, and in March 2022, a national two-hour urgent community response standard was introduced across many of the interventions which district nurses provide.

The last decade has seen a sharp decline in the district nursing workforce nationally. This has been coupled with increasing demand on the service in terms of both rising numbers of patients, as well as increasing complexity of need. Failure to address this gap between capacity and demand could have severe implications for care quality in the community, and if patients cannot be seen at home, could lead to increased pressure ‘downstream’ whereby patients later present in Emergency Departments, or cannot be discharged from a hospital setting when they can be cared for by district nurses at home.

Modelling expected demand is a key step in ensuring current services are operating safely, and for planning future capacity. There is, however, a shortage of recognised evidence or guidance around recommended staffing levels for district nursing services. The Trust commissioned Niche to produce a robust and independent base of evidence to support the planning of future services, assess whether the current workforce was sufficient to meet expected future demand, and quantify the size of any gaps.

 

What we found

The project proceeded via three-stages of both qualitative and quantitative work:

  1. The first phase consisted of interviews with senior clinical and operational staff to understand how the service was operating and identifying potential scenarios of change, alongside validating historic activity data, and engaging widely with frontline staff to understand their experience of working in the service, including how long interventions generally take to deliver.
  2. We then developed a baseline model forecasting demand over the next 5 years based on the current configuration of services, along with a set of scenarios agreed with the Trust which might impact future demand. Engagement meetings with each district nursing team within the Trust were undertaken at key stages of the modelling, both to feed back interim results and to ‘sense check’ the data against staff’s experience of working in the service.
  3. Finally, we modelled each scenario which staff had identified to allow the main drivers of the future gap between capacity and demand to be identified. A final optimised demand model, combining the scenarios with the largest impact, was then produced, along with set of findings and recommendations of how to maximise quality of care with the best use of resources. The final model was structured by locality team, staff grade, intervention, and level of urgency to provide a high level of nuance to the final recommendations.

All the findings of the final report were fully accepted.

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