Transformation Partners in Health and Care > News > System Level Demand and Capacity Modelling for Imaging Services

The Transforming Cancer Services Team and Elective Care Improvement Support Team have been supporting Integrated Care System (ICS) led demand and capacity (D&C) modelling for CT, MRI and ultrasound across North Central and North East London systems.

The approach was initially developed with the Southwest London ICS using the national demand and capacity teams ‘Core Model’. It has now been successfully delivered applying the standardised methodology across two further systems in London building demand and capacity modelling into strategic resources. It provides outputs to enable evidence-based decision making around the re-allocation of capacity within a system, opening opportunities for new ways of care delivery.

A view of imaging services across systems hadn’t been modelled and aggregated previously, therefore the outputs were extremely valuable in order to set a level of required capacity to achieve waiting time targets for patients and inform medium- and long-term planning for the ICS.

The D&C modelling started by identifying key stakeholders which included overall senior leadership involvement along with clinical, operational and informatic leads from each provider. This is a large complex piece of work and collaborative working was essential to enable successful delivery of the project.

Relationships were built at pace, whilst working very closely with Trust leads, intensive support was provided for the technical data extraction, capacity requirements, analysis, interpretation, validation and sense checking ensuring outputs were reflective of the imaging services.

The key purpose of the D&C modelling was to achieve a detailed understanding of the demand and capacity position for imaging services, including the measurement of growth based on historical requests to forecast the capacity needed in the future. At system level the outputs have the potential to inform mutual aid opportunities and the use of community diagnostics centres to increase activity in the region as D&C modelling significantly impacts the way service planning is considered.

The modelling raised numerous challenges such as competing demands and often poor and missing data which impacted on project delivery. These were overcome through providing weekly support to Trusts and escalation via the ICS when appropriate.

The provider and system level outputs include –

• A capacity position demonstrating surplus or deficit in hours per week
• A sustainable waiting list range identifying if the service is above, below or within the range. A backlog clearance calculator can determine how many additional patients should be seen per week to clear the backlog
• Growth is calculated based on historical weekly requests for elective, and non-elective services including suspected cancer referral growth
• Based on the historical growth a capacity forecast is calculated to suggest what the capacity position will be by 2025 if no changes to the service are made
• Efficiency measures are also included such as removals, DNA, and cancellations
• The provider level report includes recommendations and opportunities for improvement in line with best practice
• The system level report along with the outputs above can demonstrate warranted and unwarranted variation across the system, identify opportunities for improvement and where to prioritise.

In addition to the modelling there is the ability to test theoretical scenarios around demand management which is extremely helpful for planning such as the use of community diagnostic hubs and the impact on the service. Capacity scenarios included extended day or 7-day working and use of mutual aid between Trusts.

On completion of the project, a detailed handover helped to support the sustainability of a system level D&C modelling approach. ICSs have set up support to embed the ongoing D&C exercise as business as usual now that a level of capability, competency and skilling up has been realised due to the intensive training during the modelling process.

A demonstration of an analysis tool developed to interpret the model outputs is shared as part of the handover. Also, an example of the use of statistical process chart (SPC) to monitor key indicators for ongoing operational management following D&C modelling and how this is beneficial. Having a demand ‘baseline’ (weekly average number of requests) is a straightforward measure and helps to understand when there are weeks of high demand, enabling services to flex capacity accordingly for smoother throughput, and a more consistent patient wait time.

We feel we have supported to set up a community of practice within the systems, inter-relationships have been built which is extremely positive, we experienced valuable engagement and commitment from colleagues which remained throughout the duration of the project.

Development of an evaluation and reflective piece to capture the learning and improvements made following the modelling has been shared. This will be summarised, and a case study produced.