Scope

Background

Understanding system demand and capacity is essential for operational and strategic healthcare planning. A common approach to demand and capacity planning is spreadsheet modelling and simulating patient flow through the health system. Within each Integrated Care System (ICS) these kinds of models are possible to make, and often already exist. It is much harder to do this at a regional level because the structures and populations in each ICS are very different to one another. A tool that is developed to try and capture the intricacies and detail in one ICS is therefore likely to be unrepresentative and open to challenge if used to inform local decision making in another. Additionally, each ICS has developed their own bespoke tools to understand demand and capacity, which already supports local decision making, particularly in the acute setting.

Project scope

This project aims to understand demand and capacity in a novel way. Using temporal data on demand (e.g., population in older age groups) and capacity (e.g., workforce metrics) for each ICS geography as model inputs, this project aims to predict performance metrics (e.g., General Practice wait times above 4 weeks) using modern modelling techniques. The project uses publicly available data to generate predictions for each ICS. It also identifies which input metrics have the biggest influence on the performance metric being predicted.

Questions this work can support

Conceptually, the performance metrics should be impacted by the demand and capacity handles. Some of the demand and capacity metrics are ones that can be influenced by policy and investment. This work should be able to identify significant associations between the demand and capacity metrics and the resulting performance. This will help support high-level prioritisation for investment and policy making. For example, if it can be shown there is a strong relationship between social care funding and reducing No Criteria to Reside patients, this can be used to make a strong case for increasing social care funding, if this will be more than offset by the acute cost saving.

Proposed outputs

The outputs from this work would allow ICSs to see the relationships between their demand and capacity metrics and performance. The baseline modelling first produces a ‘do nothing’ performance trajectory, based on current/expected levels of demand and capacity. It then allows users, through ‘what-if’ scenario analysis, to sensitivity test the models for future years to understand how changes to the input metrics will affect future performance – thus allowing them to inform more optimal decisions on future spend and resource allocation.