Why we offer analytics & modelling
High quality analysis helps a variety of audiences to understand health and care challenges and decide how to respond to these. By modelling potential solutions to problems, we can predict the impact of transformational change for services and those who use them.
Our offer includes:
- Analysis to better understand and identify the challenges facing services, such as understanding drivers of demand and future population needs.
- Analysing health inequalities and helping decision makers decide what action to take.
- Modelling future demand and translating how this impacts capacity requirements for resources and the workforce.
- Predicting the likely impact of re-designing patient pathways on outcomes.
We are well positioned to deliver this work through our team of analysts with experience across different care settings. We have access to key national datasets through secure repositories to analyse the delivery of health and care across the country.
We work in close partnership with our clients so that we can fully understand their needs and ambitions. We take the time to agree modelling and analytic approaches, datasets we will work from, key assumptions, and the format of outputs.
We take a pragmatic approach to our modelling work to understand what is ‘good enough’ to support effective decision-making.
We are committed to providing open and transparent analysis and modelling. We aim to undertake all our work using open-source programmes for effective collaboration. Therefore, we use open-source languages such as R and Python, and use GitHub to share our work.
This approach enables us to deliver projects including:
- Creating interactive analysis and dashboards using Shiny, Quarto, Tableau and Power BI.
- Modelling patient pathways using Discrete Event Simulation (DES) methodologies to understand the expected behaviour of a pathway and the more unlikely scenarios.
- Creating engaging data visualisations for our clients. Examples include:
- Geospatial analysis with interactive maps that can be integrated with travel time analysis.
- Visualising patient flow using Theographs and Sankey diagrams.
- Exploring relationships and interactions between services with network graphs.
Case studies
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