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Home > Our range of support > Integrated Care Systems and STPs > Insight > Supporting care coordination and management

Informatics, analytics and digital tools to support care co-ordination and management, risk and impactability models

We have extensive experience of developing and deploying risk prediction, risk stratification and impactability models which have the potential to improve service planning and patient care.

Demand for healthcare services has increased substantially over recent years, as medical technologies advance and patients become better informed and more empowered. We anticipate that these trends will continue placing increasing pressure on ICS budgets.

We support ICSs to adopt a range of strategies to ensure their resources are optimally targeted to improve patient outcomes and reduce health inequalities. Our tools are effective over a range of different timeframes, depending on customer needs.

We have developed effective risk stratification methods to help predict patients with the greatest risk of high-cost adverse outcomes, and impactability models to improve understanding of which patients might respond to interventions to moderate these risks, and avoid triple fail events.

Risk prediction methods use historical data to explore the relationship between a patient’s characteristics and a specific adverse outcome. They can be used to predict the risk of certain outcomes and future healthcare costs.

No single risk prediction method is capable of addressing all STP and ICS needs. We have developed a portfolio of methods which can be deployed and configured to respond to the most frequently occurring requirements and have the skills to develop entirely bespoke models to address new requests.

To support an STP or ICS select or commission an appropriate tool, we would first engage in a detailed dialogue to understand how the customer intends to deploy the tool and what decisions and practices it hopes to influence.

Risk prediction tools represent significant investments, not only in technical infrastructure, software and intellectual property, but also in training staff to use the tool. Meanwhile, a strong appetite to introduce risk stratification is not always underpinned by a clear rationale or business case.

We would encourage a robust conversation to explore the incremental cost and benefits of any planned investment and a rigorous study of the alternatives including more traditional approaches to case finding. Logic models can be used to understand how risk prediction forms part of a wider strategy and can usefully highlight the critical dependencies.

We are highly skilled and experienced in the design and delivery of evaluations, using sophisticated approaches capable of evaluating and measuring the impact of risk stratification tools to mitigate against the risks of worsening health inequalities and increasing costs. We are fully committed to impact evaluations and to ensuring that new approaches have been tried and tested before being rolled out more widely.

By closely collaborating with our customers, and with access to high level evaluation methodologies through our Strategy Unit, we can ensure that appropriate and proportionate evaluation techniques are applied to support decision-making.

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