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Header image for the current page Maximising the impact of social prescribing through a case-finding tool using machine learning

Maximising the impact of social prescribing through a case-finding tool using machine learning

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Evidence suggests that social prescribing can lead to a range of positive health and wellbeing outcomes by supporting individuals to take greater control of their own health.

However, social prescribing resources are limited and need to be targeted on those patients who would benefit most leading to a positive impact for both individuals and the healthcare system. Arden & GEM’s Advanced Analytics Unit has developed a case-finding tool, based on a machine learning model, to enable social prescribers to proactively target patients in advance of a GP referral.

The challenge

Our solution

Bedfordshire, Luton and Milton Keynes (BLMK) Integrated Care System (ICS) approached the Advanced Analytics Unit (AAU) to design and develop a dashboard capable of identifying patients who may benefit from social prescribing and enabling users to measure and monitor service uptake.

A key feature of the dashboard is a case-finding tool, which utilises a machine learning model to effectively highlight and triage patients most likely to be suitable for referral to a social prescribing programme, without having to wait for a GP referral.

Social prescribing is a key part of the system’s wider ambitions to make personalised care business as usual. Providing the best tools and analytics possible to the workforce supports this ambition and enables them to make a difference.

What we did

AAU built a tool designed to assist social prescribing and primary care staff from several providers, including the VCFSE sector, in BLMK.

By leveraging and linking data from multiple primary and secondary care sources, within the data warehouse, near real-time information about the entire population of BLMK was available. The data was then used to create a Tableau dashboard showing key performance and prevalence data helping teams, practices, PCNs and the ICB to understand and monitor social prescribing uptake across different cohorts. These metrics also enable social prescribers to examine uptake and ensure equitable access across population groups.

Utilising machine learning
The team developed a machine learning model to calculate the likelihood that an individual would be referred to social prescribing based on historical patterns in social prescribing referrals. This score is then presented within a case-finding tool in order to identify and prioritise new patients who may benefit from a referral. The scores are shown alongside event-level information for health, social, economic, demographic and care utilisation to support decision making.

The tool has been iteratively developed with user testing to include:

Current and potential benefits

"Arden & GEM’s Advanced Analytics Unit has been instrumental in revolutionising the concept of social prescribing, offering a ground-breaking approach that will transform the lives of many patients. Their innovative thinking and data-driven solutions have allowed us to better serve our patient populations by connecting them to non-medical support and community resources, which are often crucial for their overall wellbeing. Working with the team has been a true pleasure, and their commitment to improving health service delivery is commendable."

Jamil Iqbal, Project Manager - BI & Analytics New Developments at NHS BLMK ICB