What if you could use predictive analytics to create better strategies for the way care services are provided?
That is the question that PredictX, Midlands and Lancashire Commissioning Support Unit (CSU) and the City of Wolverhampton Council sought to answer through their NHS Social Care Digital Programme.
Together with the funding and support of NHS Digital, this private-public partnership has developed a solution that has, so far, achieved the following three outcomes:
1. A better understanding of pressure points in the existing care system. Dashboards combining health and social care data show key metrics such as A&E attendances, hospital admissions, hospital discharges and capacity in care homes. This intelligence has given the City of Wolverhampton Council a better understanding of how the system can be improved.
2. A machine learning model predicting how many A&E patients end up being admitted to hospital and what happens to them when they are discharged. The City of Wolverhampton Council can use this data to provide best-fitting social care packages for people.
3. A new approach to population health involving the creation of care service user profiles to better determine service need in a geographical area.
The work has involved analysing the data of 3,000 users of domiciliary care and showing insight into:
The services they use.
Touchpoints they have with organisations in the system.
Socio-economic data such as indices of deprivation.
This has generated seven key profiles and unearthed an insight - amongst several others - that there are residents with long-term health conditions who do not access many services, whilst there are other residents with no conditions who access multiple services. The richness of data makes it possible to drill down into this further, ask why and re-organise services to address this.
The next step is looking at the insights from the profiles and applying them to real-life situations to see whether they can inform the way that the partners deliver services.
The plan is to use the data to better manage the health and care needs of local communities to help people stay independent for longer and take pressure off our more stretched services.