We worked in partnership to develop a working hypothesis, creating a new system to enable data to map the wider patient journey across Worcestershire.

Background

The Internet of Things (IoT) is defined as a vast number of “things” that can be connected to the internet. These multiple devices can share data to serve a common purpose. ​

Devices like hearing aids, mobility aids, glucose monitoring devices and many others not only have the potential to help patients with long-term conditions live better lives, they also have the potential to provide us with new data on a patient’s care pathway. Instead of only looking at touchpoints like GP appointments and A&E visits, we can now, through these devices, assess the patient’s health on an ongoing basis and identify critical points in the pathway where preventative measures can be effectively taken.​

The only thing missing was an advanced system that would enable the joining of assistive technology data to NHS and social care data in real-time.​

The Innovation Partnership between our Strategy Unit and technology provider PredictX aimed to create this unique system.​

Working with Worcestershire City Council, we sought to use machine learning models to combine this data and test a working hypothesis. ​

Worcestershire has a relatively high proportion of older people compared with the rest of the United Kingdom. In 2014, the proportion of older people aged 65 or over in Worcestershire was 21.2% compared to the 17.3% national average. This population is expected to increase steeply up to 2030 and beyond.  ​

Overall the county has good health outcomes compared to the English national average, however, since 2016, there has been a general pattern of decreasing gap between Worcestershire and England, particularly for the principal mortality rate. ​

Action

Using machine learning, we merged NHS and social care data with data from assistive technologies to better understand the care pathways of each patient using A&E and emergency services. ​

Data auditing procedures were set up at each stage of the process to ensure quality and resulting model accuracy when combining data from different sources and in different formats.​

Undertaking this audit process and data quality checks between the NHS, City Council and assistive technology datasets drove the development of a new, working hypothesis​.

Impact

The working hypothesis we developed will validate the opportunities machine learning can bring once assistive technology is introduced. These opportunities include:​

• an early warning system on an individual being at risk of needing additional services or presenting as an emergency admission at hospital ​
• use of integrated data and resultant machine learning models to predict and plan where assistive technology can be a major component of the service provision for a local population.​

​This data is now enabling the partnership to map the wider patient journey so Worcestershire City Council can know what cost savings can be achieved by deploying the right assistive technology services.​

Further information

If you would like more information about our services, you can contact us on strategy.unit@nhs.net