North West London Health and Care Partnership

Government Agency (Health and Adult Social Care)

Tony WillisJack HalliganDebbie WakeWill GilmourAlex SilversteinToby HydeHutan AshrafianSaira GhafurChris SainsburyDoogie BrodieScott CunninghamAmanda LucasJonty HeaversedgeHai Lin LeungAdam HigginsRoss Stone

The Institute of Global Health InnovationImperial College LondonImperial College Healthcare TrustMyWay Digital HealthAstraZeneca

1970520000000000

This project aims to develop and test data-driven end-user tools, such as apps or dashboards which can be viewed on a computer, that deliver better information visualisation and decision support directly to front-line clinicians and patients. Specifically, it will evaluate the use of risk prediction, powered by a machine learning algorithm, as a catalyst for change around early intervention, both in terms of medical management and patient self-management. The research has two aims: Develop methodology to rapidly revalidate three artificial intelligence models developed in Scotland in NWL datasets to help predict risk of complications for patients with T2DM. Assess how these models can best drive improved clinical outcomes specifically within the NWL Ecosystem

Developing and deploying AI-powered tools for clinicians and patients will provide calculated risk information to help them make better-informed decisions around treatment and self-management. This clinical decision support could improve quality of life, reduce morbidity, and reduce the healthcare burden through identification and treatment of high-risk individuals. Personalised risk profiling could also be a driver for patient behaviour change impacting on short-term markers such as weight, glucose control, medication adherence, foot care and ultimately reducing complications risk factors. Using diabetes as an exemplar, this tool will provide the opportunity to use real world data for the improvement of outcomes in patients with other long-term conditions.

The project is being carried out by: 1. Making use of existing diabetes predictive AI models developed in Scotland (developed by MyWay Digital Health). 2. Applying these to the North West London (NWL) population, by revalidating in NWL’s longitudinally linked dataset, Discover. 3. Considering, developing, and iterating prototypes for end-user interfaces that use risk prediction to drive clinician and patient behaviour change impacting on clinical decision making and patient self-management behaviour. 4. “Needs assessing” end-user requirements, UX and UI requirements and wider facilitators and barriers to success adoption 5. Testing and evaluating prototypes with clinicians and individuals with Type 2 Diabetes. 6. Participants with Type 2 Diabetes were recruited using the NWL Health Research Register. 13 participants in target areas were identified by ICHP recruiters, of which 6 were eligible and consented by the IGHI research team. Participants were aged between 32 and 70 and represented a mix of ethnic backgrounds, with half of participants coming from Black, Asian, and Minority Ethnic (BAME) backgrounds.

18/01/2022