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AI-guided clinical coaching is a nurse-led proactive care model, which remotely supports patients identified by machine-learning-based case finding. This project seeks to enhance the algorithm to better identify suitable patients, and implement it on a testbed site.
With a clear clinical case established through a randomised control trial. HN are working to prove the feasibility of: (i) scaling the case finding data-engineering already in place in England to run securely, legally and efficiently in a cloud-based context (ii) proving that it is feasible to take the same approach to NHS Greater Glasgow and Clyde within the different clinical, data and cultural context. This process will reduce the technical barriers to adoption and allow much earlier identification of patients at risk of UEC, identifying the minimal dataset and cohort build to be able to deploy the solution in real-time. The end goal is a system to support 14,350 patients by 2024, saving the NHS close to £100m.
[embed]https://sbrihealthcare.co.uk/wp-content/uploads/2021/03/Health-Navigator-video-1-1-1.mp4[/embed]
| Project | Testing the technical feasibility to both scale and spread proven AI-driven identification of high-cost, high-need patients with long term conditions, for an already proven intervention to reduce unscheduled care demand |
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| Description |
AI-guided clinical coaching is a nurse-led proactive care model, which remotely supports patients identified by machine-learning-based case finding. This project seeks to enhance the algorithm to better identify suitable patients, and implement it on a testbed site. With a clear clinical case established through a randomised control trial. HN are working to prove the feasibility of: (i) scaling the case finding data-engineering already in place in England to run securely, legally and efficiently in a cloud-based context (ii) proving that it is feasible to take the same approach to NHS Greater Glasgow and Clyde within the different clinical, data and cultural context. This process will reduce the technical barriers to adoption and allow much earlier identification of patients at risk of UEC, identifying the minimal dataset and cohort build to be able to deploy the solution in real-time. The end goal is a system to support 14,350 patients by 2024, saving the NHS close to £100m. [embed]https://sbrihealthcare.co.uk/wp-content/uploads/2021/03/Health-Navigator-video-1-1-1.mp4[/embed] |
| Funding | £ 859,655 |
| Competition | Competition 17 - Urgent and Emergency Care |
| Competition Date | October 2020 |
| Health Innovation Network Partner | Health Innovation Yorkshire and Humber |
| Website | https://www.hn-company.co.uk |