DYNAMIC-AI
Research type
Research Study
Full title
Digital Innovation with Remote Management and Predictive Modelling to Integrate COPD Care with Artificial Intelligence-based Insights: An Acceptability, Feasibility and Safety Study
IRAS ID
287942
Contact name
Chris Carlin
Contact email
Sponsor organisation
NHS GG&C
Clinicaltrials.gov Identifier
Clinicaltrials.gov Identifier
N/A, N/A
Duration of Study in the UK
0 years, 8 months, 31 days
Research summary
We are proposing undertaking this DYNAMIC-AI study to establish the acceptability to patients with COPD and technical feasibility of presenting AI model-based risk prediction scores to the COPD MDT.
We have established in the 'RECEIVER' trial that the 'LenusCOPD' based COPD digital service has shown sustained patient usage and improved COPD outcomes (significantly increased time to hospital admission or death, reduced respiratory-related hospital admissions and occupied bed days) in patients who are setup in the service. Information on the service and 'how it works' is at https://support.nhscopd.scot
In parallel with this digital transformation, we have undertaken machine-learning based risk prediction model development and validation using a large de-identified dataset of COPD patients from NHS GG&C SafeHaven. With routine healthcare data these models can predict a patient with COPD's risk of 12-month mortality, 3-month hospital admission and 3-day COPD exacerbation. The accuracy, performance, explainability and fairness metrics of these models are ready for adoption within an implementation-effectiveness evaluation framework. We therefore propose to undertake a 12-month observational cohort study, with planned 3-monthly interim analyses of the co-primary endpoints. This study design ensures scope to adapt the implementation strategy (eg adapt patient information, resolve unexpected issues with technical architecture) if these interim evaluations demonstrate suboptimal effectiveness. The secondary objective analyses will establish preliminary data including clinician interactions and actions based on model risk scores in a live clinical environment. These will help evaluate impact of risk-score triggered patient or clinician actions which may influence COPD events and model accuracy. This study is therefore an essential preliminary investigation, which will inform future implementation and design of subsequent pivotal clinical investigation(s) of COPD AI-insights, with the appropriate governance and safety scrutiny ensured by this feasibility clinical investigation.
REC name
West of Scotland REC 4
REC reference
22/WS/0152
Date of REC Opinion
21 Dec 2022
REC opinion
Further Information Favourable Opinion