Digital Innovation With Remote Management and Predictive Modelling to Integrate COPD Care With Artificial Intelligence-based Insights: An Acceptability, Feasibility and Safety Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- Technical feasibility
研究概览
简要总结
DYNAMIC AI is an MHRA-regulated medical device trial that will examine the feasibility of using AI predictive models within COPD multi-disciplinary team meetings, to allow clinicians to prioritise and optimise COPD management.
详细描述
Background: COPD need, challenge and opportunity.
Chronic obstructive pulmonary disease (COPD) is a global healthcare challenge. Whilst care-quality gaps exist across the continuum of patient's with COPD's presentation, COPD exacerbations are responsible for a large proportion of the disease-burden, adverse outcomes and healthcare costs. People with COPD prioritise the avoidance of exacerbations and resultant hospitalisations. The Patient Charter for COPD (March 2021), called for proactive, preventative management to reduce the risk of exacerbations and premature death1. Digital transformation with co-designed digital tools and emerging innovations such as wearable sensors and AI-based predictive modelling offers the opportunity to address these COPD care-quality gaps and achieve this care re-orientation.
DYNAMIC: NHS GG&C COPD digital service transformation, implementation and evidence.
'DYNAMIC' (Digital Innovation with Remote Management and Predictive Modelling to Integrate COPD Care) program commenced in 2018. This was based on an innovation partnership between NHS GG&C respiratory medicine and West of Scotland innovation teams and with LenusHealth (then StormID). Initial funding was from a digital health technology catalyst award from InnovateUK. Subsequent Scottish Government Technology Enabled Care (TEC) Program funding has continued the service and allowed scale-up provision.
Our vision was to initiate digital transformation of a COPD service with the development, implementation, and evaluation of co-designed digital tools. We co-designed the "LenusCOPD' patient and clinician web applications and support website based on cycles of user experience testing. We aimed to establish tools which would be sustainably used by patients during follow-up, and support COPD co-management with an anticipation of achieving reduction in respiratory-related admissions and occupied bed days in a high-risk COPD cohort. Evidence was gathered in the 'RECEIVER' observational cohort study and subsequently in the scale-up 'DYNAMIC-SCOT' service evaluation.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Current active user of NHS Scotland COPD digital service. Resident and under care of NHS GG&C COPD multidisciplinary team. Consent, access to technology (smartphone, tablet, computer and daily internet access), ability to understand written English and confirmed diagnosis of COPD are requirements for use of the COPD digital service
排除标准
- •Lack of provision of informed consent.
结局指标
主要结局
Technical feasibility
时间窗: 12 months
Technical Feasability of presenting live artificial intelligence-based 12-month mortality risk-prediction score from the LenusCOPD AI insights application, to COPD clinicians' multi-disciplinary team meetings in NHS GG\&C: Evaluation of feasibility will be proportion of participants with have adequate source data in LenusCOPD and who have 12-month mortality model-risk scores calculated and presented for MDT review in the AI-insights model app
Acceptability to patients
时间窗: 12 months
Evaluation of acceptability to patients with COPD of presenting live artificial intelligence-based 12-month mortality risk-prediction score from the LenusCOPD AI insights application, to COPD clinicians' multi-disciplinary team meetings in NHS GG\&C : Evaluation will be based on proportion of patients invited who consent to participate in the DYNAMIC-AI study.
Safety
时间窗: 12 months
Safety of presenting live artificial intelligence-based 12-month mortality risk-prediction score from the LenusCOPD AI insights application, to COPD clinicians' multi-disciplinary team meetings in NHS GG\&C Evaluation of safety will be based on occurrence of device-related adverse events and from the prospective evaluation of model risk scores - actions of clinicians based on model risk scores and calibration of predicted events : occurred events
次要结局
- Expand dataset for training and validation(12 months)
- acceptability and technical feasibility experience(12 months)
- preliminary experience and utility dataset(12 months)
- Evaluate technical feasibility and descriptive adoption experience(12 months)
