RECEIVER: Digital Service Model for COPD

  • Research type

    Research Study

  • Full title

    Remote-management of COPD: Evaluating Implementation of Digital Innovations to Enable Routine Care

  • IRAS ID

    261346

  • Contact name

    Chris Carlin

  • Contact email

    ccarlin@nhs.net

  • Sponsor organisation

    NHS Greater Glasgow & Clyde

  • Clinicaltrials.gov Identifier

    NCT04240353

  • Duration of Study in the UK

    2 years, 0 months, 1 days

  • Research summary

    Chronic obstructive pulmonary disease (COPD) is a serious but treatable chronic health condition. Optimised management improves symptoms, complications, quality of life and survival. Disease exacerbations, which have adverse outcomes and often trigger hospital admissions, underpin the rising costs of managing COPD (projected increase in UK to £2.3bn by 2030). The costs and care-quality gap of COPD exacerbations, coupled with the global rising prevalence present a major healthcare challenge.

    Our study proposal, which has been developed in partnership with patients, clinicians, enterprise and government representation is to conduct an implementation and effectiveness observational cohort study to establish a continuous and preventative digital health service model for COPD.

    Our implementation proposals comprise: -
    • Establishing a digital resource for high-risk COPD patients which contains symptom diaries (structured patient-reported outcome questionnaires), integrates physiology monitoring (FitBit and home NIV therapy data), enables asynchronous communication with clinical team, supports COPD self-management and tracks interaction with the service (for endpoint analyses).
    • Establishing a cloud-based clinical COPD dashboard which will integrate background electronic health record data, core COPD clinical dataset, patient-reported outcomes, physiology and therapy data and patient messaging to provide clinical decision support and practice-efficiencies, enhancing delivery of guideline-based COPD care.
    • Use the acquired dataset to explore feasibility and accuracy of machine-learned predictive modelling risk scores, via cloud-based infrastructure, which will be for future prospective clinical trial.

    Our primary endpoint for the effectiveness evaluation is number of patients screened and recruited who successfully engage with this RECEIVER clinical service. The implementation components of the project will be iterated during the study, based on patient and clinical user experience and engagement. Secondary endpoints include a number of specified clinical outcomes, clinical service outcomes, machine-learning supported exploratory analyses, patient-centred outcomes and healthcare cost analyses.

  • REC name

    West of Scotland REC 3

  • REC reference

    19/WS/0072

  • Date of REC Opinion

    14 Jun 2019

  • REC opinion

    Further Information Favourable Opinion