Introduction
Chronic obstructive pulmonary disease (COPD) is a common, preventable and treatable disease, which is characterised by persistent respiratory symptoms and airflow limitation. The main risk factor for COPD is tobacco smoke but other environmental exposure may contribute. Respiratory symptoms include breathlessness, cough and/or sputum production. These symptoms are under-reported by patients.1 2 COPD may be punctuated by periods of acute worsening of these respiratory symptoms, often referred to as exacerbation, that can result in emergency department (ED) attendance or hospital admission. For many patients, COPD is associated with significant comorbidity, which increases its morbidity and mortality.
COPD is a major healthcare challenge, with worldwide rising prevalence. The Global Burden of Disease Study reported a prevalence of 251 million cases of COPD globally in 2016.3 It was projected to be the fourth leading cause of death worldwide by 2020.4 Reductions in exacerbation and hospitalisations are the outcomes rated as most important by patients with COPD.5 Effective delivery of evidence-based interventions for COPD—smoking cessation, influenza vaccination, pulmonary rehabilitation, personalised inhaled therapy, home oxygen therapy (where indicated) and home non-invasive ventilation (NIV, where indicated)—has been shown to reduce exacerbation and hospital admission.6 There are considerable barriers to uptake and delivery of these interventions.7 This care quality gap particularly affects outcomes from COPD exacerbation. Exacerbation is the main driver of healthcare costs (estimated annual National Health Service (NHS) cost of managing COPD is £1.9 billion.8 There is an urgent requirement for an innovation-based service redesign that can integrate care to deliver these evidence-based interventions and achieve reductions in COPD exacerbation and admissions.
Self-management also plays a key role in the treatment of COPD.9 Patients who can be successfully taught and supported with COPD self-management show a significant reduction in COPD admissions.10 11 The process of establishing a multidisciplinary community respiratory team in NHS Greater Glasgow & Clyde (NHS GG&C), which supports self-management in patients identified acutely as being high risk of hospital admission, has been associated with reduction in hospital admission rates.12
While interventions should be developed for patients with all severities of COPD, it is logical to target immediate efforts towards patients with ‘high-risk’ COPD, that is, those who are at most risk of exacerbation and hospital admissions. Established data indicate that patients with COPD who have had severe exacerbation (one requiring ED attendance or hospital admission) in the previous 12 months and/or have persisting hypercapnic respiratory failure requiring home NIV fall into this high-risk group.6 13 Interventions proven in this group can then be rolled out (if cost-effective) to the lower risk groups of patients with COPD.
Digital service model
Pilot data from NHS GG&C have highlighted the potential for digital innovations to predict COPD outcomes and support treatment uptake. Using qualitative methods, Slevin et al highlighted patient acceptance to take an active role in self-management using digital health technology with the potential for healthcare professionals to provide meaningful preventative care.14 Web and smartphone-based apps have shown the capability to facilitate disease self-management and support uptake of interventions.15 16 Although patient-focused digital tools currently exist for COPD, there is a limited evidence base for their use, with evaluations mainly performed in isolation and not integrated with established clinical services or statutory electronic health records (EHRs).
Most COPD management is currently based on a reactive approach, and delays in recognising treatable opportunities underpin COPD care quality gaps. For example, patients with COPD exacerbation typically have symptom deterioration for 2 days before seeking assistance, and then a potential delay of 2–5 days in accessing clinical care.1 Several studies have indicated the ability of regularly recorded patient-reported outcomes (PROs) and home NIV parameters to predict outcomes, including exacerbation and treatment success/failure in patients with COPD.17 18 Changes in activity measured by wearable devices have been shown to predict outcomes after COPD exacerbation.19 Currently, symptom diaries and other PRO questionnaires, activity, exercise and NIV data are obtained in routine practice in NHS GG&C. However, patient and clinician engagement is not consistent, data are not acquired systematically and are not often visible or actionable at key time points of patient–clinician interaction. These shortfalls, and the arising care quality gaps, could potentially be addressed by digitising this routine clinical care, improving the patient–clinician interface for data entry, and collating the acquired data.
Patient–clinician communication for COPD management, including supporting self-management, is currently dependent on face-to-face scheduled consultations, answer phone/email asynchronous messages from patient to clinician, and unstructured advocacy triggered or initiated communication from clinician to patient. These present several inefficiencies and risks. They could be overcome by digitalising the patient–clinician messaging system to support scheduling, remote management and support COPD self-management.
Home NIV successfully improves admission-free survival in patients who have persisting hypercapnic respiratory failure following life-threatening COPD exacerbation, with a number needed to treat of seven patients.20 NHS GG&C has successfully matched the outcomes from the HOT-HMV randomised clinical trial in our service adoption pilot delivering home NIV to eligible patients with COPD, using routinely available digital technologies (adaptive ‘auto-NIV’ modes, two-way remote monitoring via ResMed AirView platform).21 22 The challenge is to extend the evidence for this approach and obtain a service adoption playbook to enable this to be adapted and delivered at scale, by other clinical teams, within COPD integrated care.
Machine learning analysis and modelling based on available data shows significant promise in COPD predictive management. Data available in patient’s EHR at triage assessment can robustly predict outcome (admission, length of stay) from severe COPD exacerbation.23 24 The addition of physiology measurements to EHR data improves machine learning predictive model performance in other clinical conditions.25 Further evaluation of these analytics and predictive modelling, in a comprehensive dataset including PROs, physiology data and clinical events, is a logical step to determine their potential role in real-time or near real-time clinical use.
Objectives
Innovations which can empower patient self-management, facilitate integrated clinical care and support delivery of evidence-based treatment interventions are urgently required. In the Remote-Management of COPD: Evaluating the Implementation of Digital Innovation to Enable Routine Care (RECEIVER) trial, we propose to explore the implementation of a platform which digitalises these as additional—potentially assistive—components alongside routine clinical care. In our endpoints, we will determine participant utilisation, clinical service impact and clinical outcomes, to evaluate the feasibility of this approach versus current standards of care. A digital infrastructure for this support of routine clinical care would also provide a foundation to explore the feasibility of approaches to predict outcomes and exacerbation in patients with COPD, to be tested in future prospective clinical and regulatory trials.
Our aims are to establish a digitalised service model for ‘high-risk’ patients with COPD which will:
Integrate current routine clinical care within a digitally enabled remote management service infrastructure.
Enable delivery of remote management of COPD at scale within the NHS and other healthcare systems.
Capture relevant routinely acquired PROs, continuous physiology data and clinical event/episode data in a patient and clinician co-designed interface which enables engagement.
Facilitate evolution from a reactive to a proactive and preventative COPD service model.
Digital service components
The digital service model will sit alongside routine clinical care, aiming to assist and enhance current patient management. Key components of proposed digital service model for COPD are noted and summarised in figure 1. Tabulated information in online supplemental material 2 outlines how the RECEIVER trial components would be used in addition to current routine clinical care.
The COPD digital service components being used in the RECEIVER trial are:
Patient-facing web portal: this has been co-designed with patients and captures PROs and provides access to standard COPD self-management content. It also includes a messaging facility which can be patient or clinician initiated. Examples of visuals available on supporting website (https://support.nhscopd.scot).
Patient wearable device: Fitbit Charge 3 device (CE marked).
Remotely monitored home NIV: provision of this is our current standard of care for patients with hypercapnic respiratory failure, using AirView NIV remote management platform (ResMed). We have integrated our COPD digital service platform with the AirView platform, so that it acquires the NIV data unmodified for review where available. NIV therapy management will continue to be conducted through the AirView platform: the RECEIVER trial COPD platform is not used to modify or enhance patient NIV therapy.
User-designed clinical dashboard: presents integrated data with an aim of facilitating improvement in provision of guideline-based COPD care, and supporting COPD self-management.
Patient–clinician asynchronous messaging: to support routine clinical care.
NHS GG&C Azure cloud-based digital architecture: this provides and integrates the above services with existing NHS GG&C electronic healthcare systems. Diagram of digital architecture included in online supplemental material 3.