My Lung Health Coach Companion App: Development and Co-design of an Electronic Patient Record-integrated Companion App for a Virtual Chronic Obstructive Pulmonary Disease Education and Self-management Support Program
Bibliographic record
Abstract
Abstract Rationale: Chronic Obstructive Pulmonary Disease (COPD) is a highly prevalent airways disease that affects 2.6 million Canadians. COPD exacerbations are one of the leading causes of emergency room visits and hospitalizations nationally and COPD costs the healthcare system $1.5B annually. Managing COPD requires effective patient self-management and education, but few patients have access to self-management interventions, which improve health-related quality of life and reduce COPD hospitalizations. To address this care gap, we developed “My Lung Health Coach” (MLHC), a freely available evidence-based COPD self-management program that connects people virtually with experienced certified respiratory educators (CREs) to provide structured COPD education and self-management support. To further enhance the MLHC program, we sought to develop a companion mobile app integrated into the electronic patient record, ensuring that patients have secure and timely access to MLHC education and self-management resources as they complete their journey through the program. Methods: The MLHC companion app was developed using Epic Care Companion architecture. We first developed a prototype app, and then undertook a user-centered rapid-cycle design process informed by patient focus groups and stakeholder meetings. We elicited feedback on app usability, content, format, acceptability, and comprehensibility, and evaluated focus group transcripts qualitatively using thematic analysis. Quantitative evaluation of iterative app usability was also conducted using the System Usability Scale (SUS). Results: The prototype app contained educational and self-management tasks linked to each MLHC session. We held 4 patient focus groups including 7 people (6 were patients with COPD who had previously completed the MLHC program, and 1 was a caregiver for someone with COPD). The mean age of participants was 69.4 years (SD 7.1), 85.7% were women, 33.3% had a history of COPD exacerbation in the previous year, and the mean CAT score was 18.1 (SD 6.8). Thematic analysis of focus group transcripts (see Table 1) and 2 stakeholder meetings resulted in several critical changes to improve final app design and implementation. App SUS score was initially low (53.75 in focus group 1) but improved with iterative app changes (mean SUS across focus groups 2-4 was 82.0 – “excellent” range). Conclusion: We co-developed an electronic patient record-integrated companion app for MLHC, a virtual COPD education and self-management support program. Our iterative user-centered design process resulted in sustained and high system usability. We will now launch a pilot study evaluating the feasibility and effectiveness of MLHC with the companion app in patients with COPD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".