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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».