Can We Improve Quality of Care in a Diabetes Clinic Through Patient-Entered Electronic Health Data?: Protocol for a Randomized Controlled Trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND Quality care for pediatric type 1 diabetes (T1D) requires frequent, multidisciplinary visits. Technological and clinical innovation have led to changes in T1D management resulting in increasing data exchange required during these visits. Capturing comprehensive personal health and diabetes-related information discretely and integrating it into the clinical workflow is critical for optimal T1D care, but time consuming. Time spent on data transfer often results in less time for holistic care, and can result in unmet needs for patients, families and health care providers, and increased time pressures in clinic. To address this, the Children’s Hospital of Eastern Ontario (CHEO) developed a caregiver proxy-reported questionnaire distributed via the MyChart® patient portal, allowing families to input care information ahead of visits, with the aim of dedicating more clinic time to personalized care. The launch of this tool that integrates caregiver-entered information directly into the physician’s documentation workflow, brings the opportunity to systematically evaluate its impact on care quality and efficiency, with potential implications for broader adoption. OBJECTIVE Our objective is to evaluate the impact of a caregiver proxy-reported, EHR-integrated pre-clinic questionnaire (MyChart questionnaire) on the quality of care in a pediatrics diabetes clinic, through measurement of its impact on caregiver-perceived quality of care compared to standard of care using two validated measures of care quality. We also aim to explore the impact of the intervention on glycemic control and visit efficiency. METHODS We conducted a single-centre, parallel-group randomized controlled trial designed for 222 children with T1D. Participants were randomly allocated in a 1:1 ratio to either the intervention (MyChart questionnaire) or standard care. Our primary outcome is caregiver-perceived quality of care as measured by the Patient’s Evaluation of the Quality of Diabetes Care (PEQD) at 8 months, administered with caregivers serving as proxy respondents for patients. Secondary outcomes are the PEQD at 4 months and Perceived Quality of Medical Care (PQMC) at 4 and 8 months. Tertiary outcomes include glycemic control and physician-reported visit efficiency at 4 and 8 months. ANCOVA models will be used to assess changes between baseline and post-intervention outcomes across treatment groups. RESULTS Recruitment for this study began in April 2023, and was completed in February of 2024, with a total of 139 participants successfully enrolled. Data collection has concluded, and the first results are expected in the spring of 2026. CONCLUSIONS This study is the first randomized trial to assess the impact of a caregiver proxy-reported, EHR-integrated, pre-clinic questionnaire distributed via a patient portal on caregiver-perceived quality of care in a pediatric care setting. Results will guide changes in health service infrastructure and delivery to enhance comprehensive data capture and improve care quality within and beyond pediatric T1D. CLINICALTRIAL ClinicalTrials.gov NCT05979077; https://clinicaltrials.gov/study/NCT05979077?term=NCT05979077&rank=1
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 | 0,030 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,010 | 0,008 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,090 | 0,012 |
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 ».