Feasibility of Implementation of a Mobile Digital Personal Health Record to Coordinate Care for Children and Youth With Special Health Care Needs in Primary Care: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Electronic health record (EHR)-integrated digital personal health records (PHRs) via Fast Healthcare Interoperability Resources (FHIR) are promising digital health tools to support care coordination (CC) for children and youth with special health care needs but remain widely unadopted; as their adoption grows, mixed methods and implementation research could guide real-world implementation and evaluation. OBJECTIVE: This study (1) evaluates the feasibility of an FHIR-enabled digital PHR app for CC for children and youth with special health care needs, (2) characterizes determinants of implementation, and (3) explores associations between adoption and patient- or family-reported outcomes. METHODS: This nonrandomized, single-arm, prospective feasibility trial will test an FHIR-enabled digital PHR app's use among families of children and youth with special health care needs in primary care settings. Key app features are FHIR-enabled access to structured data from the child's medical record, families' abilities to longitudinally track patient- or family-centered care goals, and sharing progress toward care goals with the child's primary care provider via a clinician dashboard. We shall enroll 40 parents or caregivers of children and youth with special health care needs to use the app for 6 months. Inclusion criteria for children and youth with special health care needs are age 0-16 years; primary care at a participating site; complex needs benefiting from CC; high hospitalization risk in the next 6 months; English speaking; having requisite technology at home (internet access, Apple iOS mobile device); and an active web-based EHR patient portal account to which a parent or caregiver has full proxy access. Digital prescriptions will be used to disseminate study recruitment materials directly to eligible participants via their existing EHR patient portal accounts. We will apply an intervention mixed methods design to link quantitative and qualitative (semistructured interviews and family engagement panels with parents of children and youth with special health care needs) data and characterize implementation determinants. Two CC frameworks (Pediatric Care Coordination Framework; Patient-Centered Medical Home) and 2 evaluation frameworks (Consolidated Framework for Implementation Research; Technology Acceptance Model) provide theoretical foundations for this study. RESULTS: Participant recruitment began in fall 2022, before which we identified >300 potentially eligible patients in EHR data. A family engagement panel in fall 2021 generated formative feedback from family partners. Integrated analysis of pretrial quantitative and qualitative data informed family-centered enhancements to study procedures. CONCLUSIONS: Our findings will inform how to integrate an FHIR-enabled digital PHR app for children and youth with special health care needs into clinical care. Mixed methods and implementation research will help strengthen implementation in diverse clinical settings. The study is positioned to advance knowledge of how to use digital health innovations for improving care and outcomes for children and youth with special health care needs and their families. TRIAL REGISTRATION: ClinicalTrials.gov NCT05513235; https://clinicaltrials.gov/study/NCT05513235. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46847.
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 | 0,092 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,007 |
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 ».