ARE CHILDREN WITH MEDICAL COMPLEXITY (CMC) MEETING TRANSITION-RELATED MILESTONES BEFORE ENTERING THE ADULT HEALTHCARE SYSTEM? A CROSS-SECTIONAL CHART REVIEW
Notice bibliographique
Résumé
Abstract BACKGROUND Medical and technological advances have resulted in a growing cohort of children with medical complexity (CMC) who are reaching adulthood. These children are diagnostically heterogenous and require frequent hospitalizations, intensive community services, and medical technology at home to maintain their health. Transitioning CMC to the adult healthcare system has proven challenging for patients and their caregivers, who are comfortable and empowered in the paediatric setting. This multi-faceted process requires the expertise of an interdisciplinary team and one transition can take up to 100 hours of coordination. Although each patient is diagnostically unique, common requirements for transition arise. A standardized approach to transition would empower caregivers to be better advocates for CMC in the adult healthcare system. In order to implement an approach, a baseline understanding of this growing population is required. OBJECTIVES 1. To obtain demographic, medical, and transition-related data for CMC approaching transition to assess whether current transition practices are aligned with critical transition milestones and events, as detailed by key informants 2. To identify optimal targets for intervention in the transition process and produce a standardized toolkit to address gaps DESIGN/METHODS This was a cross-sectional chart review study. It was conducted at one of the largest Canadian complex care programs, which follows 400+ children. Prior to chart review, critical transition-related parameters and milestones were ascertained from interviews with key informants, such as transition coordination experts, the medical lead of the complex care program, family physicians, social workers, and nurse practitioners, who are the most involved with transitioning patients clinically. This previously siloed knowledge was centralized and converted into a standardized clinical toolkit including an age-stratified checklist and caregiver handouts. Having determined key transition parameters, 51 CMC were identified between the ages of 14 and 17 (inclusive) and were included in the study. Demographic, medical, and transition-related data was collected from care plans and clinical notes. Data was subsequently stratified by age and care location. Descriptive statistics were generated for each key transition parameter. RESULTS This cohort of 51 CMC (51% male, 49% female) displayed steady growth between years. On average, these CMC had 9 diagnoses, 6 subspecialists, actively used 8 medications, and were dependent on one home-technology. Only 50% of patients had discussed transition-related topics and 76% of patients did not have a designated transition lead. Although discussion of transition increased with age, many crucial transition-related topics, both in medical and social categories, were not routinely addressed. From a medical perspective, over 40% of patients had not found a family physician, who serve as the primary medical care coordinators in the adult healthcare system. In the final year before transition, less than 50% of patients had been referred to new adult subspecialists and of these patients, half had not met these new providers. Adult social support programs, which provide critical funding for medications, respite, and home care, represent the other major component of transition. At age 17, 50% of patients had not applied for any provincial disability and respite funding, which can take up to 2 years to receive approval for. CONCLUSION This study has identified areas for improvement in medical and social aspects of transition-related planning that may benefit from a standardized clinical toolkit. These are highly complex patients who are not meeting transition-related milestones before turning 18, and are entering the adult healthcare system unprepared. Without approved funding and established medical follow-up, patients and caregivers suffer. Due to the highly heterogenous nature of this patient population, a standardized approach to transition, in tandem with further research and education, is needed.
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 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».