Peer-reviewed article
Systematic narrative review of pediatric-to-adult care transition models for youth with pediatric-onset chronic conditions
Husayn Marani, Jamie Keiko Fujioka, Sahar Tabatabavakili, Natasha P. Bollegala ·
Summary
Written by AI, not yet AICS's view ·Key points
Limitations
Abstract
Pediatric-to-adult transition in chronic disease care is often poorly managed. While there is growing interest in this field, existing reviews of transition models, which are critical in informing the development of best practices and evaluation metrics in transitional care, do not explore the critical design features of models that best improve health outcomes. This systematic review compares existing such models and the design features that positively impact clinical outcomes. Five databases were searched (Medline, CINAHL, EMBASE, PubMed, CENTRAL) until 2018. English-language evaluations (with defined clinical outcomes) of existing models for patients aged 13–28 with a pediatric-onset chronic disease were pulled. Cochrane tools were used for data extraction and risk-of-bias assessment. Twenty-nine studies showed improvements in outcomes for 10 pediatric-onset chronic diseases. A narrative synthesis of findings revealed two broad model types: transition clinics involving single or multiple appointments and/or multidisciplinary team, and transition program facilitators. Both model types included examples of remote (online or telephone) care. Most studies focused on a standardized multidisciplinary transition process that emphasized care coordination. All types resulted in improvements in access to medical care, reduced acute and chronic complications, and increased rates of follow-up. Commonly used strategies in successful programs were patient/family education, transition clinics jointly staffed by pediatric/adult physicians, or clinics dedicated to young adults within adult services. Despite the importance of effective transitional care, there are no commonly agreed upon quality metrics in transition care. Standardized evaluation metrics to compare models across disease types are needed.