Responding to the crisis in emotional-behavioral and mental health in children and youth by leveraging automation of a collaborative data platform (Preprint)

UNSTRUCTURED Emotional-behavioral and mental (EBMH) disorders in pediatrics have been on the rise prior to the pandemic, and have escalated in response to it. Deeply rooted systemic issues, such as fragmentation of care for EBMH and lack of provider capacity, inherent complexities of EBMH, such as it’s focus on more time-consuming biopsychosocial analyses, along with the lack of a scalable infrastructure to meet the explosion in demand have led to extreme delays in receiving care. In this case study, we explore a promising approach to help tackle this problem through the use of a data-centric and collaborative clinical framework. This involves three key elements: 1) the development of an end-to-end clinical pathway that describes the flow of narrative and discrete dimensional data from patients with EBMH concerns through the utilization of both existing validated measures and practical clinical measures; 2) the automated collection of this data from the appropriate individuals and sources; 3) the integration of this information into a collaborative health record in a manner that accomplishes automation in documentation and administrative activity within the health record, while optimally displaying this rich data set in a clinically effective manner at point-of-care. The development and utilization of this framework over the last 3 years in an iterative fashion has yielded both remarkable results in clinical efficiency while also producing a unique and comprehensive real-time data set that can be leveraged for intelligent clinical triage and fueling of clinical-decision support algorithm development. It is felt that this approach has the ability to represent a paradigm shift in quality improvement and access to care for patients in need of support with EBMH.