T245. A MODEL 2.0 FOR EARLY INTERVENTION SERVICES FOR PSYCHOSIS: USING A LEARNING HEALTHCARE SYSTEM APPROACH TO IMPROVE EVIDENCE-BASED CARE

BACKGROUND: In Canada, 26.3% of people reporting having mental disorders have indicated that they did not receive adequate care for their mental illness. However, early and evidence-based treatment can significantly reduce the severity of mental illnesses. Early Intervention Services (EIS) for psychosis are an example of such an intervention. EIS are widely recognized as a more effective treatment than routine care for early psychosis. Most Canadian EIS for psychosis follow recommendations on clinical components of care (i.e., easy and rapid access, a case management team approach); however, evidence-based interventions (e.g., measurement-based care or integrated psychosocial interventions) are not always available. Overall, various barriers limit the provision of quality care in the mental health sector, including EIS for psychosis treatment. These barriers include insufficient funding at a time of increasing demand; lack of services; lack of evidence- and measurement-based treatments; and insufficient training for staff and resources for patients. Innovative solutions are required. This presentation describes how e-mental health (eMH) technologies can mitigate these barriers, thus increasing access to evidence-based treatments. METHODS: Using a learning healthcare system approach, this 2.0 mental health services model aims to (a) identify, describe, and explain the factors affecting the routine incorporation and sustainability of eMH technologies in EIS for psychosis, and (b) optimize the methods associated with the development, adaptation, and evaluation of eMH technologies in real clinical settings. These aims are achieved by implementing three e-MH projects and unpacking the co-design/adaptation process and test the implementation, evaluation, and sustainability of eMH interventions and their effects on patient outcomes. RESULTS: The learning healthcare system is considered a new research paradigm able to promote quality, safety, and value in health care. Three project are at the core of this learning healthcare system for psychosis: (1) e-Mental Health Assessment and Monitoring (Project A: DIALOG+/e-Pathways to care): (a) To promote evidence- and measurement-based care in EIS for psychosis and (b) to use such technologies (such as electronic data capture platforms and data visualization) to support shared decision-making during treatment; (2) e-Treatment (Project B: CBT/pathways to care game-based interviews): (a) To facilitate the access and use of e-cognitive behavioral therapy (e-CBT) interventions in EIS for psychosis and (b) to support the treatment of secondary illnesses/comorbidities (depression and anxiety); (3) Web-based Training (Project C e-Training): (a) To co-produce web-based training and evaluate its effects on building capacity for the use of eMH technologies in EIS for psychosis and (b) to deliver psycho-educational interventions and continuing education training through interactive case-based learning. DISCUSSION: This work is timely. The innovative use of the rapid learning system approach in EIS for psychosis will offer a unique opportunity for integrating technologies and data into clinical practice, and should bring meaningful benefits to patients and promote Quebec’s open science research.