Ferrari, M., Iyer, S., LeBlanc, A., Roy, M.-A. et Abdel-Baki, A. (2022). A Rapid-Learning Health System to Support Implementation of Early Intervention Services for Psychosis in Quebec, Canada: Protocol. JMIR Research Protocols, 11(7), e37346.
Background: Given the strong evidence of their effectiveness, early intervention services (EIS) for psychosis are being widely implemented. However, heterogeneity in the implementation of essential components remains an ongoing challenge. Rapid-learning health systems (RLHSs) that embed data collection in clinical settings for real-time learning and continuous quality improvement can address this challenge. Therefore, we implemented an RLHS in 11 EIS in Quebec, Canada.
Objective: This project aims to determine the feasibility and acceptability of implementing an RLHS in EIS and assess its impact on compliance with standards for essential EIS components.
Methods: Funding for this project was secured in July 2019, and ethics approval was received in December 2019. The implementation of this RLHS involves 6 iterative phases: external and internal scan, design, implementation, evaluation, adjustment, and dissemination. Multiple stakeholder groups (service users, families, clinicians, researchers, decision makers, and provincial EIS associations) are involved in all phases. Meaningful EIS quality indicators (eg, satisfaction and timeliness of response to referrals) were selected based on a literature review, provincial guidelines, and stakeholder consensus on prioritization of indicators. A digital infrastructure was designed and deployed comprising a user-friendly interface for routinely collecting data from programs; a digital terminal and mobile app to collect feedback from service users and families regarding care received, health, and quality of life; and data analytic, visualization, and reporting functionalities to provide participating programs with real-time feedback on their ongoing performance in relation to standards and to other programs, including tailored recommendations. Our community of practice conducts activities, leveraging insights from data to build program capacity while continuously aligning their practices with standards and best practices. Guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework, we are collecting quantitative and qualitative data on the reach, effectiveness, adoption, implementation, and maintenance of our RLHS for evaluating its impacts.
Results: Phase 1 (identifying RLHS indicators for EIS based on a literature synthesis, a survey, and consensus meetings with all stakeholder groups) and phase 2 (developing and implementing the RLHS digital infrastructure) are completed (September 2019 to May 2020). Phases 3 to 5 have been ongoing (June 2020 to June 2022). Continuous data collection through the RLHS data capture platforms and real-time feedback to all stakeholders are deployed. Phase 6 will be implemented in 2022 to assess the impact of the RLHS using the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework with quantitative and qualitative data.
Conclusions: This project will yield valuable insights into the implementation of RLHS in EIS, offering preliminary evidence of its acceptability, feasibility, and impacts on program-level outcomes. The findings will refine our RLHS further and advance approaches that use data, stakeholder voices, and collaborative learning to improve outcomes and quality in services for psychosis.