Article type
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Abstract
Background: Accessing evidence-based care is a fundamental principle of the Canadian universal healthcare system. Unfortunately, 26% of Canadians who experience mental illnesses have reported that they have unmet needs in mental health care. Early intervention services (EIS) for psychosis are widely recognized as being more effective for treating early psychosis than routine care. EIS attempt to decrease delays to treatment access, actively engage service users and families, offer appropriate patient-staff ratios, and integrate evidence-based psychosocial interventions. Although many provinces have implemented EIS, studies have revealed heterogeneity in the implementation of the essential components of EIS identified by international experts and guidelines.
Objectives: The Rapid Learning Health System (RLHS) for Quebec EIS project aims to address this gap by systematically collecting real-time data in eleven EIS programs in Quebec using an electronic data capturing platform and by building capacity to use this data for shaping clinical practices through targeted continuous education. Specifically, to (1) Determine the feasibility of implementing a RLHS aimed at improving the quality of care of EIS, and (2) Assess the impact of the RLHS on service compliance with standards of care across eleven Quebec EIS.
Methods: The RE-AIM model informs and guides data collection on the implementation process and impact of this RLHS. RedCap, a user-friendly electronic data capturing, repository and reporting system, will capture selected indicators of service quality. Simultaneously, Dialog+, an e-intervention, will provide feedback to and from service users and providers, to promote quality of life and care and to support shared decision making and measurement-based care.
Results: We will present learnings and preliminary descriptive data following the five RLHS phases: Scan (Performed knowledge synthesis activities and need assessment in services), Design and Implement (Stakeholders engagement, open communication to keep stakeholders informed and tailored capacity-building activities aimed at improving quality of services), and Evaluate and Adjust (Description of the data gathered, reporting system and feedback to services). We will focus on knowledge synthesis activities that informed this project; strategies to engage patients, families, and front-line staff; and IT technologies and data.
Conclusions: A RLHS can improve the uptake of clinical guidelines and evidence-based interventions in clinical settings and the translation of knowledge into practice. The RLHS approach has been shown to promote innovation in healthcare. This project will contribute to new evidence of its impact in the mental healthcare context. It will assess how real-time data and a learning community of EIS across Quebec can share best practices to improve clinical and yield province-wide outcomes.
Patient involvement: Patients, families, clinicians, and decision-makers have been involved to enhance the relevance, uptake, and sustainability of this project.
Objectives: The Rapid Learning Health System (RLHS) for Quebec EIS project aims to address this gap by systematically collecting real-time data in eleven EIS programs in Quebec using an electronic data capturing platform and by building capacity to use this data for shaping clinical practices through targeted continuous education. Specifically, to (1) Determine the feasibility of implementing a RLHS aimed at improving the quality of care of EIS, and (2) Assess the impact of the RLHS on service compliance with standards of care across eleven Quebec EIS.
Methods: The RE-AIM model informs and guides data collection on the implementation process and impact of this RLHS. RedCap, a user-friendly electronic data capturing, repository and reporting system, will capture selected indicators of service quality. Simultaneously, Dialog+, an e-intervention, will provide feedback to and from service users and providers, to promote quality of life and care and to support shared decision making and measurement-based care.
Results: We will present learnings and preliminary descriptive data following the five RLHS phases: Scan (Performed knowledge synthesis activities and need assessment in services), Design and Implement (Stakeholders engagement, open communication to keep stakeholders informed and tailored capacity-building activities aimed at improving quality of services), and Evaluate and Adjust (Description of the data gathered, reporting system and feedback to services). We will focus on knowledge synthesis activities that informed this project; strategies to engage patients, families, and front-line staff; and IT technologies and data.
Conclusions: A RLHS can improve the uptake of clinical guidelines and evidence-based interventions in clinical settings and the translation of knowledge into practice. The RLHS approach has been shown to promote innovation in healthcare. This project will contribute to new evidence of its impact in the mental healthcare context. It will assess how real-time data and a learning community of EIS across Quebec can share best practices to improve clinical and yield province-wide outcomes.
Patient involvement: Patients, families, clinicians, and decision-makers have been involved to enhance the relevance, uptake, and sustainability of this project.