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Emerging Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and produce early results that are consistent with the objectives of the activities and thus indicate effectiveness.
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Peer Review Study
This article outlines a framework for data collection among communities of color based on a project by local organizations in Canada. The authors also discuss their encounter with data gaps and provide recommendations and strategies for policymakers and community/health stakeholders on how to improve, use, and analyze disaggregated data. As part of the Strengthening Disaggregated Sociodemographic Data Related to COVID-19 project with the Ottawa Local Immigration Partnership (OLIP) and the University of Ottawa, a framework for guiding equitable data collection and use was created.
Promising Practices that show evidence of effectiveness in improving public health outcomes in a specific real-life setting, as indicated by achievement of aims consistent with the objectives of the activities, and are suitable for adaptation by other communities.
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Systematic Review/Meta-Analysis
This article explores how health data technology tools such as Artificial Intelligence (AI) and Machine Learning (ML) tools can be implemented and adapted to assist in better responses and outcomes to the COVID-19 pandemic, as well as future epidemics. This literature review focuses on peer-reviewed articles concerning four themes: COVID-19 and the need for AI; utility of AI in COVID-19 screening, contract tracing, and diagnosis; use of AI in COVID-19 patient monitoring and drug development; AI beyond COVID-19 and opportunities for Low-Middle Income Communities (LMIC). This review contains examples of ways healthcare systems have implemented AI and ML to predict and treat outcomes of COVID-19, as well as potential capacities for AI.