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Novel Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and are in the process of generating evidence of effectiveness or may not yet be tested.
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Toolkit
The toolkit describes positive and problematic practices for centering racial equity across the six stages of the data life cycle: (1) data collection, (2) data access, (3) use of algorithms and statistical tools, (4) data analysis, and (5) reporting and dissemination.
Novel Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and are in the process of generating evidence of effectiveness or may not yet be tested.
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Toolkit
The Community Information Exchange (CIE) Data Equity Framework’s goal is to build data systems to help institutions, and the communities they serve, approach CIE® planning and systems change work from a place of anti-racism by: (1) naming how data system design reflects understanding of and participation by the intended beneficiaries of current programs and interventions; (2) acknowledging and documenting the effects of a spectrum of data system design types on oppressed populations and communities; (3) identifying strategies needed to eliminate the harm of current processes and practices; (4) highlighting the behavior change needed to rebuild or change the overall data system to better meet community needs across racial and ethnic populations; and (5) adopting practices that promote restorative justice and mitigate harm and exploitation.
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.