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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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Systematic Review/Meta-Analysis
This article details an exploratory roadmap to address how artificial intelligence can assist in linking clinical and community data to enhance population health and the reduction of health disparities. The article uses a literature review to create the roadmap.
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.
RELEASE DATE:
Systematic Review/Meta-Analysis
This literature review examines technology companies that have created initiatives to address digital health literacy and the impact of those initiatives. The review found 38 companies with relevant initiatives, but limited data on their effectiveness.
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.
RELEASE DATE:
Systematic Review/Meta-Analysis
This review looks at how artificial intelligence and big data can help manage the pandemic, including monitoring, forecasting, and predicting future outbreaks and resource utilization.
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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Commentary
This article provides an overview of the link between racism and COVID-19 disparities, missing race and ethnicity data, and literature on demographic data gaps. The authors also provide recommendations on how health departments and healthcare systems can engage communities of color to co-develop race and ethnicity data collection processes.
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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Commentary
This article outlines a framework for partnering with Indigenous nations in research and data collection and calls for the need for equitable data use agreements. The framework guidelines include (1) incorporating respect and collaboration early in negotiations; (2) recognition that specificity of terms is key to trust-building; (3) remembering that good data stewardship entails safeguarding; and (4) building sustainable relationships.
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.