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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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Summary Report/Recommendations
This article shows efforts of six grant-funded regional partners to create a usable public health analytic system to address health inequities among COVID-19 positive cases on an individual patient level. The article highlights the many challenges of this Multistate Data Strategy, including lack of standardization across data sources, missing data fields, and different state-level reporting requirements. However, the ability to produce this analytic system in real time, including a standardized COVID-19 data dictionary, demonstrates the necessity for healthcare administrators to utilize deidentified patient-level data in order to provide better care for state residents, particularly in disadvantaged communities.
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.