SCCM Resource Library-Discovery, the Critical Care Research Network: Datathon Winner Presentations

Video Transcription

Video Summary

Renata, a PhD student from Columbia University, discussed her team's exploration at a Datathon, focusing on racial disparities in COVID-19 therapeutic outcomes. They specifically investigated the impact of proning on COVID-19 patients across different ethnicities using a global registry. The study revealed that globally, Black patients were less frequently prone compared to others, though no significant differences were noted within the U.S. Female patients were generally prone less, and the study concluded no significant effect of proning on mortality across ethnicities. Despite acknowledging their research as a preliminary study limited by data constraints, they emphasized the need for more research on proning documentation.<br /><br />Subsequently, Tiago, representing another team, examined the impact of limited English proficiency (LEP) on critically ill patients. Their analysis suggested LEP patients had no significant difference in mortality timing. They highlighted older and Medicaid patients as underrepresented, noting frequent and late end-of-life conversations among LEP female patients. The team called for more substantial data samples and advanced analysis.<br /><br />Finally, Nikolai Anderson from team sugar data presented research on disparities in glucose measurements from point-of-care versus serum tests, revealing potential biases linked to digital determinants of health, emphasizing the need for device and protocol examination in specific hospitals. Each presented research underscores the importance of examining healthcare disparities using extensive data analysis to foster equitable healthcare solutions.

Asset Caption

45-Minute Session | Discovery, the Critical Care Research Network: Datathon Winner Presentations

Meta Tag

Content Type Presentation

Membership Level Professional

Membership Level Select

Year 2024

Keywords

racial disparities

COVID-19 outcomes

proning effect

limited English proficiency

healthcare disparities

glucose measurement bias

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