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Leverage the opportunities afforded by the rapidly evolving field of data science.
Build a foundation for critical care data science to enhance its role in clinical decision-making and research. Establish principles for data standardization, sharing, and developing common data elements, validated through a pilot study.
Create a Discovery Critical Care Datahub to strengthen current data automation collaborations and expand future collaborations.
Connect critical care clinicians with data scientists in a two-day event aimed at creating pragmatic, data-driven models applicable to the care of critically ill patients.
Assessment of implementation methods in sepsis: study protocol for a cluster-randomized hybrid type 2 trial
A Path to Real-World Evidence in Critical Care Using Open-Source Data Harmonization Tools
Coronavirus Disease 2019 in Pediatric Oncology Patients: A Matched-Cohort Analysis of the SCCM Discovery Viral Infection and Respiratory Illness Universal Study COVID-19 Registry
Perceived Hospital Stress, Severe Acute Respiratory Syndrome Coronavirus 2 Activity, and Care Process Temporal Variance During the COVID-19 Pandemic
Leveraging the SCCM Discovery VIRUS COVID-19 Registry to Evaluate Drug Repurposing Research Dataset
The Impact of Steroids on Outcomes of Children Admitted to the Hospital With Non-MISC COVID-19: An Analysis from the SCCM Discovery Network Virus Registry
Association of Early Steroid Administration With Outcomes of Children Hospitalized for COVID-19 Without Multisystem Inflammatory Syndrome in Children
Severe Acute Respiratory Infection—Preparedness: Protocol for a Multicenter Prospective Cohort Study of Viral Respiratory Infections
Pre-Hospital Aspirin Use and Patient Outcomes in COVID-19: Results From the International Viral Infection and Respiratory Illness Universal Study (VIRUS)
Gastrointestinal Manifestations in Hospitalized Children With Acute SARS-CoV-2 Infection and Multisystem Inflammatory Condition: An Analysis of the VIRUS COVID-19 Registry