Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

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CMP was successful in reducing death for all patients. For complex patients with diabetes mellitus in the intervention group, death and hospital usage were lower. Per clinic, hypothesized savings from decreased hospitalizations was $17,384 to $70,349.

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BackgroundThe emergence of the Internet has increased access to health information and can facilitate active individual engagement in health care decision making. Hispanics are the fastest-growing minority group in the United States and are also the most underserved in terms of access to online health information. A growing body of literature has examined correlates of online health information seeking behaviors (HISBs), but few studies have included Hispanics.ObjectiveThe specific aim of this descriptive, correlational study was to examine factors associated with HISBs of Hispanics.MethodsThe study sample (N=4070) was recruited from five postal zip codes in northern Manhattan for the Washington Heights Inwood Informatics Infrastructure for Comparative Effectiveness Research project. Survey data were collected via interview by bilingual community health workers in a community center, households, and other community settings. Data were analyzed using bivariate analyses and logistic regression.ResultsAmong individual respondents, online HISBs were significantly associated with higher education (OR 3.03, 95% CI 2.15-4.29, P<.001), worse health status (OR 0.42, 95% CI 0.31-0.57, P<.001), and having no hypertension (OR 0.60, 95% CI 0.43-0.84, P=.003). Online HISBs of other household members were significantly associated with respondent factors: female gender (OR 1.60, 95% CI 1.22-2.10, P=.001), being younger (OR 0.75, 95% CI 0.62-0.90, P=.002), being married (OR 1.36, 95% CI 1.09-1.71, P=.007), having higher education (OR 1.80, 95% CI 1.404-2.316, P<.001), being in worse health (OR 0.59, 95% CI 0.46-0.77, P<.001), and having serious health problems increased the odds of their household members’ online HISBs (OR 1.83, 95% CI 1.29-2.60, P=.001).ConclusionsThis large-scale community survey identified factors associated with online HISBs among Hispanics that merit closer examination. To enhance online HISBs among Hispanics, health care providers and policy makers need to understand the cultural context of the Hispanic population. Results of this study can provide a foundation for the development of informatics-based interventions to improve the health of Hispanics in the United States.