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    About Cerner RWD

    Powered by Real-World Data solutions, the Cerner RWD Publications page is a repository featuring clients’ academic research to help address health care issues.
    Learn more about Cerner RWD Publications.

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    Opioid use disorder
    and health service
    utilization among
    COVID-19 patients
    Opioid use disorder and health service utilization among COVID-19 patients in the US: A nationwide cohort from the Cerner Real-World Data
    Client: University of Utah
    Both opioid use and COVID-19 affect respiratory and pulmonary health, potentially putting individuals with opioid use disorders (OUD) at risk for complications from COVID-19....
    Cerner RWD Publications - Dark
    Powered by Real-World Data solutions, the Cerner RWD Publications page is a repository featuring clients’ academic research to help address health care issues.
    Learn more about Cerner RWD Publications.


    Subscribe to receive new articles every month.
    Predicting interactions
    of drugs in COVID-19
    clinical trials
    Drugs in COVID‐19 clinical trials: predicting transporter‐mediated drug‐drug interactions using in vitro assays and real‐world data
    Client: University of California San Francisco
    Racial and ethnic
    outcomes of COVID-19
    patients in emergency
    department
    Racial and ethnic differences and clinical outcomes of COVID-19 patients presenting to the emergency department
    Client: Emory
    Racial disparities
    in COVID-19 outcomes
    Racial disparities in COVID-19 outcomes exist despite comparable Elixhauser comorbidity indices between Blacks, Hispanics, Native Americans, and Whites
    Client: University of Utah
    Retrospective analysis of
    pregnant women with COVID-19
    The risk of clinical complications and death among pregnant women with COVID-19 in the Cerner COVID-19 cohort: a retrospective analysis
    Client: University of Utah
    Cerner Real-World Data
    Advance research discovery with national, de-identified, person-centric data.
    Statistical learning
    for predicting
    COVID-19
    severity among
    cardiovascular patients
    A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions
    Client: Children's Hospital of Orange County
    Prognostic values
    of serum
    ferritin and
    D-dimer trajectory
    in COVID-19
    patients
    Prognostic Values of Serum Ferritin and D-Dimer Trajectory in Patients with COVID-19
    Client: University of Utah
    Acute ischemic
    stroke and
    COVID-19
    Acute Ischemic Stroke and COVID-19 An Analysis of 27,676 Patients
    Client: University of Missouri
    Statins decrease
    mortality rate
    among COVID-19
    patients
    Decreased mortality rate among COVID-19 patients prescribed statins: data from electronic health records in the U.S.
    Client: University of California San Francisco
    Habits of
    emergency
    physicians for
    diagnosis of
    non-traumatic
    headache
    A description of prescribing habits of emergency physicians for a diagnosis of non-traumatic headache using Cerner Health Facts, a national EHR database (5130).
    Client: Truman Medical Centers
    Small sleepers,
    big data
    Small sleepers, big data: leveraging big data to explore sleep disordered breathing in infants and young children
    Client: Children's Mercy Hospital
    HealtheDataLab:
    predicting multicenter
    pediatric readmissions
    HealtheDataLab – a cloud computing solution for data science and advanced analytics in health care with application to predicting multi-center pediatric readmissions.
    Client: Children’s Hospital of Orange County
    Relationships
    between COVID-19
    and cardiovascular
    health outcomes
    Facilitating the study of relationships between COVID-19 and cardiovascular health outcomes using Cerner Real-World COVID-19 de-identified data set.
    Client: University of Missouri
    Sharp and
    smooth
    synthetic
    EHR data
    generation
    SMOOTH-GAN: towards sharp and smooth synthetic EHR data generation.
    Client: Stony Brook University
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