The manifestations and associated outcomes of patients with Coronavirus disease 2019 (COVID-19) are not well studied in large studies. To understand how the COVID-19 affects, and is affected by, cardiovascular conditions, larger and more diverse patient samples are necessary. A better understanding of how the disease interacts with pre-existing risk factors and COVID-19 induced complications may improve the ability of clinicians to provide informed care.
A Cerner COVID-19 deidentified dataset based on electronic medical records in the Cerner Real-World Dataset is available to researchers. Data analytics pipelines are used to select study populations and identify risk factors, co-morbidities, and outcomes in patients with COVID-19 with various manifestations.
A total of 54 healthcare facilities across the United States for the April 2020 release.
Patients with a minimum of one emergency department (ED) visit, who were admitted for observation, or had an inpatient encounter with a diagnosis code that could be associated with COVID-19 exposure or infection, or those with a positive laboratory result for COVID-19.
Standard care as per institution.
Main data elements
The encounters include pharmacy, clinical and microbiology laboratory, admission, and billing information. The patient population can be identified using the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) primary or secondary diagnosis codes and procedures can be identified using Procedure Coding System(ICD-10-PCS) and Current Procedural Terminology (CPT) codes.
The Cerner COVID-19 deidentified dataset provides data from a large cohort of COVID-19 patients, which may increase the ability to address gaps in current knowledge regarding how the disease affects and is affected by cardiovascular conditions. Key words— Electronic health records; Coronavirus; COVID-19; diagnosis codes; procedural codes