The Data Sources


TriNetX is a commercial company that in conjunction with Stony Brook Medicine supports queries for cohort discovery and basic questions around COVID-19. This platform supports an elegant interactive interface that  allows anyone with a Stony Brook Medicine account to interrogate the COVID-19 patient data in a way that requires no IRB approval to view, as the data is de-identified. The data can be downloaded in a de-identified form as well, which requires acknowledgement by the IRB, and the identified data can be obtained with IRB approval. The TriNetX data is very good and closely approximates the information in the Data Commons; however, it is not as carefully curated and does not contain all the same elements. While very useful, TriNetX data does not supplant the need for the Data Commons.
Learn more about TriNetX, including training information, access and FAQs.

Data Commons

COVID-19 ImageTo aid researchers in a quick and reliable interrogation of the COVID-19 patient cohort, the Data Commons contains a very well curated edited set of more than 1,700 data elements selected for clinical relevance in this population. The available information includes pertinent hand-abstracted information as well as validated electronic health  data (EHR) and covers a range of potential clinical topics -- for example such things as demographics including ethnicity, clinical presentation , co-morbidities, anticoagulation status, acute kidney injury, mechanical ventilation to name a few of the almost 2000 available variables.  This resource is available  to Stony Brook faculty and staff, after gaining DAC approval, for quality initiatives as well as for research. This data set is growing as the team in Biomedical Informatics works with research groups to identify and vet additional data elements.  For example the information is currently being augmented with culture data integrated from SunQuest, the Pathology information system, currently not integrated with Cerner Millenium, our Stony Brook EHR.

To accelerate the pace of research associated with the Stony Brook Medicine COVID-19 Data Commons, an internal, secure website ( at quickly exploring the data has been developed. Approved researchers can easily select cohorts by filtering from more than 2,000 criteria, or bring their own cohort by cross referencing a list of medical record numbers. Researchers can rapidly analyze relationships between up to four variables at a time with built-in graphing tools, or select a broader complement of information for more sophisticated analysis offline. The Stony Brook Medicine COVID-19 dashboard tools will also soon be deployed in the National COVID Cohort Collaborative (N3C).