AIM-AHEAD Data Bridge from MedStar Health

Cardiometabolic, Cancer, and Behavioral Health: Onset of Disease During COVID-19

Utilizing the MedStar Cerner MedConnect Electronic Health Record and the MedStar Analytics Platform to to use AI/ML to address disparities and minority health in behavioral health. The datasets estimate the extent of the impact of COVID-19 on the onset and management of chronic diseases, with an emphasis on health disparities and equity.  Approximately 1 Million records extracted from the EHR are highly potential to examine patterns of disease onset and health disparities as a result of COVID-19.

How Big is this Dataset?

Who are the Patients?

Breakdown of patients demographic, emphasis on health disparities and equity.








Age Group

Age Group


What is the available data at glance?

Domain Example Variables Description


Sex, Age, Race, 3-digits Zip Code, Ethnicity, Deceased

Demographic characteristics from EHR (Age, Sex, Ethnicity, Race, etc.)


Diagnosis Type, Diagnosis name, Diagnosis Priority, Diagnosis Time, ICD Code

Disease onset from diagnosis and condition list based on ICD codes (heart and kidney failure, diabetes, cancer, depression, and anxiety).


Encounter ID, Registration or Admission time, Discharge Time, Reason for Visit, Location of Care, Insurance

Healthcare utilization (e.g., ER, inpatient, and outpatient visits)


Order Mnemonic, Order Details, Order Status, Original Order Time

Lists of all medications with the order details. 


Concept, Time, Status

Problem lists Concepts contain clinical notes. 


Event name, Event time, Result Value

Clinical controls (e.g., BMI, smoking status, height, and weight)

Covid Diagnosis

Encounter ID, ICD Code, Person ID, Diagnosis Time

COVID19 diagnosis from diagnosis and condition list

Where are the patients from?

This dataset was extracted from Medstar Electronic health records. To explore the geographic coverage of our patients, please see the map below.


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