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.
Gender
Race
Ethnicity
Age Group
What is the available data at glance?
| Domain | Example Variables | Description |
|---|---|---|
|
Cohort |
Sex, Age, Race, 3-digits Zip Code, Ethnicity, Deceased |
Demographic characteristics from EHR (Age, Sex, Ethnicity, Race, etc.) |
|
Diagnosis |
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 |
Encounter ID, Registration or Admission time, Discharge Time, Reason for Visit, Location of Care, Insurance |
Healthcare utilization (e.g., ER, inpatient, and outpatient visits) |
|
Medication |
Order Mnemonic, Order Details, Order Status, Original Order Time |
Lists of all medications with the order details. |
|
Problems |
Concept, Time, Status |
Problem lists Concepts contain clinical notes. |
|
BMI |
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.
