Comagine Health

Surveillance of Burden on Patients POST-COVID-19 - Project Abstract Summary Comagine Health, a 501(C)(3) Nonprofit Organization, Is Thrilled to Submit This Application to the Component a Scope of Work. We Have Worked With Federal and State Clients to Implement Analytics Solutions for the Betterment of Healthcare for Over 40 Years. as One of the Component a Awardees, We Will Work to Advance Interoperability and Surveillance by Partnering With the Other Component a and B Awardees and Leveraging Existing Health Information Exchange (HIE) Data. Hie Data Include the Diagnoses, Procedures, Clinical Notes, and Lab Values Recorded in Electronic Medical Records, Aggregated, and Processed for Surveillance, Analysis, and Reporting. Our Team Is Led by Experienced Investigators From Boise State University’s (BSU) Public Health and Population Science Department With Deep Expertise in Surveillance and Developing and Running Cohort Studies. Beyondhie Is Our Connection to the Discrete Hie Organizations That Will Lead the Data Collection and Aggregation for Our Approach: the Utah Health Information Network (UHIN) and Bronx Regional Health Information Organization (bronxrhio). We Have the Possibility of Adding One to Two Additional States (through Other Hie Partners) Prior to the End of the Study.this Partnership Arrangement Leverages Existing Partnerships With Public Health and Community Organizations in Utah and the Bronx and Improves Upon Data Exchange Processes Between Two Established Hies. Our Approach Will Allow Us to Measure the Burden, Distribution, and Impact of POST-COVID-19 Conditions (PCC) in a Reliable, Scalable, and Population-based Manner. We Will Work in Two Distinct Geographic Areas: Cohort 1 Will Be Derived From a State-wide Hie With Data From Utah Residents; Cohort 2 Will Be Derived From a Borough-wide Hie With Data From Residents of the Bronx, New York City, New York. Cohort Individuals From Both Catchment Areas Include Children, Adolescents, and Adults, Reflect Urban and Rural Areas, Socio-economically Diverse Areas, and Individuals Disproportionately Impacted by COVID-19. We Will Identify Individuals Who Have COVID-19 Infections and Characterize the Symptoms and Severity of Infection Within the Two Cohorts Using Biophysical Measurements, Lab Results, Diagnoses, Treatments, Medications, and Hospitalization Data, as Well as the Date of All Observations. These Data Elements Will Further Be Used to Characterize Pre-existing Conditions, as Well as PCC. to Identify Subtleties of Illness Burden and Track Progression Over Time We Will Analyze Healthcare Provider Visit Notes Using Natural Language Processing (NLP). Analysis of These Data Will Describe and Quantify the Intensity and Duration of Fluctuating Potential PCC Symptoms. We Will Also Analyze Socio-economic Data Associated With Affected Individuals’ Census Block to Depict the Relationship of Social Determinants of Health to the Frequency and Severity of Possible PCC Conditions. Results of Our Approach Will Be Shared Regularly With Public Health and Community Partners, Cohort a and B Collaborators, the Funding Agency, and Other Partners Who Can Use These Surveillance Reports to Inform COVID-19 Prevention and Mitigation Programs.