Description:
Phase I SBIR proposals will be accepted.
Fast-track proposals will not be accepted.
Phase I clinical trials will not be accepted.
Number of anticipated awards: 1
Budget (total costs): Phase I up to $243,500 for up to 6 months; Phase II of up to $1,972,828 and a Phase II duration of up to 2 years
PROPOSALS THAT EXCEED THE BUDGET OR PROJECT DURATION LISTED ABOVE MAY NOT BE FUNDED.
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Background
Electronic health record (EHR) technologies are increasingly promoted as innovative platforms to streamline preventive health programs
and improve compliance with clinical guidelines. EHR alerts have been created to streamline hepatitis C virus (HCV) and HIV screening
processes in primary care settings and to develop predictive models that identify patients at a high risk of HIV acquisition who may benefit
from pre-exposure prophylaxis (PrEP). There is a lack of such functionality to identify patients with HIV not in care-to our knowledge; few
medical centers have any “homegrown” electronic medical record algorithms in place to identify persons lost to HIV care. This SBIR
project seeks to utilize EHR data that are typically available in EHR systems to develop a “core” algorithm that can be used in multiple
healthcare systems to identify patients newly and previously diagnosed with HIV and categorize their linkage to care, antiretroviral (ART)
prescriptions, retention in care, and viral suppression status. Interoperability of different EHR systems with regards to this functionality will
also be explored to improve generalizability and functionality throughout the country.
Persons living with HIV may not be engaged in HIV care but may continue to access the health care system in other settings such as other
primary care or specialty clinics, emergency rooms, urgent care, and inpatient admissions. Such access can provide opportunities to reengage them to HIV care. The data derived from the algorithm could be displayed on an EHR dashboard which would be accessible in any
clinical setting affiliated with a healthcare system. Healthcare providers could utilize the information displayed to immediately identify a
patient as not-in-care, and initiate care coordination and re-engagement efforts. Alternatively, a health care system could query its EHR data
at regular intervals to identify patients who may have fallen out of care.
