HIV Infections, Preexposure Prophylaxis (PrEP)
Conditions
Keywords
HIV, preexposure prophylaxis (PrEP), risk prediction, clinical decision support, electronic health records
Brief summary
Scale-up of HIV preexposure prophylaxis (PrEP) is a key strategy of the federal initiative to end the HIV epidemic. However, healthcare providers lack tools to identify patients who are at increased risk for HIV infection and thus likely to benefit from PrEP. This pilot study will test the hypothesis that an electronic health record (EHR)-based clinical decision support system that incorporates an HIV risk prediction model can help providers identify patients at increased risk for HIV infection and improve PrEP prescribing in safety-net community health centers. The clinical decision support system will be implemented in the EHR at 2-3intervention clinics, while 2 control clinics will receive standard of care. The primary outcome is PrEP prescriptions. Other key metrics of PrEP-related care to be assessed include medication persistence, adherence to monitoring guidelines for PrEP, and rates of HIV/STI testing and diagnoses. The expected outcome is the foundation for a large-scale cluster randomized trial to test whether EHR-based clinical decision support tools for PrEP can improve PrEP prescribing and prevent new HIV infections in a national network of community health centers.
Interventions
OTHERStandard of care
Two control clinics will be selected based on a set of matching criteria, e.g., urbanity and the sex, race, and age distributions of the patient population. Control clinics will not participate in study activities.
OTHERClinical decision support for PrEP
Healthcare providers at 2 intervention clinics (total of approximately 60 providers anticipated) will be prompted by an EHR-based tool to discuss PrEP with patients whose demographics and clinical history indicate increased predicted HIV risk. Providers will be offered clinical decision support tools to guide sexual health discussions and support PrEP prescribing.
Sponsors
OCHIN, Inc.
Oregon Health and Science University
National Institute of Mental Health (NIMH)
Beth Israel Deaconess Medical Center
Harvard Pilgrim Health Care
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
NONE
Intervention model description
The intervention will be implemented in 2-3 clinics, with 2-3 control clinics receiving standard of care. Eligible community health centers will be those with at least 500 patients and a minimum of 10 HIV diagnoses. Clinics located in counties identified as HIV hotspots will be prioritized. Clinics specializing in HIV or sexual health care, where providers may have less need for PrEP decision support, will be excluded. Overall, 26 clinics are expected to meet these eligibility criteria, with a total of 831 incident HIV diagnoses in 2018. Of eligible clinics, 2-3 will be recruited to receive the intervention. Using a covariate-constrained matching procedure with EHR data on important covariates (e.g., urbanity and the sex, race, and age distributions of the patient population), 2-3matched control clinics will be selected.
Eligibility
Sex/Gender
ALL
Age
15 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Primary care providers at participating clinics who are licensed to prescribe PrEP
Exclusion criteria
* N/A
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| PrEP prescriptions | 9 months | Number of patients with increased predicted HIV risk who are prescribed PrEP |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of clinical decision support system | 6 months | Rates at which providers view alerts about patients with increased predicted HIV risk and rates at which providers access the EHR-based decision support tools using Epic date-time stamps |
| Acceptability of clinical decision support system | 6 months | One-on-one feedback interviews with clinic administrators and 3-5 providers at intervention clinics |
Countries
United States
Outcome results
None listed