The following is a guest article by Jacqueline Naeem, MD, Vice President of Clinical and Social Health at PCCI

During the last decade, we have seen major breakthroughs in preventing HIV infection. However, even with these advances, the infection rate has not appreciably dropped. Pre-exposure prophylaxis (PrEP) has emerged as a highly effective preventive strategy for HIV, reducing the risk of HIV infection by up to 99% when taken consistently. Due to its effectiveness, the CDC recommends that medical providers counsel and prescribe PrEP to all sexually active patients if they are at risk for HIV infection.  However, despite its efficacy, PrEP remains underutilized, in large part due to a lack of awareness. This is where artificial intelligence (AI) has stepped in to significantly advance our HIV prevention efforts.

Despite improvements in morbidity and mortality associated with HIV due to antiretroviral therapy and the availability of an effective preventative medication, the incidence of HIV has only modestly decreased, with a 9% decrease between 2015 and 2019 and a total of 36,136 cases in 2021. In Dallas County, we find a situation that is almost at crisis levels with the spread of sexually transmitted infections (STIs), including HIV. For example, Dallas County ranks 2nd highest in HIV, 6th in Syphilis, 21st in Gonorrhea, and 26th in Chlamydia infection rates compared to the other 254 Texas counties. With its position as North Texas’s largest safety-net hospital system, Parkland Health (Parkland) serves an extensive population of at-risk patients, creating a vital opportunity to enhance HIV testing and facilitate connections to PrEP programs.

Although we knew the mission was clear, the challenge was also great. We have an effective preventive treatment— PrEP, and opportunities to reach PrEP candidates—through Parkland, but what we were lacking was a way to identify candidates for referral in a simple way that could be incorporated into Parkland’s workflows and be paired with provider tools to guide discussion and assessment of indications and eligibility criteria for PrEP. To address this critical gap, we developed and implemented a predictive model, PCCI’s HIV Detection AI/ML Model, informed by EHR data and paired with provider tools to guide discussions on PrEP eligibility criteria, to efficiently identify (and target for outreach) individuals who stand to benefit most from PrEP.

PCCI’s HIV Detection AI/ML Model project work began in the latter part of 2020. Once underway, we then worked with Parkland’s IT to integrate the developed model for provider alert-based, risk-stratified interventions, in silent mode. We then automated PrEP Model load to the Parkland test table for piloting and testing the workflow.  We also identified the patient population cohort eligible for HIV risk scoring.  In late 2022, the model went live, using information from the EHR to predict the individuals at increased likelihood of acquiring HIV and who may be candidates for HIV PrEP. Once identified, the patients can be offered HIV testing, and if negative, can be offered PrEP. So far, the HIV Detection AI/ML Model has risk stratified hundreds of thousands of patients, demonstrating that machine learning models can be used for predicting and classifying the risk of HIV using available EHR data.

We see this as a breakthrough for identifying candidates who are at risk for HIV infection. PCCI’s HIV Detection AI/ML Model has been shown to effectively address the needs of vulnerable populations and can be implemented in hospital settings with limited resources. There are opportunities to expand this model to reach even more patients in Dallas County, through an additional project underway with Dallas County Health and Human Services.

We revealed the methods and results of PCCI’s HIV Detection AI/ML Model in three peer-reviewed papers released in the past year:

• AJPM Focus: Supporting Access to HIV Pre-Exposure Prophylaxis in a Shifting Financial and Insurance Landscape

• Journal of Acquired Immune Deficiency Syndrome (JAIDS): Using Machine Learning to Identify Patients at Risk of Acquiring HIV in an Urban Health System

• Applied Clinical Informatics: Association of an HIV-Prediction Model with Uptake of Preexposure Prophylaxis

Leveraging predictive models within Parkland and Dallas County allows providers to identify individuals at high risk for HIV acquisition and those who are prime candidates for PrEP. By doing so, we can implement proactive interventions that can bridge critical gaps in the HIV prevention cascade, thereby contributing to the broader goal of reducing HIV incidence in Dallas County.

About Jacqueline Naeem

Jacqueline Naeem, MD, is Vice President of Clinical and Social Health at PCCI. She is a graduate of the University of Manchester Medical School, Manchester, England, where she also obtained her post-graduate diploma in Psychiatry at the University of Manchester. She undertook postgraduate training in both psychiatry and general practice also in the UK, as well as working as a medical school examiner. Since joining PCCI, Dr. Naeem has used her clinical experience and unique insights in several projects, particularly those with an emphasis on Non-Medical Drivers of Health and also mental behavioral projects. Dr. Naeem was also the program leader for the U.S. Centers for Medicare & Medicaid Services (CMS) Accountable Health Communities (AHC) Model in Dallas County.