Methods: We sought to develop an online, interactive tool for ARV decision support that incorporates patient, viral, and therapeutic factors to provide evidence-based recommendations for individualized ARV regimen selection. We additionally incorporated educational materials within the tool to enhance provider learning while simultaneously improving real time clinical care. Our goal is to optimize ARV regimen selection to provide personalized HIV care that improves patient outcomes, minimizes adverse effects, and enriches clinician knowledge of antiretrovirals.
Results: We developed HIV-ASSIST (https://www.hivassist.com) as a freely available, online resource to support clinicians caring for HIV patients. Our tool utilizes decision-analysis principles based on current guidelines, scientific literature, and expert opinions to deliver tailored recommendations on ARV regimen selection specific to each patient encounter. Decision algorithms were optimized in an iterative fashion using feedback on complex case scenarios from regional HIV experts. HIV-ASSIST evaluates and ‘ranks’ all possible multi-drug ARV regimens across a variety of domains, including common laboratory markers (viral load, genotype, etc.) and patient-specific characteristics (medical comorbidities and drug interactions, treatment history, adherence, etc.). Through a user-friendly interface, clinicians are shown the impact of these modifying factors on ARV regimens and dosing, along with supporting clinical trial evidence.
Conclusion: HIV-ASSIST is a patient-centric tool to improve patient outcomes through real time ARV decision support and enhance knowledge of evidence-based HIV care guidelines.
M. Shah, None