Background Mobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients who exhibit a high probable risk of contracting human immunodeficiency virus (HIV). A machine-guided tool is an algorithm that takes a set of subjective and objective answers from a simple questionnaire and computes an HIV risk assessment score. Objective The primary objective of this study is to establish that machine learning can be used to develop machine-guided tools and give us a deeper statistical understanding of the correlation between certain behavioral patterns and HIV. Methods In total, 200 HIV-negative adult individuals across three South African study sites each (two semirural and one urban) will be recruited. Study processes will include (1) completing a series of questions (demographic, sexual behavior and history, personal, lifestyle, and symptoms) on an application system, unaided (assistance will only be provided upon user request); (2) two HIV tests (one per study visit) being performed by a nurse/counselor according to South African national guidelines (to evaluate the prediction accuracy of the tool); and (3) communicating test results and completing a user experience survey questionnaire. The output metrics for this study will be computed by using the participants’ risk assessment scores as “predictions” and the test results as the “ground truth.” Analyses will be completed after visit 1 and then again after visit 2. All risk assessment scores will be used to calculate the reliability of the machine-guided tool. Results Ethical approval was received from the University of Witwatersrand Human Research Ethics Committee (HREC; ethics reference no. 200312) on August 20, 2020. This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. We will report on the machine-guided tool’s performance and usability, together with user satisfaction and recommendations for improvement. Conclusions Machine-guided risk assessment tools can provide a cost-effective alternative to large-scale HIV screening and help in providing targeted counseling and testing to prevent the spread of HIV. Trial Registration South African National Clinical Trial Registry DOH-27-042021-679; https://sanctr.samrc.ac.za/TrialDisplay.aspx?TrialID=5545 International Registered Report Identifier (IRRID) DERR1-10.2196/30304

BACKGROUND Mobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients that exhibit high probable risk of contracting human immunodeficiency virus (HIV). A machine guided tool is an algorithm that takes a set of subjective and objective answers of a simple questionnaire and computes an HIV risk assessment score. OBJECTIVE The primary objective of this study is to establish that machine learning can be used to develop machine guided tools and give us a deeper statistical understanding of the correlation between certain behavioral patterns and HIV. METHODS 200 HIV negative adult individuals each across three South African study sites (two semi-rural and one urban) will be recruited. Study processes will include i) completing a series of questions (demographic, sexual behavior and history, personal, lifestyle, and symptoms) on an application system, unaided (assistance will only be provided upon user request); ii) two HIV tests (one per study visit) being performed by a nurse/counsellor according to South African national guidelines (to evaluate prediction accuracy of the tool) and, iii) communication of test results and completion of a user experience survey questionnaire. The output metrics for this study will be computed by using the participants risk assessment score as “predictions” and the test results as the “ground truth.” Analyses will be completed after visit 1 and then again after visit 2. All the risk assessment scores will be used to calculate the reliability of the machine guided tools. RESULTS This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. Ethical approval was received from the University of Witwatersrand Human Research Ethics Committee (ethics reference number 200312) on 20 August 2020. CONCLUSIONS Machine guided risk assessment tools can provide a cost-effective alternative to large scale HIV screening and help in providing targeted counselling and testing to prevent the spread of HIV. CLINICALTRIAL The study was registered on the South African National Clinical Trial Registry (www.sanctr.gov.za) (DOH-27-042021-679). This study is funded by the Bill and Melinda Gates Foundation (OPP 1204282).