JMIR Publications

ABSTRACT

Background:

Sexual and gender minority (SGM) adolescents and young adults (AYAs) are at increased risk of HIV infection, particularly in the Southern United States. Despite availability of effective biomedical prevention strategies, such as pre-exposure prophylaxis (PrEP), access and uptake remains low among SGM AYAs. In response, the Louisiana Department of Health initiated the LA TelePrEP Program, which leverages the power of telemedicine to connect Louisiana residents to PrEP. A virtual TelePrEP Navigator guides users through the enrollment process, answers questions, schedules appointments and facilitates lab testing and medication delivery. In order to increase participation of SGM AYAs in the Program, the TelePrEP program partnered with researchers to develop a chatbot that would facilitate access to the Program and support Navigator functions. Chatbots are capable of carrying out many functions that reduce employee workload and, despite their successful use in healthcare and public health, they are relatively new to HIV prevention.

Objective:

In this paper, we describe the iterative and community-engaged process we used to develop an SMS-based chatbot tailored to SGM AYAs that would support Navigator functions and disseminate PrEP related information.

Methods:

Our process was comprised of two phases: conceptualization and development. In the conceptualization phase, we identified aspects of Navigator responsibilities, program logistics and user interactions to prioritize in chatbot programming (e.g., scheduling appointments, answering questions). We also selected a commercially-available chatbot platform that could execute these functions and could be programmed with minimal coding experience. In the development phase, we intentionally engaged Department of Health staff and SGM AYAs within our professional and personal networks. We conducted five different rounds of testing with various groups to test each iteration of the chatbot. After each iteration of the testing process, the research team met to discuss feedback, guide the programmer on incorporating modifications, and re-tested the chatbot.

Results:

Through our highly collaborative and community-engaged process, we successfully created a rule-based chatbot with artificial intelligence components. We gained important knowledge that could advance future chatbot development efforts for HIV prevention. Key to the PrEPBot’s success was resolving issues that hampered the user experience, like asking unnecessary questions, responding too quickly, and misunderstanding user input.

Conclusions:

HIV prevention researchers can feasibly and efficiently program a rule-based chatbot with the assistance of commercially-available tools. Our iterative process of engaging researchers, program personnel, and different subgroups of SGM AYAs to obtain input were key to successful chatbot development. If the results of our pilot trial show that the chatbot is feasible and acceptable to SMA AYAs, future HIV researchers and practitioners could consider incorporating chatbots as part of their programs.