Title

Developing and testing a chatbot to integrate HIV education into family planning clinic waiting areas in Lusaka, Zambia

Document Type

Article (peer-reviewed)

Publication Date

9-26-2022

Abstract

Background: To maximize protection against both unintended pregnancy and HIV, it is important that family planning (FP) services integrate HIV counseling, both to support method choice and identify potential HIV services of interest, such as pre-exposure prophylaxis (PrEP). However, FP providers often lack sufficient time and knowledge to address HIV vulnerability with clients. To potentially offload some of the initial HIV counseling burden from FP providers, we developed and tested a chatbot that provided information about HIV and dual protection to FP clients in waiting areas of FP clinics in Lusaka, Zambia. Chatbot Development: We drafted a scripted conversation and tested it in English in formative workshops with Zambian women between the ages of 15 and 49 years. After translating the content to Bemba and Nyanja, we conducted a second round of workshops to validate the translations, before uploading the content into the chatbot platform. Chatbot User Test: Thirty volunteers tested the chatbot in 3 Lusaka FP clinics, completing an exit survey to provide feedback. A large majority (83%) said they learned new HIV information from the chatbot. Twenty (67%) learned about PrEP for the first time through the chat. Most (96%) reported discussing HIV with the provider, after engaging with the chatbot. In response to an open-ended question, several testers volunteered that they wanted to learn more about PrEP. Conclusions: Pre-consultation waiting-area time is an underutilized opportunity to impart HIV information to FP clients, thereby preparing them to discuss their dual HIV and pregnancy prevention needs when they see their providers. FP clients expressed particular interest in learning more about PrEP, underscoring the importance of integrating HIV into FP services.

DOI

10.9745/GHSP-D-21-00721

Language

English

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