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Abstract

Background: In Rwanda, a two-way-SMS-based mHealth intervention, WelTel, was deployed to support isolated COVID-19 patients throughout the pandemic. Patients received daily open-ended check-in-messages throughout their isolation period. To inform further health system digitalization, we sought to quantify WelTel enrollment, assess patients’ usage patterns, and explore how patient characteristics influence such behaviours. We further sought to investigate patient isolation experiences by AI-enhanced-analysis (Natural Language Processing (NLP)) of patient-clinician conversations, to improve similar programs. Methods: WelTel registration and messaging records were extracted, supplemented with Rwanda Ministry of Health data, and quantified. Patient use (≥1 conversation) was computed and compared across sociodemographic groups (sex, age, province, COVID-19-status, pandemic-wave) using logistic regression. Conversation counts and characteristics (language, messages/conversation) were quantified alongside patient communication behaviours (conversations/user, response-times) which were also compared across sociodemographic groups using non-parametric tests. To understand isolation experiences, conversations were sampled (n=2,791/12,119), English-translated (as necessary), topic-labelled, language-restored, and used to train single-topic classifiers (Traditional-ML/Transformer architectures). Best-performing models meeting a F1≥0.7 cutoff were applied to unlabeled conversations. Topic prevalence and sociodemographic differences were assessed in human-labelled, and human-and-machine- labelled corpora using logistic regression. Results: Rwanda registered 33,081 individuals in WelTel (March 2020-March 2022). Of those, 18% (n=6,021) used WelTel, with variation by sex, COVID-19-status, province, and pandemic-wave (p