Follow-up of patients with severe asthma using a conversational AI virtual assistant: feasibility and impact

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Introduction: The follow-up of patients with severe asthma (SA) represents a current challenge due to disease control variability requiring long-term monitoring. This study aimed to evaluate the feasibility and impact, in terms of reach and adherence, of a follow-up program using a conversational artificial intelligence (AI)-based virtual assistant in patients with SA. Methods: This single-center, prospective observational study followed patients at the Asthma Unit for 22 weeks. The virtual assistant Lola (Tucuvi®) conducted automated monthly calls to collect clinical data. Adults with SA were included, excluding those with language barriers or recent clinical trial participation. Results: A total of 168 patients were included, with a median age of 63 years, of whom 66% were female (demographic data in Fig erj;66/suppl_69/PA3638/F1 F1 F1 1). Over the 22-week period, 818 automated calls were fully completed (4.86 calls per patient), resulting in a program reach of 88% and an adherence rate of 96%. Alerts generated by the AI led to the early detection of 14 exacerbations, 17 medication adjustments, 16 unscheduled evaluation visits, and 3 emergency department referrals. Conclusions: A follow-up program using a conversational AI virtual assistant achieved high reach and adherence, facilitating early detection of clinical deterioration and prompt interventions. However, its clinical efficacy in improving outcomes remains unproven.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.