Background: The prevalence of opioid use disorder (OUD) among women giving birth in US hospitals continues to rise. Increasing fatalities due to perinatal drug overdose have in part been attributed to decreased access to social support and treatment services. In recent years, mobile health (mHealth) technologies have been used to provide care to patients outside of traditional clinical settings. Given that perinatal OUD treatment works best when there is frequent contact between patient and clinician, harnessing mHealth could be valuable for improving patient engagement in perinatal OUD care. Objective: To address the increasing epidemic of perinatal OUD, which is compounded by loneliness and social isolation, this study aimed to adapt an existing mHealth chatbot intervention for postpartum individuals by developing educational and supportive content relevant to people in the postpartum period experiencing OUD and recovery. Methods: We used a participatory design to conduct a needs assessment via in-depth qualitative interviews with patients (n=12) and providers (n=11). Interview data were analyzed using NVivo software (Lumivero) according to a priori codebooks and using inductive coding, which identified major themes to address in a future adaptation of an interactive chatbot. An advisory board was convened to review the data and contribute to content generation that would be most useful for perinatal mothers who are in treatment for OUD. Results: Patients and providers agreed that an interactive chatbot would be useful for patients seeking information on childbirth recovery, substance use recovery, pain management, and infant care, including feeding. Patients specifically wanted a chatbot that felt personal and addressed their individual and specific needs. Providers indicated that the chatbot should address topics that came up frequently in after-hours phone calls to reduce provider burden. Conclusions: Conducting an in-depth participatory-driven needs assessment will support the refinement of an existing chatbot to better meet the needs of a high-risk, difficult-to-reach population. As we move forward to test the chatbot for acceptability and #feasibility, data from the needs assessment will support ongoing iterative enhancement of the content, delivery, and safety of the chatbot.

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