Chat-Based Decision Support System for the Maternal Health Journey in Assam, India: Protocol for a Mixed Methods Multiphase Implementation Study.

Background: Assam, India, has the country's highest maternal mortality ratio (195 per 100,000 live births), mainly due to poor access to and quality of maternal health (MH) care. Many women receive inadequate antenatal and postnatal services, made worse by isolation, socioeconomic barriers, and weak health care infrastructure. Digital tools like mobile messaging and chatbots have improved antenatal care (ANC) and facility-based deliveries in similar settings. The e-SAATHI (Strengthening ANC/PNC via AskNivi Tailored Health Information, Referrals, and Follow-Up) project aims to provide personalized, stage-specific maternal health support through a chat-based system in Assam. Objective: This study assesses the acceptability, feasibility, and effectiveness of the e-SAATHI chatbot in increasing women's access to MH information and improving ANC and PNC service uptake across public and private facilities. Objectives include increasing ANC/PNC use (eg, ≥4 ANC visits and timely PNC), promoting respectful care, and gathering insights for scaling digital health in high-burden regions. Methods: Phase 1 (0-3 mo) involves co-designing and pilot testing aligned with World Health Organization and national guidelines. Phase 2 (4-24 mo) involves enrolling pregnant and postpartum women via health facilities and social media. The chatbot sends 2-3 messages weekly from 10-week pregnancy to 15 weeks postpartum. About 300 health care providers will be trained and engaged for onboarding and feedback. Phase 3 (25-36 mo) involves scaling up across districts, reaching 225,000 women. Data collection includes interviews, surveys, facility assessments, and chatbot analytics. Qualitative analysis will explore experiences; quantitative data (ANC completion, facility delivery, PNC follow-up, and satisfaction) will compare pre- and post-interventions. Ethical approvals, informed consent, and data confidentiality are observed. Results: The study was funded in September 2022. As of August 2025, 210 facilities have been onboarded, and 201,813 women were enrolled. Chatbot-based data collection began in April 2023 and will continue through the study period. Qualitative and quantitative evaluation data collection started in November 2023 and is expected to complete in June 2027. Interim analyses will be conducted after midline data collection in 2026; final analyses will be performed after endline data collection in 2027. The primary outcome will be the change in the composite quality score of maternal and newborn care. Secondary outcomes will include service uptake indicators, user-reported knowledge and self-care practices, and satisfaction with care. Operational feasibility-including provider integration and barriers such as digital literacy and connectivity-will also be assessed. Ongoing collaborative learning and adapting cycles are expected to capture intervention adaptations and inform optimal strategies for scale-up. Conclusions: e-SAATHI offers a scalable digital approach to improve MH across a variety of sociodemographic, linguistic, and risk settings. By delivering timely, personalized support, the chatbot may enhance health-seeking behavior and outcomes in Assam and in similar low-resource areas globally.