Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{199796,
        author = {Vaishnavi Keshav Rawankar and Harshada Yuvraj Patil and M. E. Maniyar},
        title = {Neo Maternity Care: An Integrated AI-Driven System for Automated Documentation, Risk Analysis, and Medication Vigilance in Maternal Healthcare},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15722-15727},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199796},
        abstract = {The current standard traditional maternal care systems are inadequate to cope with the fragmented data and this could be responsible for the administrative overhead and delay in life-saving procedures in case of severe gestational hypertension, anemia, etc. In this work, we propose Neo Maternity Care, an end to end web-based AI driven system to automate clinical documentation and provide decision support for obstetrics in real time. We have developed a modular system that takes inputs from doctor-patient conversation using speech recognition (Whisper-based) and natural language processing to generate a SOAP note. We then include the Maa AI maternal risk stratification model and the Med-Vigilance part to do prescription review against pregnancy safe drugs using Named Entity Recognition. Aggregating laboratory report results through OCR, it proceeds to automate patient follow-ups through WhatsApp API thus converting it from a data storage to a live clinical assistant. Our pilot validation study demonstrated a decreased workload on manual documentation and increase in care continuity due to all the intelligent components. Neo Maternity Care is an efficient modular and technically sound system.},
        keywords = {Maternal Healthcare, Clinical Decision Support Systems, Natural Language Processing (NLP), Med-Vigilance, Automated Documentation, Risk Stratification.},
        month = {April},
        }