How GenAI is Helping Reimagine Antenatal Care in A Low-Resource Setting: From Provider Enablement to Patient Empowerment

2026-04-24

AI Summary

This work addresses Pakistan’s persistently high maternal mortality, driven by fragmented paper-based records, low literacy, scarce healthcare resources, and gender-related barriers that disrupt care continuity. To overcome these challenges, the authors developed and deployed Awaaz-e-Sehat, a voice-based generative AI system. Initially designed to assist clinicians in auto-generating Urdu electronic health records, it evolved into a WhatsApp-integrated platform enabling pregnant women to independently create structured clinical notes via voice input, receive prenatal guidance, and share their records across facilities using QR codes. By empowering patients as active producers and owners of their health data, the system reimagines electronic health records and clinical decision support as dynamic tools for self-advocacy and shared accountability. Deployed in resource-constrained settings, Awaaz-e-Sehat facilitates a patient-centered transformation of antenatal care, significantly enhancing health data continuity and patient engagement.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Despite steady global advances, maternal mortality remains alarmingly high in Pakistan (155 deaths per 100,000 live births in 2023); largely as a consequence of fragmented paper records, low literacy, poor access to quality healthcare, and gendered barriers that compromise care continuity. Over three years, we designed, deployed, and iteratively developed Awaaz-e-Sehat, a speech-based artificial intelligence (AI) system that generates electronic medical records (EMRs) and supports decision-making in maternal health. The tool evolved from a clinician-facing AI assistant that automated Urdu speech-to-EMR generation into a patient-centred WhatsApp-based platform, enabling women to generate their own structured clinical notes, receive AI-generated antenatal guidance, and share QR-coded records with providers anywhere in the country. This case study documents that translational journey, i.e., how the ground realities of workload, linguistic nuance, and infrastructural constraints reshaped our design. The result is not merely a new method of record-keeping, but a reimagining of antenatal care and electronic medical records themselves. In settings where clinicians are time-constrained and have little institutional incentive to document, Awaaz-e-Sehat proposes a model of care that centres patients as active participants in generating and owning their health data. By keeping patients informed about their own risk factors and integrating them into the clinical decision-support loop, the system transforms EMRs and CDSS from static institutional artefacts into dynamic tools for self-advocacy and shared accountability in maternal health.

Problem

• maternal mortality
• antenatal care
• low-resource setting
• electronic medical records
• healthcare access

Innovation

• Generative AI
• Speech-based EMR
• Patient-centered care
• Maternal health
• Clinical decision support system