PS-09Healthcare & Well-being
MaaSuraksha
Offline-First Maternal Risk Screening and Referral Support
SDG 3SDG 5
The challenge
Frontline health workers may need to identify maternal danger signs in villages with weak connectivity and limited access to specialists. Important information such as blood pressure, haemoglobin, gestational age, bleeding, severe headache, swelling or reduced fetal movement may be recorded inconsistently or escalated late. Build an offline-first decision-support tool that structures this information, applies validated red-flag logic or a carefully evaluated model, explains the risk level and supports referral coordination when connectivity becomes available. It must be positioned as support for trained workers, not an autonomous diagnostic system.
What a working submission needs
Create an Android/PWA prototype with offline case capture, multilingual or voice-assisted input, risk-level output with reasons, recommended escalation category, local secure storage and delayed synchronisation to a supervisor dashboard.
Stretch goals
facility matching, transport coordination or follow-up tracking. Use synthetic cases unless appropriate de-identified open data is available.
Optional. A working core counts for more than feature count.Suggested stack
Web/mobile application, AI/ML models, a mapping or dashboard interface, and open APIs as appropriate to the problem.
Scope and responsibility
Healthcare, safety and public-sector challenges are decision-support tools, not autonomous clinical, legal, emergency-command or enforcement systems. Name your prototype’s limitations in the demo. Judging weighs a working core, justified use of AI, technical quality, real-world impact and responsible design above feature count.
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