MomSAFE

Introduction

Empowering expectant mothers through AI-driven care.

During our user interviews, an expectant mother told us: "I would love a chat feature with professionals at 2 AM when I'm struggling." That sentence captures the core problem MomSAFE exists to solve; pregnancy doesn't follow office hours, and for millions of women, there's no one to ask when something feels wrong at midnight.

As a UX Researcher & Designer, I conducted expert interviews with labor nurses and OB/GYNs, and user research with expectant mothers. One thing became clear: the gap wasn't just about access to doctors; it was about access to understanding. Mothers couldn't parse medical language, couldn't tell when a symptom was normal, and couldn't get reassurance when they needed it.

MomSAFE uses LLMs and machine learning to bridge that gap: synthesizing user inputs and clinical data to deliver personalized, real-time pregnancy insights in language mothers actually understand.

My role: UX researcher on a 3-person team. I conducted interviews with expectant mothers and healthcare providers, synthesized findings into design requirements, and shaped the research direction and recommendations.

Global Alignment

Designing for the UN's Sustainable Development Goals.

Problem Statement

The maternal healthcare gap is a crisis hiding in plain sight.

The numbers are staggering: women in nearly half of U.S. counties lack access to an OB/GYN, and 800 women die daily from preventable pregnancy complications. Yet when we asked expectant mothers about their care, most said "fine" because they had no benchmark for adequacy. They didn't know what they were missing.

User Research & Insights

Doctors and mothers told us
completely different stories.

Healthcare professionals saw a medical literacy problem: mothers panic over normal symptoms or ignore serious ones because they don't understand clinical terminology. Mothers saw an access and timing problem: they knew something felt off but couldn't reach anyone when it mattered. Both were right, and MomSAFE needed to solve both.

Key Features

Every feature traces back to
something a mother or nurse told us.

The AI chatbot exists because mothers need answers at 2 AM. Simplified language exists because a labor nurse told us "it's not expected that an average mother understands complex medical terms." Survey check-ins exist because OB/GYNs told us longitudinal data is the single most valuable input for early risk detection. Every feature is rooted in a specific research finding.

Design Direction

Research-informed principles for accessibility, inclusivity, and trust.

A striking finding: health apps often made mothers more anxious. Overloaded dashboards, alarming colors, and medical jargon turned tracking into stress. This shaped MomSAFE's design: a calm experience that surfaces only what's relevant, frames insights as guidance rather than warnings, and uses explanatory language. We iterated on Figma screens to illustrate these principles; a full prototype remains future work.

Design Decisions

Two trade-offs the research forced
us to make.

Mothers and providers gave us conflicting signals about what MomSAFE should be. Each conflict surfaced a real design choice, and the wrong call in either direction would have undone the research.

Impact & Future Vision

Where MomSAFE goes from here.

MomSAFE is a proof of concept. What we demonstrated is that AI, simplified medical language, and 24/7 access address real pain points mothers and healthcare providers independently confirmed. Next steps would require clinical validation, regulatory review, and healthcare partnerships. The research foundation is solid, and the design framework is ready to scale.