A Maternal Health Emergency Coordination Platform reducing maternal mortality in rural Nigeria by connecting frontline health workers, volunteer drivers, and hospitals

Table of Contents

  • The Challenge 

  • My Role & Tools

  • Research

  • Ideation & Prototyping

  • Final Design

  • Impact & Next Steps

The Challenge

Problem Summary
Nigeria accounts for nearly 30% of global maternal mortality. In rural Nigeria, many women die from preventable pregnancy complications simply because they cannot reach a hospital in time. The health system suffers from poor coordination, limited transportation options, and lack of real-time communication tools.

My Role & Tools

Roles

  • UX Researcher
    Led field research and journey mapping in rural clinics
    Conducted field testing and iterative development with CHIPS agents, ETS drivers, and hospital administrators

  • UI/UX Designer
    Designed the CHIPS agent mobile app and the admin dashboard
    Ensured usability through co-creation and low-fidelity prototyping

  • Full-Stack Developer
    Developed the end-to-end system using React and Firebase
    Built location-based ride matching and condition-based hospital routing logic

Tools & Technology

  • Frontend: React, CSS

  • Backend: Firebase (Auth, Firestore, Functions, Realtime DB)

  • Design: Figma, Canva

  • APIs: Google Maps API

  • Fieldwork: Interview guides, journey mapping, usability testing, paper prototyping

Timeline

  • 12 months (July 2024 – July 2025)

Team

  • 2 design engineers (myself + Valentina Arango)

  • Collaborated with CHIPS agents, ETS drivers, and hospital administrators

Research

Understanding Emergency Transport in Rural Nigeria

To design an effective intervention, we began by deeply immersing ourselves in the daily workflows of key stakeholders. Through interviews, field shadowing, and participatory observations, we sought to understand what really happens when a pregnant woman in distress needs help.

Mapping the Emergency Journey

We documented the journey from the moment an emergency is identified to when a woman receives (or fails to receive) care. This user journey revealed critical delays and coordination breakdowns.

Phases of the journey:

  • Seeking care: CHIPS agents (community health workers) conduct home visits and identify complications.

  • Reaching care: Agents write transfer slips and attempt to call local volunteer drivers. Often, drivers are unavailable or unreachable. If a ride is secured, the woman is taken to the nearest hospital.

  • Receiving care: If the hospital cannot treat her condition, she must be transferred again, often when it’s already too late.

Each step marked with a clock icon represents a delay point that puts the patient at risk.

A mapped journey of the emergency response process. We identified the second delay, between identifying an emergency and reaching appropriate care as the key intervention point for UMMA NA.

Key Insights from Fieldwork

  • Paper-based systems slow down care
    CHIPS agents use handwritten logs and referral slips; there's no standardized digital process.

  • Volunteer driver coordination is manual and inefficient.
    When a woman needs urgent transport, CHIPS agents call multiple drivers one by one, trying to find someone who is both available and close by. This process is often slow and unreliable, especially when drivers are in transit or outside phone coverage. There’s no system for real-time availability or automated ride assignment.

  • No visibility into hospital readiness
    Agents send women to the nearest hospital without knowing if it has the right staff or equipment.

  • No central record of transport history or outcomes exists.
    There is no system to track who was transported, where they went, what condition they had, or whether they survived. This lack of data makes it difficult to improve systems or advocate for policy change.

Ideation & Prototyping

From Field Realities to Design Principles

Based on the insights from our research, we defined core design principles:

  • Symptom-based flow
    CHIPS agents are not trained to diagnose. We replaced condition selection with a guided symptom checklist that maps to likely complications.

  • Real-time driver assignment
    Instead of calling drivers one by one, the app automatically matches requests to available drivers based on location and current status.

  • Facility-aware routing
    The system matches patients to hospitals not just by distance, but by their readiness to handle specific complications (e.g. PPH, eclampsia, breech).

  • Minimal and familiar interfaces
    We mirrored the structure of existing paper forms to make the mobile app feel intuitive and reduce the learning curve for CHIPS agents.

Early Concepts & Sketches

We explored various low-fidelity prototypes, testing with real CHIPS agents and healthcare supervisors:

  • Wireframes for the mobile request flow

  • Card-based views of available drivers

  • Facility scoring models based on condition-specific needs

  • Admin dashboard prototypes showing live ride tracking and logs

(Insert low-fidelity sketches/wireframes here)

Iterative Testing

We conducted ongoing testing with stakeholders:

  • CHIPS agents tested early mobile flows using Figma prototypes

  • Health supervisors reviewed and validated triage logic

  • Feedback loops helped refine button sizes, language clarity, and navigation

This iterative approach helped ensure the tool fit real-world constraints while building trust among users.

Final Design

After several rounds of iteration and testing, we arrived at a streamlined, mobile-first emergency coordination system with four core components:

CHIPS Agent Mobile App

Designed for rapid emergency reporting in the field

  • Symptom-based form to avoid requiring medical diagnosis

  • Auto-match to nearby available drivers based on GPS

  • Facility recommendation based on complication readiness

  • Offline-first support for low-connectivity areas

  • Log of past requests and current ride status

ETS Driver App

For accepting ride requests and updating availability

  • Request notifications with patient pickup location

  • Status updates: available, on a ride, unavailable

  • Directions to pickup and hospital drop-off

  • Simple UX optimized for low-literate users

Facility Matching Logic

Smart routing to increase chances of survival

  • Every hospital in the system is profiled for key capabilities

  • Each complication (e.g. PPH, eclampsia, breech) has an ideal treatment requirement

  • The system scores hospitals based on proximity and readiness

  • CHIPS agents are automatically shown the best-matched option

Admin Dashboard (HQ Interface)

For supervisors and coordinators to monitor activity in real time

  • Live map of ride activity, agent and driver status

  • Filters by region, complication type, and facility readiness

  • Manual override tools for emergency reassignment

  • Exportable logs for public health reporting and analytics

Run through of entire system

Impact & Next Steps

While UMMA NA has not yet been piloted, the project has generated strong interest and early traction with key stakeholders in global health and maternal care.

Early Progress

  • Prototype complete
    Mobile app (CHIPS and ETS driver), facility matching logic, and admin dashboard fully developed.

  • Stakeholder validation
    System design and logic reviewed by CHIPS agents, supervisors, and hospital staff during field research.

  • Active discussions with partners
    In advanced discussions with leading maternal health funders about scaling and pilot deployment.

  • Interest from local health authorities
    Ongoing discussions with state-level health agencies to align with national maternal health strategies.

What UMMA NA Will Transform

The current maternal emergency response system relies on:

  • Paper logs

  • Verbal driver coordination

  • No shared hospital database

  • No visibility into complications or bottlenecks

UMMA NA introduces:

  • Real-time digital coordination

  • Location-based ride matching

  • Smart hospital routing based on condition

  • Data capture for planning and accountability

Next

U-GO