Gates Foundation: ANNE Maternal

5 months (2025)

Red Dot Design Award

design system

user testing

mobile

5 months (2025)

design system

user testing

ux design

mobile

Did you know nearly 300,000 women die each year from childbirth and 94% are in LMICs?

Maternal Patient Monitoring System

Anne Maternal develops innovative sensor technologies utilizing care stratification algorithms to enhance risk assessment during labor. By supporting clinician decision-making on labor progression and C-sections, the platform aims to improve pregnancy care quality and reduce adverse maternal and neonatal outcomes in low- and middle-income countries (LMIC). I led the design of the monitoring solution that integrates sensors into healthcare systems in Nigeria, India, and Pakistan.

Problem

When Every Minute Matters, Paper Fails

In many LMICs, over 75% of clinicians still record patient vitals by hand. This manual process is time-consuming, error-prone, and delays urgent decisions—especially during labor. Without access to real-time data, care teams rely on fragmented notes and memory. Clinical scoring systems like MEOWS (Modified Early Obstetric Warning Score) and LCG (Labour Care Guide) also require hours of manual tracking, adding to the pressure on already overextended providers.

In many LMICs, over 75% of clinicians still record patient vitals by hand. This manual process is time-consuming, error-prone, and delays urgent decisions—especially during labor. Without access to real-time data, care teams rely on fragmented notes and memory. Clinical scoring systems like MEOWS (Modified Early Obstetric Warning Score) and LCG (Labour Care Guide) also require hours of manual tracking, adding to the pressure on already overextended providers.

20%

error-prone data

2-3 hrs

per day spent

Solution

Designing for Clarity in Critical Moments

I led the end-to-end design of a product that replaced manual charting with real-time vitals from wearable sensors. By automating MEOWS and LCG scoring, the system reduced clinical workload and enabled faster, more confident decisions during labor.

User testing revealed the importance of factoring in preexisting conditions. I introduced category and time-based sorting in the LCG screen to help clinicians surface risk more quickly. Built-in tutorials supported onboarding and ease of use in low-resource settings.

Impact

Clinicians reported a noticeable reduction in manual workload and felt more confident making time-sensitive labor decisions.

Reduction in Manual

Charting Time

2.5
hrs per shift (avg.)

Clinician

Satisfaction

87%

improved their workflow

Reported
Ease of Use

91%

positive feedback

Visual Design

I built a design system and complex data visualization components.

2025 halie.dsgn™

Stack