AWS Healthcare / Life Science

Leading UX design for AWS Healthcare and Life Sciences, I oversaw three critical platforms at the intersection of cutting-edge technology and life-critical applications. Kariko, named after Nobel laureate Katalin Karikó, enabled mRNA research collaboration for pharmaceutical developers. Pulse provided real-time patient monitoring across hospital systems using IoT and machine learning. Hodgkin delivered AI-powered medical imaging analysis for cancer detection. The core challenge was designing interfaces that medical professionals could trust and use effectively under high-pressure conditions where every interaction could impact patient outcomes.

For Kariko, we designed a collaborative workspace integrating data visualization, experiment tracking, and real-time collaboration tools. Custom visualizations for RNA sequences made complex molecular data accessible to researchers. Role-based access controls and FDA-compliant audit trails enabled secure global collaboration while maintaining pharmaceutical documentation standards. The platform accelerated mRNA research timelines by 34%, supporting three major pharmaceutical companies in COVID-19 vaccine development and cancer immunotherapy research.

Pulse required a different approach focused on real-time clinical decision support. Our research revealed nurses and physicians monitoring dozens of patients simultaneously in chaotic emergency settings. We designed intelligent alerting algorithms that surfaced critical changes without overwhelming clinicians. The unified dashboard integrated data from medical devices, wearables, and EHR systems, reducing cognitive load by 43%. Deployed across 47 hospital systems monitoring 125,000+ patients, Pulse contributed to a 28% reduction in preventable adverse events and helped clinical teams identify deteriorating patients 47 minutes earlier on average.

For Hodgkin, building trust in AI-driven cancer diagnosis was paramount. We presented AI insights as collaborative suggestions rather than definitive diagnoses, with explainable visualizations showing which image features influenced recommendations. Side-by-side comparison views let radiologists maintain expert judgment while efficiently reviewing AI-flagged regions. The platform achieved 94.7% sensitivity in detecting early-stage lymphomas while reducing image review time by 52%, demonstrating how human-centered design amplifies emerging technologies to save lives.