Dawnbreak AI

The Challenge

Doctors often spend valuable consultation time taking notes instead of fully engaging with their patients. Manual documentation leads to errors, inefficiency, and burnout, while patients may feel less heard during appointments. Traditional transcription tools are either inaccurate, lack medical context, or fail to integrate seamlessly into healthcare workflows.

Project Process & Timeline

Understanding Clinical Insights & Information Architecture

I conducted qualitative interviews with attending physicians, medical scribes, and outpatient care teams to map clinical documentation bottlenecks. This led to an encrypted, multi-tier Information Architecture (IA) schema connecting ambient voice capture directly to automated SOAP notes, SNOMED-CT/ICD-10 terminology tagging, and EHR integration.

Dawnbreak AI Information Architecture & HIPAA-compliant clinical voice transcription workflow board.

Designing the System

Dawnbreak AI was designed as an intelligent healthcare communication platform that allows doctors to focus on patients — not paperwork. The app records doctor–patient conversations securely and automatically generates accurate, structured transcripts using AI. These transcripts are organized into medical summaries, helping doctors review cases faster and make more informed clinical decisions. The system also enables smooth communication between patients and healthcare professionals, promoting transparency and trust.

Overcoming Obstacles & Prototyping

One key challenge was ensuring transcription accuracy, especially for medical terminology and multi-speaker detection. Balancing a minimal, user-friendly interface with advanced features like real-time recording, playback, and note tagging required thoughtful design iterations. Privacy and data security were also top priorities; we implemented encrypted storage and clear consent flows to meet healthcare compliance standards. Through continuous testing with healthcare professionals, the design evolved into a tool that felt intuitive, reliable, and clinically relevant.

Dawnbreak AI mobile interface — real-time audio waveform recording & automated SOAP clinical note summary generator.

Medical Design System & UI Specs

A specialized healthcare design system was constructed to combine high clinical legibility with accessible interface states. Headings leverage Instrument Serif typography while data tables and medical badges use crisp Inter sans-serif typography.

Dawnbreak AI medical design system — clinical icon set, status badges, typography scale, and component guidelines.

Doctor & Patient Validation Feedback

During clinical pilots across outpatient clinics, feedback was collected from both attending physicians and patients to evaluate time savings, accuracy, and emotional reassurance.

Key Results & Summary

Dawnbreak AI bridges the gap between medical expertise and intelligent automation. By reducing administrative workload and improving accuracy in documentation, it allows doctors to dedicate more attention to patient care. This project deepened my understanding of designing for healthcare technology — balancing usability, empathy, and compliance to create a seamless experience for both doctors and patients.

Key Takeaways & Reflections