Vaccine Genie is a service that turns fragmented and multilingual vaccination records into accurate digital profiles through collaboration between AI and users. AI handles extraction, translation, and structuring, while users maintain final review and approval to ensure trust and safety.
Vaccination records are often needed for immigration, school enrollment, employment, and travel. However, many exist only on paper and in multiple languages, which makes verification slow and error-prone. Digitization was necessary, but fully automated medical data processing could lead to harmful mistakes.
After uploading a document, users did not understand:
what information was extracted,
what needed their attention,
or how confident the system was.
The core issue was not data capture, but the lack of a clear verification experience.
From interviews and market research, we learned:
Users care more about what AI did than how advanced it is.
Trust increases when the original document remains visible.
Unexplained errors feel more threatening than expected inaccuracies.
Users want guidance on what to check first.
Transparency and responsibility are more important than speed.
The interface needed to guide verification, not just display results.
We initially surfaced there features:
Upload
AI extraction and confidence scoring
User confirmation and correction
Verified record storage and sharing
This is about 'Human and AI Collaboration Model.' AI focuses on organization and prioritization. Users remain the final decision makers.
AI suggests, humans decide.
Maintain access to the original document.
Handle errors with clarity, not blame.
Key interaction patterns make verification simple and predictable.
The outcome was not faster automation, but a safer and more guided health record management experience.
I conducted three rounds of usability testing to evaluate how well the core features were understood and used. Additional user testing will be required to support future AI evaluation and refinement.
