HealthFlow

Inspiration

We noticed that medical information is often available, but not organized in a way that is easy to use in everyday life.

A prescription may contain several medicines with different timings, while medical reports can contain terms and numbers that are difficult for a normal person to understand. Important information can also end up scattered across phone galleries, paper documents, messages, and different apps.

That made us think: what if a medical document could become the starting point for a simple, organized health record instead of just another photo sitting in a gallery?

That idea became HealthFlow.

What it does

HealthFlow allows users to upload medical documents such as prescriptions and reports. The system uses OCR/document understanding and AI to extract relevant information and turn it into structured data.

For prescriptions, the extracted information can be reviewed by the user and then used to create a medication schedule and reminders. For medical reports, HealthFlow can provide simple explanations of medical terms and selected health values.

The information is then organized into a personal health timeline so users can find previous prescriptions, reports, medications, and other records in one place.

How we built it

Our planned architecture uses a modern web application with Next.js and TypeScript for the frontend, Python and FastAPI for backend services, and PostgreSQL for structured data storage.

For document processing, we combine OCR/document vision with AI-based structured extraction. Extracted information is validated against defined schemas and shown to the user for verification before it becomes part of their health record.

We deliberately keep important operations such as reminder scheduling, date calculations, permissions, and health calculations under deterministic software logic rather than allowing an AI model to make those decisions on its own.

Challenges

One of our biggest challenges was deciding how AI should be used in a healthcare-related application.

We did not want HealthFlow to simply read a prescription and make decisions for the user. Medical information needs an extra layer of care, so we designed the system around AI-assisted extraction followed by user verification.

Another challenge was turning information from documents with different layouts and formats into consistent, structured data that the rest of the application can reliably use.

What we learned

While working on HealthFlow, we learned that building a useful healthcare application is not only about adding AI. The way information is verified, structured, stored, and presented is just as important.

We also learned that a good user experience can make complex information much easier to manage. Instead of trying to replace doctors or make medical decisions, our goal is to make the information people already receive more organized, understandable, and easier to keep track of.

What's next

We want to expand HealthFlow beyond prescriptions and reports by adding more document types, multilingual support, better health-history visualization, caregiver/family sharing, exportable health summaries, and integrations with other health data sources.

Our long-term goal is simple: turn medical documents into organized, understandable health information that people can actually use.

Built With

• ai
• artificial
• document
• fastapi
• intelligence
• language
• large
• models
• next.js
• ocr
• postgresql
• python
• react
• typescript