Inspiration
The growing trend of people facing health issues globally highlights the need for a solution to effectively track and manage the combination of ailments a patient is experiencing.
What it does
Care.AI addresses the challenge of vague and incomplete information in medical histories. Our platform helps individuals present detailed and accurate accounts of their health issues, such as panic attacks, migraines, and blackouts. By combating memory fog, Care.AI ensures a clearer picture for doctors, minimizing delays and enhancing the quality of medical diagnoses and support.
Inspired by the structure of heatmap and charts for open source projects, Care.AI incorporates visual representations displaying a year's worth of crisis logs. The color variations on the charts indicate the intensity and frequency of reported health issues, enhancing visibility and comprehension.
The Crisis Log form design draws inspiration from Google Notes, prioritizing crucial details like duration of pain, pain levels, and date and time. These elements play pivotal roles in the medical diagnostic process.
To consolidate insights and guarantee the accuracy of information before going for a medical consultation, the health assistant queries details across all logs, builds vectors from contextual log information minimizing the risk of AI hallucination, to present a virtual AI consultation experience with Overall feedback and improvement details to help jog your mind to present accurate details to your doctor.
How I built it
- MongoDB: Utilized as the main database to store and manage Crisis Logs.
- Pinecone: Implemented for AI agent metric, enabling efficient analysis of queries from all the Logs.
- Prisma Studio: Used to manage multiple databases and streamline database operations.
- Nextjs, TailwindCss, Typescript: As Frontend of The Platform.
- Clerk Auth: For Authentication and Middleware security for separation of Data in different Accounts.
Challenges I ran into
Creating a seamless and secure user experience, implementing effective AI for insights, and time constraints were thrilling to tackle.
- Lot of Debugging
- AI agent should not hallucinate and provide precise answers. Solved it by using a vector storage database.
- Heat Map should summarize information in a more human way like 'high' instead of numeric 8 as pain level.
Accomplishments that I'm proud of
Successfully integrating heatmap and charts features into Care.AI and overcoming challenges in designing an effective and user-friendly platform for health tracking and management.
What I learned
Throughout the development process, I learned the importance of balancing user experience with technical complexity. I also gained insights into the nuances of health data interpretation and scoring within an AI framework.
What's next for Care.AI
The future for Care.AI involves continuous improvement and expansion.
Built With
- clerk
- nextjs
- pineconedb
- tailwindcss
- typescript
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