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Content Summary
The post describes a personal project where the author built a mini AI-powered EHR system over two weekends using tools like Firebase, Next.js, and Genkit. The system includes features such as secure patient onboarding, ICD-10/CPT/SNOMED search, structured note-taking, and six AI agents for tasks like diagnosis prediction and preventive care gap analysis. The author emphasizes a fast, iterative approach, focusing on user testing and AI-first thinking. Comments highlight challenges in adopting new EHR systems and interest in open-sourcing the project.
Opinion Analysis
Mainstream opinion is that the project is impressive and highlights the potential of AI in healthcare. Many commenters appreciate the focus on user testing and AI-first design. However, there are conflicting views on the feasibility of competing with established EHR systems like Epic. Some believe it's difficult to get professionals to try new systems, while others see value in open-source alternatives. There's also debate about the role of AI in healthcare—some view it as a useful tool, while others caution against over-reliance on AI without proper oversight.
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Firebase | https://firebase.google.com | Backend Services | Authentication, Firestore for database |
| Next.js | https://nextjs.org | Web Framework | App Router, TypeScript support |
| ShadCN | https://ui.shadcn.com | UI Components | Tailwind CSS-based design system |
| Genkit | https://github.com/GoogleCloudPlatform/genkit | AI Toolkit | Integrates with Gemini for agent development |
| Gemini 1.5 Pro | https://deepmind.google/ | LLM | Used for AI agents in the project |
| React Hook Form | https://react-hook-form.com | Form Handling | For form validation and management |
| Zod | https://zod.dev | Schema Validation | For data validation |
| Tailwind CSS | https://tailwindcss.com | UI Styling | For styling components |
| TypeScript | https://www.typescriptlang.org | Programming Language | Used throughout the project |
USER NEEDS
Pain Points:
- Fragmented documentation and care planning in healthcare
- Lack of user-friendly AI-assisted tools for clinicians and patients
- Difficulty in getting professionals to try new EHR systems due to existing solutions like Epic
Problems to Solve:
- Improve patient onboarding and encounter logging
- Provide structured note-taking based on clinical standards (SOAP)
- Enable AI-augmented workflows for diagnosis, compliance, and preventive care
Potential Solutions:
- Developing an AI-first EHR system that integrates seamlessly into clinical workflows
- Creating open-source or community-driven tools to encourage adoption
- Focusing on user testing and iterative development to ensure practical usability
GROWTH FACTORS
Effective Strategies:
- Focusing on a minimal viable product (MVP) with clear clinical relevance
- Iterative development with working vertical slices
- Prioritizing user testing and real-world use cases
Marketing & Acquisition:
- Leveraging open-source contributions to build community and visibility
- Sharing personal stories and use cases to highlight value
- Engaging with niche communities (e.g., r/aiagents) for feedback and early adopters
Monetization & Product:
- Emphasizing non-commercial, curiosity-driven development to build trust and credibility
- Highlighting the importance of product-market fit through user feedback
- Focusing on technical excellence and developer experience (e.g., TypeScript, Next.js)
User Engagement:
- Encouraging community involvement through GitHub and open-source sharing
- Using Reddit as a platform to gather feedback and spark discussions
- Building a prototype that aligns with real-world needs to drive engagement