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Designed a comprehensive platform that aggregates cancer information from three distinct sources—public web content, AI-generated responses, and clinician-reviewed answers—within a secure, role-based healthcare application. The system helps cancer patients navigate the overwhelming landscape of online health information while maintaining the highest standards of medical accuracy and data security.
Skills
Key Deliverables
- Implemented multi-source scraping from Google, Reddit, Quora
- Built role-based access control for patients, reviewers, admins
- Created clinician review workflows for content validation
- Integrated OpenAI for synthesized medical responses
- Designed transparent source identification UI
The Challenge: Information Overload and Trust Deficit
Cancer patients face overwhelming mix of accurate and misleading content
Two-thirds of cancer patients seek health information online, yet only 30% discuss their findings with clinicians—creating a dangerous verification gap. The internet fragments valuable cancer information across multiple platforms (medical websites, Reddit communities, Quora discussions), but no unified system aggregates these perspectives for comparison.
Patients struggle to distinguish reliable medical information from unverified content. AI-generated answers may sound authoritative but lack clinical validation. And different stakeholders—patients, medical reviewers, administrators—require sophisticated access controls in a healthcare context.
Research shows patients trust in-person healthcare professionals (84%) and personalized materials (75%), but practical constraints limit direct access. This platform bridges that gap by integrating clinician-reviewed content into the digital information ecosystem.
The Solution: Three-Source Aggregation Model
Systematic approach to gathering and presenting cancer information with clinical validation
1. Public Web Content via Firecrawl
Firecrawl intelligently scrapes cancer-related questions and answers from Google, Reddit, and Quora. The system identifies frequently asked questions, retrieves highly-rated responses, and extracts clean, structured data from varied web formats.
The automated scraping process respects rate limits and ethical practices while capturing authoritative medical sources, patient experiences, and diverse expert perspectives. This aggregation ensures patients access the breadth of information available across the web.
2. AI-Generated Responses
OpenAI API integration provides synthesized, evidence-based answers with consistent formatting and medical terminology. The platform explicitly labels these responses as AI-generated, addressing transparency concerns identified in healthcare AI research.
Unlike standalone chatbots that obscure sources, this system contextualizes AI responses alongside clinical and web sources. This approach prevents users from mistaking algorithmic output for validated medical information.
3. Clinician-Reviewed Content
Medical reviewers with oncology expertise validate or flag AI-generated and web responses. They provide authoritative clinical perspectives, add nuanced medical context, and flag potentially misleading content from public sources.
This three-pronged approach aligns with research showing that patients benefit from layered information sources integrating clinical review and transparent validation. The clinician layer becomes the trust anchor.
Role-Based Access Control: Security by Design
Tailored access for patients, reviewers, and administrators
Patient Access
- ✓View aggregated answers to common cancer questions
- ✓Compare information across all three sources
- ✓Access personalized reading recommendations
- ✓Limited permissions protecting reviewer workflows
Medical Reviewer Access
- ✓Review and validate AI-generated responses
- ✓Flag inaccurate public web content
- ✓Add clinical annotations and context
- ✓Access audit logs of content changes
Administrator Access
- ✓Manage user accounts and role assignments
- ✓Configure scraping sources and frequency
- ✓Monitor system health and API usage
- ✓Review compliance and security settings
Security & Authentication
The platform implements JWT-based authentication with secure token management, bcrypt password hashing, and automatic session timeout for inactive users. API rate limiting prevents abuse while input validation at both frontend and backend prevents injection attacks.
These measures align with HIPAA Security Rule requirements for administrative, physical, and technical safeguards protecting electronically stored health information. Comprehensive audit trails log all access to sensitive information for compliance verification.
User Experience: Designing for Trust
Visual transparency and consistent design throughout
Visual Transparency
Each answer clearly indicates its source with distinct visual treatments. Public web content shows original platforms and dates. AI responses display generation timestamps. Clinician content shows anonymized reviewer credentials and review dates.
Consistent Interface
Inconsistent interfaces erode trust in healthcare applications. The platform maintains consistent typography, spacing, and color schemes throughout. WCAG 2.1 AA accessibility standards ensure mobile-responsive layouts work for patients accessing information anywhere.
Comparison Interface
The core feature allows users to see all three source types side-by-side, identify areas of consensus and divergence, understand relative confidence of different sources, and access supplementary resources. This structured presentation empowers informed decision-making.
Technical Implementation
Frontend Architecture
React with TypeScript provides type-safe, maintainable user interfaces. Component reusability with strongly-typed props, predictable state management, and IDE support reduce runtime errors and improve developer productivity.
Backend Performance
FastAPI delivers async request handling for high-performance I/O operations. Automatic OpenAPI documentation, Pydantic type validation, dependency injection for clean authentication, and background tasks for scheduled scraping ensure scalability.
Web Scraping Intelligence
Firecrawl handles dynamic content loading, respects platform rate limits, filters low-quality responses, and standardizes varied HTML structures into consistent data models—solving the core challenge of extracting structured data from diverse web sources.
AI Integration
OpenAI API generates synthesized medical information while maintaining explicit transparency about AI-generated content. The system integrates AI outputs within a clinical validation workflow rather than presenting them as standalone answers.
Impact and Healthcare Implications
Measurable improvements in patient information access and clinical workflow
Information Quality
Aggregating clinician-reviewed content alongside public and AI sources provides patients validated medical information previously scattered across the internet.
Trust Building
Transparent source identification and multi-perspective approach builds patient confidence in information received, potentially increasing discussion of online findings with care teams.
Reviewer Efficiency
Medical reviewers systematically validate AI-generated content and flag problematic public information, scaling their expertise beyond individual patient interactions.
Comparative Learning
Patients understand how different sources approach the same question, developing critical evaluation skills for future information seeking beyond this platform.
Healthcare Technology Principles
Transparency Over Opacity
Explicitly labeling AI-generated content addresses trust concerns in healthcare AI research. Users always know the source and nature of information.
Clinical Integration
Involving medical reviewers in content validation bridges the gap between automated systems and clinical expertise. Technology amplifies clinical judgment rather than replacing it.
Multi-Source Synthesis
Acknowledging that cancer information exists across platforms and communities, this approach aggregates diverse perspectives rather than forcing information into a single source.
Security-First Design
Implementing RBAC and comprehensive security from the outset ensures scalability and compliance with healthcare regulations like HIPAA.
Conclusion: A Model for Trustworthy Health Tech
The AI-Powered Cancer Education Platform demonstrates that healthcare technology can be simultaneously innovative and trustworthy. By combining modern web scraping, AI synthesis, and clinical expertise within a secure, role-based application, the platform creates a unique information ecosystem that respects both the promise of technology and the irreplaceable value of medical expertise.
In a field where information quality directly impacts health outcomes, this platform offers a model for how technology can serve as a bridge between patients' information needs and the medical community's commitment to evidence-based care.
Technologies Used
Frontend
- React
- TypeScript
- JWT Authentication
Backend
- FastAPI (Python)
- Role-Based Access Control (RBAC)
- HIPAA Compliance
Data & AI
- Firecrawl Web Scraping
- OpenAI API
- Pydantic Validation
Security
- JWT Tokens
- bcrypt Hashing
- API Rate Limiting
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