Simplifying Healthcare Policy with AI

CGAdvocacy is an AI-powered platform designed to simplify complex healthcare policy documents, enabling the public to engage more effectively with healthcare regulations. By leveraging Natural Language Processing (NLP) models and Retrieval-Augmented Generation (RAG) techniques, the platform transforms dense policy data into digestible summaries, making it accessible to non-experts.

Christine played a pivotal role in both the vision and development of the CGAdvocacy platform. She not only led the strategic development and technical integration but also recruited developers, convincing them of her vision for how AI could enhance public engagement with healthcare policy. Her leadership and vision were crucial in building the initial platform and ensuring its success.



democracy

RAG + AI

Challenge OR Problem statement

Navigating healthcare policy documents is a daunting task for advocacy groups, policymakers, and the public. Dense language, extensive length, and technical jargon create significant barriers to understanding, limiting meaningful engagement and actionable feedback.

MENU

/ Advocacy Groups /

/ Public Stakeholders /

/ Policymakers /

"Quickly analyze healthcare policy documents to craft effective responses."

"Understand policies and provide feedback without requiring technical knowledge."

"Collect informed, actionable feedback from diverse stakeholders."

Jobs-to-be done

Key Collaborators:
  • AI developers specializing in NLP.
  • User experience researchers.
  • Policy experts and advocacy groups providing domain-specific feedback.
  • CU Ventures
  • NSF iCorps

My Role:
  • Led the project from concept to implementation, including AI training, design oversight, and stakeholder engagement.

Meet Dev Team

Collaborations and Acknowledgements

Key Technologies/Tools:
  • AI & NLP: Integrated GPT-4 and NLP techniques to preprocess, summarize, and streamline complex healthcare documents, ensuring clarity without sacrificing critical details.
  • Retrieval-Augmented Generation (RAG): Used RAG techniques to retrieve relevant data from a large corpus of healthcare policies and feed it into the AI models, enhancing the accuracy of document summarization.
  • Python: Contributed to building and optimizing AI models using Python, ensuring seamless integration with the platform.
  • Azure Cosmos DB: Managed the large-scale storage and retrieval of healthcare policy documents through Azure Cosmos DB, allowing for real-time access to relevant data.
  • UX/UI Design: Christine led the UX design process, focusing on creating an intuitive interface that made the platform user-friendly for non-expert users.
  • Design Thinking: Facilitated design thinking workshops to ensure that the platform was built around user needs, optimizing both usability and engagement.

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Approach and Methodology

/ Tailored Information /

/ Enhanced Engagement /

/ Efficiency & Accessibility /

Highlights the most relevant sections of lengthy policy documents, personalizing content to user needs.

Simplifies dense technical language for broader understanding.

Guides users in crafting meaningful feedback for policy decisions.

Supports effective public comments through structured, actionable suggestions.

Processes 1,900-page policy documents in under 15 minutes, significantly reducing cognitive load and review time.

Incorporates AI models designed with explainability and ethical transparency.

Features

Future directions

Integration with Public Platforms

AI-Enhanced Collaboration Tools

Multilingual Support

Policy Evolution Tracking

Personalization Features

Challenges:
  • Complex Document Summarization: One of the main challenges was ensuring that the AI could accurately summarize intricate policy documents while retaining the original intent. Christine’s strategic oversight was critical in balancing the technical depth of NLP with the need for simplified, user-friendly content.
  • Public Engagement: Another challenge was ensuring that the platform was accessible and intuitive for a non-expert audience, despite the technical complexity of its backend systems. Christine’s expertise in UX design helped bridge this gap.
Outcome:
  • CGAdvocacy successfully allowed the general public to engage more deeply with healthcare policy by providing AI-generated, RAG-enhanced summaries that simplified complex documents. This led to increased public participation in regulatory feedback processes.
  • The platform was praised for its user-friendly design and its ability to streamline policy documents in a way that made them accessible to non-experts, demonstrating the effectiveness of AI-driven civic engagement tools.
  • Accepted and completed NSF Research to Markets in iCorps West Hub.

Challenges and outcomes

revolutionize  policy engagement

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