Our inspiration came from the people who struggle the most with healthcare access—immigrants, low-income families, and non-tech-savvy individuals. We encountered stories like that of Maria, an immigrant mother who works multiple jobs but finds it overwhelming to navigate the complex U.S. healthcare system. Maria’s challenges of finding in-network providers, understanding insurance plans, and overcoming language barriers sparked the idea for our GenAI Healthcare Assistant. We wanted to create a tool that would help people like Maria get the healthcare they deserve, without facing insurmountable hurdles.
What We Learned During the project, we gained deeper insights into the complexity of healthcare systems and how overwhelming they can be for underserved populations. We learned:
Healthcare navigation is a significant challenge for many, especially when they lack knowledge of medical terms, insurance policies, or access to digital tools. Insurance literacy is low, and many people do not understand the terminology or how to choose the right plan for themselves or their families.
Language barriers further complicate the process for immigrants and non-English speakers. We also discovered the potential of AI in simplifying communication, offering personalized assistance, and creating more inclusive, accessible tools.
How We Built Our Project We started by identifying the key pain points that low-income and immigrant populations face when accessing healthcare. We designed a GenAI-powered healthcare assistant that:
Identifies in-network providers based on the patient’s insurance, location, and medical needs. Simplifies insurance explanations into easy-to-understand terms. Offers multi-lingual support, allowing patients to interact with the AI in their native language. Our technical stack involves LLMs to dynamically generate SQL queries from the natural conversations to provide recommendations over time. We will also plan to integrate the solution with Electronic Health Record (EHR) systems to streamline provider matching in the future.
Challenges We Faced One of the main challenges we encountered was:
Data Access: Ensuring access to accurate, real-time data about healthcare providers and insurance plans was critical, yet challenging. Integrating with external APIs and making sure that the data we presented was up-to-date requires significant effort.
Addressing Complex Insurance Terms: Making insurance terms and options understandable for non-experts was a considerable challenge. We worked hard to break down complex terminologies into simple, clear language.
Scalability and Accessibility: Designing a solution that could scale to a variety of clinics, while still being user-friendly for non-tech-savvy users, required multiple iterations and feedback from healthcare professionals. Despite these challenges, our focus remains on building a scalable solution.
Conclusion Building this project taught us the power of technology for social good. We hope our solution can bridge the gap between underserved communities and the healthcare system, empowering individuals to take control of their healthcare journey.
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