CareExplainAI

1) Project Title CareExplain AI — Your Multilingual Care Companion for Understanding Medical Reports

2) Project Description Chicago is one of the most linguistically diverse cities in the United States. Over 35% of Chicago residents speak a language other than English at home, and nearly 15% speak English less than “very well,” creating significant barriers to accessing healthcare information. Across Illinois, about 1 million residents have limited English proficiency, many living in the Chicago metro area where immigrant communities are rapidly growing. When patients leave hospitals, they often receive discharge instructions filled with complex medical terminology—in English. For immigrant and multilingual patients, misunderstanding these instructions can lead to missed medications, complications, and avoidable hospital readmissions. CareExplain AI solves this problem by turning medical documents into clear, understandable guidance in the patient’s own language. Users can: ● Upload discharge papers, prescriptions, or lab reports ● Instantly receive plain-language explanations ● Translate explanations into multiple languages, including Nepali and Burmese ● Listen to instructions aloud with natural voice playback ● Ask questions through an AI chat grounded in their actual medical documents

By transforming complex medical instructions into accessible, multilingual care guidance, CareExplain AI helps bridge one of healthcare’s most critical gaps: understanding.

What did you struggle with? ● Integrating multiple complex features—document parsing, multilingual chat, voice playback, and a clinic locator—into one coherent system without breaking existing functionality. ● Managing the limited time constraint of building a fully functional, reliable application in less than 48 hours. ● Preventing technical issues such as overlapping voice playback and maintaining stable conversation context when multiple documents were uploaded.

What did you learn? ● How to build a full-stack AI application that connects document analysis, conversational AI, and real-world APIs into a single workflow. ● How to collaborate effectively in a team by managing tasks, dividing responsibilities, and coordinating under tight deadlines. ● The importance of grounding AI outputs in real medical documents to ensure the system remains trustworthy and useful. ● How thoughtful UI/UX design can make AI more accessible, especially for vulnerable populations such as immigrants and uninsured patients.

● How to rapidly prototype, debug, and iterate within hackathon time constraints while prioritizing features with meaningful social impact.

Built With

• reach
• tailwind