Health Bridge

Health Bridge acts as a link between healthcare providers and patients to improve customer understanding. This application uses AI and ML to 'bridge' the gap between patients and doctors.

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Inspiration

Throughout the healthcare industry there are many gaps in patient care that struggle to be filled because of time and people constraints. This means that many people are confused about their health in various different situations. We were inspired by our and other people's struggles with that confusion and sought to find a solution, especially for people who face many obstacles in their pursuit for understanding.

What it does

Our web application has several components that perform many necessary, interesting functions. Firstly, we implemented a pdf analyzer which examines uploaded pdf's of bloodwork and synthesis the information in a relevant, simple way. This feature explains to the patient the results of their blood test and how they can use those results to build a happier, healthier future. Next, we implemented a chatbot. Asking questions, especially about confusing medical jargon, can be a hassle and stressful when talking to a clinic where one person is juggling many tasks. Instead, our AI chatbot will listen and answer any questions that may arise, whether its about medical test results, symptoms, or anything else. We also are integrating machine learning to produce suggestions for patient care. Many times when patients have certain symptoms and the severity or length of time is low, patients prefer 'waiting it out'. This feature gives patients an opportunity to understand their condition and recommend over-the-counter medication to help alleviate symptoms. The symptoms and analysis can be sent to their primary physician for further support.

How we built it

We built Health Bridge as a web application using a modern full-stack approach. The frontend is powered by React, providing a dynamic and responsive user interface. The backend uses FastAPI, handling data processing, AI integration, and machine learning recommendations. For data storage, authentication, and database management, we relied on Firebase, which allows secure storage of patient PDFs and other sensitive information. Key features include a PDF analyzer for bloodwork results, an AI chatbot for answering patient questions, and a machine learning system to provide personalized care suggestions.

Challenges we ran into

Complex data extraction: Developing the PDF analyzer required sophisticated parsing and interpretation of medical bloodwork reports, ensuring that clinically relevant data could be accurately extracted and translated into clear, patient-friendly insights.

Medical accuracy and safety: Maintaining a high standard of reliability in chatbot responses and care recommendations demanded careful prompt design, validation, and iterative testing to ensure the information remained helpful, responsible, and medically appropriate.

Integration of multiple technologies: Orchestrating a seamless workflow across React (frontend), FastAPI (backend), Firebase (authentication and storage), and AI/ML components presented significant architectural and integration challenges, particularly in maintaining efficiency and data consistency across services.

User experience considerations: Designing an interface accessible to users with varying levels of health literacy and technical familiarity required multiple design iterations, usability testing, and a strong emphasis on clarity, simplicity, and intuitive navigation.

Accomplishments that we're proud of

Successfully developed a PDF analyzer that simplifies complex medical results for patients.

Created an AI-powered chatbot that can answer questions about symptoms, medical jargon, and test results.

Integrated machine learning recommendations for patient care, offering actionable insights and suggestions.

Built a secure, full-stack application that manages sensitive healthcare data safely using Firebase.

Collaborated with a team to solve complex issues and effectively debug

Delivered a platform that addresses a real healthcare gap, helping patients understand their health more clearly.

What we learned

How to process and extract meaningful data from medical PDFs for actionable insights.

Best practices for combining frontend, backend, cloud storage, and AI systems into a single application.

The importance of clarity, safety, and accessibility in health technology.

How to implement secure authentication and data storage using Firebase for sensitive information.

Collaboration skills across different tech stacks and problem-solving approaches for complex healthcare needs.

What's next for Axxcess

Expand machine learning recommendations to cover more conditions and personalized care options.

Improve chatbot intelligence and conversational abilities to handle more nuanced medical questions.

Enhance user interface and accessibility, ensuring all patients can easily navigate the platform.

Integrate direct physician audio/video communication features, allowing patients to share results and questions with their primary care providers.

Explore mobile-friendly or app versions to broaden access for users on the go.

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