Medical Advocacy Assistant

Inspiration

Women can often be underestimated or misunderstood, especially regarding their health concerns, leaving them feeling lost. As three women in Computer Science, we decided to create a tool that empowers women to take control of their health and connect with a safe community that ensures their voices are heard.

What it does

The Medical Advocacy Assistant is a web-based platform designed to help women monitor their health and seek medical attention for concerns that are often overlooked or dismissed. The platform offers several features to support informed and confident healthcare advocacy. These include a symptom tracker to document symptoms and identify patterns over time, a personalized question generator that creates tailored questions for medical appointments based on reported symptoms, and a medical timeline to organize health history in one place.

Additionally, the platform provides a treatment tracker to monitor treatments and evaluate their effectiveness, advocacy talking points to help users communicate concerns clearly and assertively, and a medical note analyzer to help interpret and understand clinical notes. A community feature is also included, allowing users to connect with other women who may share similar experiences, insights, or advice.

How we built it

We built this app as a client-side Next.js app with TypeScript that runs in a browser. There are no backend servers or database, no API routes, and all data is stored in the browser's localStorage. This prioritizes privacy by keeping all medical data in the browser. The app uses a custom hook useLocalStorage that saves data to the browser's localStorage, persists across sessions, handles Date objects, and syncs between tabs. Features are self-contained components that use static template functions instead of AI with select questions/talking points based on appointment type. Export functionality generates print-ready HTML for PDFs and formats data as tab-separated values.

Challenges we ran into

While creating the medical advocacy assistant, we were faced with the challenge of trying to integrate the Google Gemini LLM into our tool using the Gemini API documentation. This was not successfully integrated because we were hitting the cap of the maximum allowed API calls included in the free trial.

Accomplishments that we're proud of

While building this project, we set clear milestones to ensure the platform included features we believed would be both helpful and necessary for women. It can often be difficult to relate to others as a woman, especially when it is hard to speak openly about personal health issues and concerns. We are proud to have created a solution that advocates for women and addresses a real-world problem in which medical concerns are frequently overlooked or not taken seriously.

As three computer science students, we had limited experience building a user-centered application. However, driven by our desire to challenge ourselves, we attended a UI/UX workshop to learn how to design a more effective and intuitive platform. As a result, we were able to create a product that we are incredibly proud of within the time allocated.

What we learned

We learned a great deal while creating this platform, as we were still becoming familiar with the process of building an application. The workshops were especially valuable in helping us find a starting point and establish a strong design direction. During the design phase, we brainstormed ways to make the platform visually appealing and user-friendly, while ensuring that women’s concerns were addressed inclusively and that the platform could support users regardless of economic status or external circumstances.

Throughout this process, we also learned how to collaborate effectively as a team, working together to collectively design and implement features that reflected our shared vision for the application.

What's next for Medical Advocacy Assistant

Next steps for Medical Advocacy Assistant would be to integrate Google Gemini AI into the system. This would grant the users the ability to have an interactive conversation with the LLM and answer questions the LLM asks to gain further insight on the user's medical situation. We also want to add a feature with Gemini AI that generates a report once the conversation has completed and requests the user to create an appointment with the best rated clinics in their local area. Once the user does create an appointment at their selected clinic/practitioner, the report would get sent to them to help provide the healthcare provider with more details about the patient's concerns.

Built With

• css
• javascript
• next.js
• node.js
• react
• tailwind
• typescript