CliniTrack

Inspiration

Clinical trials often suffer from high dropout rates, partly because participants feel overwhelmed or disconnected. This leads to a poor outcome for both the patients that may miss out on life-saving medicine, as well as the pharma companies that can often be set back months and millions of dollars. We wanted to create a more empathetic, user‐friendly approach—something that turns daily check‐ins into supportive, conversational experiences rather than burdensome tasks, while providing the powerful data you would expect from traditional techniques.

What it does

Clinitrack transforms static clinical trial forms into dynamic conversations. Patients chat with an AI assistant that asks tailored questions about their daily well‐being, collects relevant trial data, and provides real‐time engagement insights for clinical teams, and then transforms it into a data-friendly form for analysis. The goal is to keep patients feeling supported and engaged throughout the study, reducing the likelihood of dropout while enabling researchers to make progress in the field.

How we built it

Backend: Python with FastAPI, using Firebase for secure authentication and real‐time data storage. Frontend: Svelte for a responsive, lightweight user interface. AI Integration: A language model that dynamically generates prompts based on form inputs and conversation context. All of our code is in a public GitHub repository, with thorough documentation and a demo video showing Clinitrack in action.

Challenges we ran into

Creating a natural, real‐time conversation flow was a core challenge. We had to integrate the AI model with Firebase, and preserve a consistently empathetic tone. Balancing performance constraints with a user‐centered design approach required multiple iterations.

Accomplishments that we're proud of

Conversational Transformation: Turned traditional data formats into engaging dialogues, and back into traditional data formats Prompt Engineering: Achieving a friendly and concise experience much better than one-shot approaches.

What we learned

Building Clinitrack underscored the importance of user‐centered design—especially in healthcare, where clarity and compassion are crucial. We gained experience integrating diverse technologies (FastAPI, Firebase, and AI) under tight deadlines, and learned how iterative testing and feedback loops improve both functionality and usability.

What's next for CliniTrack

Personalized Follow‐Ups: Adapting the conversation flow based on each participant’s prior responses. Advanced Analytics: Using data points to predict patient behaviour for the benefit of trial progress Generalization of the Product: We think that this technology can be applied to other fields as well, as we feel that many customers may be dissatisfied with the traditional method of getting feedback.