Therapy Match

Inspiration

The inspiration for Therapy Match came from our own experiences with the mental healthcare system. We noticed that finding a therapist who was the right fit in terms of specialization, approach, and personality was a daunting and time-consuming task. We wanted to create a solution that would make it easier for people to find the right therapist and ultimately improve their mental health outcomes.

What it does

Therapy Match is a web application that connects users with therapists based on their unique needs and preferences. Users input their location, and describe their situation through text or voice recording. Our application then analyzes their input to identify key points, and matches them with therapists who specialize in addressing those issues. The application ranks therapists by their relevance to the user's needs and proximity to their location, making it easy for users to find the most suitable therapist.

How we built it

We built Therapy Match using a combination of React for the frontend and Python (Flask) for the backend. We utilized the OpenAI GPT-3.5 model to analyze user input and identify key points related to their mental health needs. We then used SQLite to store therapist data and implemented custom algorithms to match users with therapists based on their specialties and proximity. To facilitate geocoding and distance calculations, we integrated OpenStreetMap API.

Challenges we ran into

We faced several challenges throughout the development process, including:

1. Ensuring accurate and relevant results when matching users with therapists.
2. Implementing an efficient algorithm for ranking therapists based on multiple criteria.

3. Handling edge cases and potential issues with user input and API limitations.

   Accomplishments that we're proud of

   We are proud of our ability to effectively utilize OpenAI API to analyze user input and identify key points that can be used for matching. We are also proud of creating a user-friendly interface that streamlines the process of finding a suitable therapist. Overall, we are excited about the potential impact Therapy Match could have on the mental health community by simplifying the search for a compatible therapist.

   What we learned

   Throughout the development of Therapy Match, we learned:

4. How to effectively utilize GPT-4 for natural language processing tasks.

5. The importance of considering various factors when designing an algorithm to match users with therapists.

6. How to manage and process user input to ensure accurate and useful results.

What's next for Therapy Match

We have several ideas for future improvements and features for Therapy Match, including:

Expanding the database of therapists to include more locations and specialties. Allowing users to filter results based on insurance, availability, and other preferences. Incorporating user reviews and ratings to further refine therapist recommendations. Developing a mobile app to make the service more accessible on various devices.

Built With

• azure
• flask
• openstreetmap
• react
• sqlite