## Inspiration

Our inspiration for developing the Hospital Inventory Manager stemmed from the pressing need for efficient healthcare resource management and improved patient outcomes, particularly in the context of organ donation and allocation.

## What it does

The Hospital Inventory Manager is a comprehensive platform designed to streamline various aspects of hospital resource management, including inventory tracking, scheduling, patient management, and organ matching. The system incorporates QR code functionality for quick access to vital information.

## How we built it

* **PostgreSQL**: Utilized as the database to store all our data.
* **Pandas**: Employed for data manipulation and analysis.
* **QR Code Generation Library**: Used for creating QR codes for inventory items and patient identification.
* **Git**: Implemented for version control and collaborative development.

## Challenges we ran into

We faced several challenges, including:

* Integrating multiple complex systems (inventory, scheduling, patient management) into a cohesive platform.
* Ensuring data privacy and security, particularly regarding sensitive patient information.
* Implementing an efficient organ matching algorithm.
* Creating a user-friendly interface capable of handling complex operations.
* Managing real-time updates across different sections of the application.

## Accomplishments that we're proud of

We take pride in several achievements:

* Developing a fully functional prototype that addresses various aspects of hospital resource management.
* Implementing QR code functionality for quick access to information.
* Creating a system that can potentially save lives by enhancing organ donation and allocation efficiency.
* Building a scalable solution adaptable for various healthcare settings.

## What we learned

Through this project, we gained valuable insights:

* The complexities of healthcare logistics and the critical need for efficient resource management in hospitals.
* The integration of multiple Python libraries and technologies to create a comprehensive web application.
* The significance of user experience design in healthcare applications.
* Techniques for securely handling sensitive data.
* The importance of iterative development and continuous testing in building robust software.

## What's next for CareCost

Moving forward, we plan to:

* Implement advanced predictive analytics for inventory management.
* Enhance the organ matching algorithm using machine learning techniques.
* Develop a mobile application for on-the-go access.
* Integrate with existing hospital information systems for seamless data exchange.
* Conduct pilot tests in real hospital environments to gather feedback and refine the system.
* Explore the use of blockchain for secure and transparent organ donation tracking.
* Expand the platform to include features for blood bank management and vaccine distribution.

We believe that with further development and refinement, the Hospital Inventory Manager has the potential to significantly enhance healthcare resource management and improve patient outcomes.
## Inspiration

Ever book a hospital appointment and then find out the cost later? Many people struggle with understanding the potential costs associated with various medical procedures. This tool aims to fill the gap by offering a reliable estimate of these costs.

## What it does

We want to provide transparent and accessible healthcare cost information. CareCost is designed to estimate the costs of different medical procedures based on user input, such as procedure type and location (ZIP code). It can give users a breakdown of potential costs, including lower and higher estimates, typical insurance costs, and additional considerations like complications or recovery times.

## How we built it

CareCost is built using Flask as the web framework, with a frontend likely composed of HTML templates such as templates/index.html for user interaction. The backend uses Python to process inputs and generates prompts tailored to specific procedures using methods defined in app.py. The application integrates with OpenAI's API to find cost estimates.

## Challenges we ran into

Developing a comprehensive healthcare cost estimator involves handling diverse data inputs, ensuring calculations are precise, and making sure the tool is intuitive for users. Additionally, integrating with external services like OpenAI's APIs presented challenges regarding versions and response handling.

## Accomplishments that we're proud of

I'm proud that this hack didn't take me that long to make.

## What we learned

Throughout the development of CareCost, I learned valuable lessons about healthcare pricing, web development (Flask, HTML/CSS), and API integrations.

## What's next for CareCost

Moving forward, CareCost could expand by adding more procedures, refining estimates with up-to-date data, and possibly incorporating user feedback mechanisms. Improvements in UI and further leveraging AI models for dynamic estimation are other potential areas for advancement.

I also want to explore additional data sources to increase the accuracy and coverage of cost information. Additionally, improving accessibility and mobile-friendliness would ensure broader user adoption and satisfaction.
## Inspiration

In a world where mental health and cyber security are becoming more and more important we've decided to develop and app that tries to interconnect the two issues.

## What it does

Split Mind help you keep track of various external and internal factors that affect you everyday life.

## How we built it

Using flutter, dart and the @platform.

## Challenges we ran into

Not enough time and inexperience with the technologies required.

## Accomplishments that we're proud of

Everything

## What we learned

To keep the scope small and separate concert better among the teammates.

## What's next for Split Mind

Depends on the placement at this hackathon