# Project export: Proscribe

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## Project metadata

- Hackathon: TreeHacks 2024
- Tagline: Proscribe evaluates medication risk levels using comprehensive and practitioner-specific patient data to minimize medical errors.
- Devpost: https://devpost.com/software/proscribe
- GitHub: not linked
- Team: contributor stats unavailable

## Devpost submission (written by the team)

### Inspiration

On average, every single patient in the United States will experience 1 significant diagnostic error in their lifetime.¹ Every year: 👤 400,000 patients affected² 💸 $20 billion in costs³ 🪦 100,000 deaths⁴

### What it does

💡 ForeStall works to predict potential diagnosis errors through analyzing a patient’s risk level to a certain medication at both a macro and micro scale. Searchable macro dashboard identifies high risk patients across entire hospitals Searchable macro dashboard identifies high risk patients across entire hospitals Practitioner micro dashboard identifies high risk patients for individual practitioners Practitioner micro dashboard identifies high risk patients for individual practitioners Output patient risk level analysis by finding mappings of medication incompatibility using IntegratedML Output patient risk level analysis by finding mappings of medication incompatibility using IntegratedML Detailed patient profiles with thread of patient notes so that different practitioners can remain on the same page Detailed patient profiles with thread of patient notes so that different practitioners can remain on the same page Anonymous error reporting feature, addressing the stigma of not reporting medical errors due to fear of backlash Anonymous error reporting feature, addressing the stigma of not reporting medical errors due to fear of backlash

### What we learned

We are deeply committed to delving further into this project by utilizing advanced machine learning models to enhance the accuracy of our predictions and further reduce medical errors. Additionally, we aim to implement this service in actual hospitals to evaluate our technology through the gathering of real data and to assess its efficacy in making real-world medication predictions. Following this evaluation, we plan to refine the user interface and enhance the machine learning predictive capabilities. Ultimately, our goal is to make ForeStall available as an open-source tool for doctors and medical professionals, enabling them to access guidance and resources at no cost for making optimal medication decisions.

## README (from the GitHub repository)

No README available.

## Detected evidence (automated analysis)

No repository was indexed for this project. Claimed technologies below could not be checked against code.
- Python (language) — claimed on Devpost, not found in the code
- React (technology) — claimed on Devpost, not found in the code
- SQL (language) — claimed on Devpost, not found in the code

## Codebase structure

No repository index available.

## Key source files

No repository index available; no source files included.