Proscribe

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

• Practitioner micro dashboard identifies high risk patients for individual practitioners

• 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

• 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.

Built With

• figma
• python
• react.js
• sql