Our inspiration for the hackathon is the current COVID-19 pandemic causing surrounding infrastructure problems in hospitals. For example, ventilators and other supplies like masks are in a JIT (just-in-time) supply chain where hospitals have to anticipate demand and not stockpile. During outbreaks like this, the supply chain is suddenly hit with a huge demand for PPE and other equipment. Our web platform aims to create a central database of designs and information that doctors can leverage to obtain the equipment they need. In this way, we hope to connect the healthcare industry to the common people in a collaborative effort to save lives across the globe.
What it does
Our platform aims to alleviate this issue by creating an extensible environment that is specifically designed to help design and fabricate 3D printed/CAD models for essential medical devices and PPE. On our platform, doctors can request components to be designed or fabricated, in addition to having access to a repository of crowd-sourced designs. Fabricators are able to upload designs and collaborate with other engineers around the world to meet medical needs, as well as print existing designs for local hospitals that need supplies.
This platform is extensible as well to other components and manufacturers later on for other non 3D printable or easily manufactured supplies like N95 face masks, where quantities and supplies are documented. The current issue is the lack of centralized effort in resources and the lack of community input in alleviating the situation. Our platform aims to tackle that.
How we built it
We build the platform using a tech stack including a Pyramid server with an SQL database that interfaced with a Jinja template system.
What's next for Med3D
On a large scale, we plan on integrating predictive analytics into Med3D to elastically anticipate demand, as well as creating a heatmap of cases and supplies currently available at hospital locations around the world. On a slightly smaller scale, we plan on implementing an adaptive rating system for fabricators that incorporates machine learning algorithms as our user-base grows, ensuring that doctors will be able to trust the products they receive. In addition, we plan on implementing a system of tagging design and fabrication requests by difficulty, which will help people who are new to the process learn and spread awareness of the potential for saving millions of lives with such simple concepts as communication and collaboration.