Model. Predict. Respond.

Our team has developed a model to predict how long hospital resources will last. Then, hospital distributors can prioritize hopsitals in their network based on projected days on hand

Problem: Hospitals are faced with the challenge to keep stocked inventory. Suppliers and distributors are unable to make accurate predictions in times of unstable demand of which hospital is in the most critical situation.
Solution: Our data combines many data sources starting at the county level. Then, we simulate inventory burnout using this data and other predictive models, and return a burnout schedule (including days on hand) to the user. In the future, distributors of a hospital network could use the projected days on hand to prioritize hospitals within the network.
The team: Sebastian Hollister (CS '21), Maximilian Hollister (BME '21), Akash Moozhayil (ME '21), Brandon D'arienzo (ME '21), Mike Adamo (BME '21)
The Team

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The Solution

Solution: Based on our simulated PPE burn rate per hospital, suppliers can more appropriately direct their supplies of PPE equipment to prioritize hospitals in need.