Government funded clinics and community pharmacies struggle to properly manage their inventories during outbreaks and other medical crisis, loosing large amounts of money, and risking civilian health. Studies show pharmacies loose millions of dollars every year just because of poor inventory management, and there are thousand of such community and government funded clinics in the United States alone. Prediction algorithms used by larger pharmacies like Walmart and Walgreen use million dollar technical solutions. PULSE offers a low cost solution to inventory management for community clinics.

The main objective of PULSE is to predict what drugs would experience an increase in demand, and to maintain low runtime expenses. To predict such rises in demand, we needed an external source of input beyond a clinic's past data. PULSE uses news due to its ease of access and its cost optimality. PULSE utilizes a proprietary pipeline that starts with a Google embeddinggemma model to turn the news into vector embeddings that are processed through a streamlined process starting with a Euclidean distance and regression model hybrid that identifies what disease is most frequently mentioned across hundreds of recent news articles, and then, the frequency scores and the original vector embeddings go through a main model that returns a final risk score, representing the risk posed by the disease. This signals are then tied to the pharmaceutical drug supply that each pharmacy carries, and converted into shortage warnings adapted to each pharmacies historical data.

The final tool is a simplistic website where pharmacies can sign up, upload information of how their past demand looked, and, the website first identifies what medical drugs that pharmacy holds, what signals are relevant to it, and then, returns the final predictions for what medical drugs would be high in demand in the coming days, and by what quantity.