SS11 – PharmaShield

Medication mistakes can cause catastrophic consequences. Pharmacists are crucial in ensuring medication safety by reviewing prescriptions, assessing potential interactions, and providing patients counseling. However, there is a chance that they make blunders. According to an article posted on HK01 in 2021, a pain patient with aspirin sensitivity was prescribed the same anti-inflammatory analgesic, causing discomfort and sensitivity. The Hospital Authority noted that healthcare workers did not associate the two drugs. Our AI program relied on an autoencoder model, which is an artificial neural network that is designed on how the human brain processes information. Also, it includes data mining techniques to analyze prescription records, electronic health records, and other healthcare data sources. By using this model, it could simplify pharmacy workflow, minimize administrative burden, and frees pharmacists to focus on patient care. To make the AI programme to indicate all kinds of drug-drug interactions, it is needed to label data from thousands of drugs and millions of different combinations of possible interactions. In summary, the optimization models consider factors like lead time, inventory costs and service levels to determine the optimal stocking levels for different drugs. Consequently, it can predict drug demand, optimize inventory management, and maintain adequate stock levels.