AI for Drug Shortages and Pharmacy Inventory

Most drug shortages start on the supply side. Common causes include manufacturing quality problems that shut down a production line, markets with only one or two makers, very low margins on older generic sterile injectables, and occasional demand surges. In 2023, for example, US shortages of cisplatin and carboplatin followed disruption at a major manufacturer. A pediatric respiratory illness surge coincided with amoxicillin suspension shortages in 2022 and 2023. Pharmacists follow the FDA's drug shortage database and the shortage lists maintained by ASHP (the American Society of Health-System Pharmacists). AI cannot make more drugs. What it can offer is earlier warning, better allocation and faster substitution. Demand forecasting sets reorder points and par levels. Shortage risk models combine signals such as the number of manufacturers, recalls, FDA inspection findings, wholesaler allocations and price trends to estimate which products may become scarce. A key misconception is that the right response to a warning is to buy as much as possible. Panic ordering spreads through the supply chain, and small demand changes become large swings in upstream orders, known as the bullwhip effect. That worsens shortages for other hospitals. Many health systems therefore have allocation and conservation policies, and a well-designed system recommends buying within those limits. Substitution needs care. Therapeutic interchange must follow protocols approved by the pharmacy and therapeutics committee. Substitutes can differ in concentration, route, stability or how they are handled, and those differences can cause dosing errors. Software can list approved options and do the conversion math, but a pharmacist checks it and communicates the change to prescribers and nurses. Another misconception is that shortage prediction can be precise. Shortages are rare and often sudden, so these models produce risk rankings, not firm dates.