AI Demand Sensing, Integrated Data Systems Drive Early Wins in Pharmacy Shortage Forecasting

Valerie Bandy, PharmD, vice president of pharmacy solutions at Tecsys, explains that limited real-time visibility across pharmacy inventory and care settings remains one of the biggest constraints on hospital pharmacy and supply chain performance. According to a survey by her company, only 20% of healthcare leaders report having real-time, systemwide visibility into pharmacy inventory, and even among that group, roughly three-quarters say they are not fully prepared to manage major disruptions.

Bandy emphasizes that health systems cannot predict every disruption, but improved visibility can significantly reduce risk and improve response. The most common visibility gaps occur across multiple operational boundaries: between wholesalers and health systems; between centralized warehouses and pharmacy operations; between central pharmacies and individual hospitals or clinics; and even within hospitals themselves across inpatient, outpatient, and procedural areas. Fragmentation is especially common where different automation systems—such as automated dispensing cabinets—or standalone outpatient sites operate independently, sometimes ordering and managing inventory without central pharmacy oversight.

Because of these disconnects, many pharmacy teams operate with delayed, partial, or siloed data. Instead of integrated, real-time insight, they rely on manual reports, lagging indicators, and experience-based judgment. In today’s environment—where drug shortages, price volatility, and supply disruptions are constant—this lack of visibility forces organizations into reactive rather than proactive decision-making.

Operationally, that reactivity leads to several negative consequences. Pharmacies often discover shortages too late to respond efficiently, overorder to create buffer stock, and carry excess inventory to compensate for uncertainty. These practices increase costs and tie up resources unnecessarily. Bandy notes that without connected, cross-setting visibility, even well-intentioned preparedness efforts fall short. The result is a cycle of defensive purchasing, fragmented coordination, and elevated financial and operational strain across pharmacy supply chains.

Bandy also comments on how hospitals can better prepare for future shortages; the risks in relying on those aforementioned manual or fragmented tracking processes; and much more.

A transcript of her conversation with PC can be found below.

PC: When it comes to forecasting and predicting shortages, what types of AI and machine learning use cases are delivering the most value so far, and what barriers are slowing broader deployment?

Bandy: I think the most effective AI and machine learning use cases that we see in pharmacy supply chain really focus on the short term—demand sensing, shortage risk identification, exploration forecasting, really getting ahead of that inventory optimization—but it's only good when the data foundation is solid.

Those integrated systems are able to send that data back and forth so that there is a clear picture of what is going on within their system and their operations. Tecsys helps by assisting in that connectivity for pharmacy, supply chain, clinical, and financial data, and creates that real-time view that enables earlier signals so that there is better forecasting and smarter decisions before those disruptions escalate into something that's out of control and they're having to scramble and react to.

Instead of discovering a shortage at the point of care, that real-time usage and inventory trend allows the pharmacy teams to really see that risk earlier and adjust their sourcing, avoid those last-minute scrambles, substitutions, and delays that can jeopardize that patient care.

It’s not really that it's the technology itself—it’s that integration, and they're not lacking technology. Pharmacy has a ton of technology and has for a very long time. It's the challenge of standardizing that technology and integrating it to the pharmacy platforms, so that data moves across those four areas that are so important in the success of pharmacy, and this is really going to create value for the pharmacy and the health system, moving from those manual workflows to more of anticipating the shortages and rebalancing the inventory, reducing emergency purchasing, and then they become more resilient and more of a strategic driver for the overall performance, versus just always having to chase their tails and fix problems.