Four Takeaways about the Potential for AI in the Healthcare Supply Chain

There are legitimate concerns about the future of AI in our lives, and the debate rages on about how scared or excited we should be. But regardless of how we feel about AI and the ways it may affect us as humans, it is clear that, intrinsically, AI has the potential to become an essential tool that could dramatically shift the healthcare industry as we know it. 

AI has already shown value in clinical areas, such as helping to diagnose diseases from imaging and predict patient outcomes (here’s an interesting article about the potential and reality of AI in healthcare today). But AI also has the potential to help us optimize and streamline the healthcare supply chain, thereby saving hospitals money and improving patient care. 

Here are 4 ways that AI could make a difference in the healthcare supply chain:

Match clinical inventory with clinician practices and preferences

AI could help streamline and optimize on-hand inventory to meet the needs of each individual clinician by avoiding stock-outs, while at the same time getting rid of redundancies and avoiding over-stocking. It could create more accurate predictive models for the reordering of supplies by learning what each clinician actually needs and uses for each type of procedure, taking into account the specifics of anatomy and disease stage of patients to be treated, based on their medical records. This application of AI has the potential to be extremely helpful in predicting and ensuring the preparation of case carts and preference cards that are more adapted to the physician’s practice and habits, as well as the specific patient being treated. 

Automate supplies purchasing and stock replenishment

AI could ensure that product orders with the right quantities are placed in time for clinical needs and help manage backorders. It could automate the purchasing and replenishment of stock with greater visibility in the upstream supply chain all the way to manufacturers (and even their own sourcing channels), including logistics around the delivery of goods. This would enable the hospital supply chain to figure out alternate channels and suppliers based on specific requirements, including availability, price of goods, delivery mechanism, and cost.

Improve data and data management

AI could set and maintain data standards across internal and external systems to ensure better communication, as well as facilitate process integration upstream in the supply chain and downstream in the clinical areas with clinical data. AI could ensure that clean, accurate, and comprehensive data is tracked and disseminated according to global standards, such as UDI, in order to avoid gaps and inaccuracies in the healthcare supply chain. AI could also help audit data and remedy any discrepancies.

Link clinical outcomes to product usage

AI could also be used to predict and recommend combinations of medical products and implants that are closely tailored to each specific case (e.g., based on EMR and physician preferences). It could also help analyze patient outcomes and link them to their original diagnosis and treatment, including choice of the combination of implants and medical products, which would enable hospitals to determine which winning combination has performed best in terms of total cost to clinical outcome. This would impact purchasing patterns, par levels, and on-hand product variation, thereby streamlining orders and inventory.

Although there are many challenges ahead, the application of AI in healthcare is rapidly becoming more common and accepted, from analyzing data to improve clinical decision-making to interpreting diagnostic images and clinical notes. It also shows tremendous promise for the healthcare supply chain as a tool for automation, analysis, and standards compliance.