Anticipating back orders of supplies may not be the first thing that comes to mind when it comes to AI in healthcare, but while supply chain management may not be as sexy a topic as, say, the predictive use of genomics, it’s a key ingredient in the effort to drive down healthcare costs.
In a recent article at SupplyChainBrain, supply chain expert Karen Conway describes a number of potential uses for AI in healthcare management.
For example, she notes, as efforts increase “to redesign care pathways based on the needs of specific patient populations, there is an increasing need to match the right product to the right patient. AI can play an important role in understanding what works best on what kinds of patients, and (managers can) leverage this data for value analysis and sourcing, as well as making sure the right products are in the right place.”
Interestingly, Conway points out that “AI-enabled companies focused on patient flow are utilizing tools commonly deployed by third-party logistics companies, such as UPS, to chart the fastest ambulance routes to transport patients to the hospital or other care delivery sites.”
Picking up on that logic, she suggests the same “technologies could be used to assist healthcare supply-chain professionals as they grapple with the migration of care outside the acute care settings. AI can help determine the best transportation methods, frequency and routes to move both products and caregivers to the rapidly expanding number of locations where they will be needed, from home and retail clinics to urgent care and ambulatory surgery centers.”
As mentioned before, yet another use for AI in supply chain management could be to “anticipate backorders and stockouts, but also to help manufacturers gather data across their highly complex supply chains to better predict disruptions, take corrective action, and help their customers identify alternatives.”
Despite the potential for AI in supply chain management, Conway points out that the same potential pitfalls exist in this category as do in more clinical applications. Specifically, the need for effective data governance.
“The beauty of AI,” she says, “is that it can analyze large amounts of data to identify patterns and hidden correlations that would otherwise take humans considerably longer to decipher, if at all. . . . But despite the sophistication of the tool, the old adage — garbage in, garbage out — still applies.”
Consequently, supply chain managers who are thinking of launching an AI initiative need to make sure they have enough data, “and that the data adheres to well-defined data policies, standards, definitions and processes.”
Only once managers can have trust in the system, she cautions, should they move to applications of AI in supply chain management in which the system makes decisions and takes action without human intervention.