Could AI fix medical supply chain woes?

Using artificial intelligence (AI) in the medical supply chain is a hot topic. But is AI just wishful thinking, or can it really fix supply chain woes— especially those that emerged during the pandemic?

Hospitals were largely aware that utilizing tools such as AI can enhance sourcing, reduce costs and boost efficiency. A 2019 Syft survey of 100 healthcare supply chain leaders and hospital executives found that most considered supply chain management a priority; more than half said it could increase margins from 1% to 3%. 

Then the pandemic hit, and many hospitals experienced significant interruptions to their supply chains. The need became real, really quickly. 

As we emerge from the pandemic, many hospitals have not adopted AI, according to Michael Schiller, Senior Director of Supply Chain at the American Hospital Association. 

“AI is not in use, broadly, across the health care supply chain field," he said. A handful of health systems are using it to power their data analytics, but there's more discussion and anticipation surrounding the perceived benefits rather than adoption.

AI for medical supply chains 

Predictive and prescriptive analytics used in AI can improve patient Contently 1 experience and staff workflows. It forms more robust, transparent and dependable supply chains by delivering automation and decision support. The predictive analytics can forecast what supplies will be needed and when. It can manage logistics, show product cost comparisons, and handle invoices and payments. 

Pete Bennett, Senior Vice President of Global Planning for Global Products and Supply Chain at Cardinal Health, said AI can eliminate redundant work performed by staff. 

“This allows humans to work on problem solving," Bennett explained. 

Barry Holleman, COO and Co-founder of MUUTAA, an AI supply chain solutions provider, said it also improves order management. 

“Which leads to a wide range of benefits from reduced panic buying and stock outs to greater efficiency and lower overall costs," Holleman explained. 

According to a 2021 Accenture report of nearly 400 health executives in multiple countries, a quarter were utilizing digital twins—a comparative, virtual model that lets organizations run simulations without risk to gauge operations and outcomes. And 66% of the respondents predicted their organizations would invest in the tool between 2021 and 2022.

Hospitals aren't the only health organizations that can use AI to ease supply chain issues. Just last month, Cardinal Health announced its program to help labs and health systems more accurately pre-order diagnostics ahead of the upcoming seasonal flu, strep and RSV season. 

Adopting AI for hospital supply chains 

Eugene Schneller, PhD, a Professor of Supply Chain Management at the Arizona State University W.P. Carey School of Business, agreed that hospitals are not quick to embrace AI for their supply chains. 

“By and large, AI has not impacted the medical supply chain in hospitals," he said. 

That's mostly because hospitals haven't brought together information systems to build the big data that AI and machine learning depend on, Schneller said. 

“One of the things we learned early on during COVID was the lack of Contently 2 data transparency both within and across systems," Schneller explained. “This lack of transparency is a big hinderance. One needs not just data, but the right data."

Schneller's research has shown that supply chain managers were so busy with operations and didn't have an opportunity to deploy new technologies. 

Tinglong Dai, PhD, a Professor of Operations Management and Business Analytics at Johns Hopkins University Carey Business School, believes health systems may be hesitant to use AI for the supply chain due to a lack of training—and the intense need to focus on more pressing tasks. 

“A more serious issue is that hospitals often find it difficult to recoup their investments in making their supply chains more resilient. So there is an incentive issue, too," Dai added. 

Schneller warns that “AI is an enabler—not a solution."

“AI is not a 'once and for all' process of dumping data into a repository. It is an iterative process to advance learning," Schneller added. 

Earlier this year, the U.S. Department of Health and Human Services announced that they invested in AI tools to manage public health supply chains. 

Exploring supply chain solutions 

AI is a robust solution for medical supply chains, but it's not the only one available, Schneller said. There are many approaches to better supply chain management, and a hospital's drive to use it can (and should) depend on the issue they want to address. It may work best, along with machine learning, in larger health systems, he noted. 

Dai said AI has only recently begun to impact the medical supply chain. But hospitals shouldn't use AI if they don't have reliable demand data or good supply chain visibility or they could spend a lot on something and not get the value in return. 

While AI has “formidable predictive power," building more resilient supply chains requires much more. 

“Understanding institutional contexts, ensuring smooth communications, and aligning incentives across different supply chain members are often Contently 3 just as important; AI cannot replace the human intelligence required to do these jobs well," Dai said. 

Moving forward with AI 

Holleman said it's simple to start using AI, and all hospitals should do it. 

Supply chain leaders should not be afraid to take advantage of the insights they can gain when they follow the data to address a business challenge their teams are facing, Holleman said. You need to identify data sources, collect data, and then anonymize and structure it in order to classify, interpret and operationalize the data. 

“Supply chain leaders should be aware that deploying AI effectively is a journey," Holleman said. This journey can and should start even if all the data sources are still scattered and not integrated." 

“The right AI service partner can pinpoint areas where opportunities are evident and start leveraging these to demonstrate tangible results," he said. 

Cardinal Health utilizes machine learning to cleanse data and improve access to information that provides better insights on potential supply disruptions. This can alert them to disruptions and delays sooner so an organization can deploy plans to mitigate situations. 

“In order to land inventory more accurately in the network we employ demand sensing technology that identifies trends based on consumption rates and triggers alerts for our deployment of inventory throughout the network," Bennett says. “Though these AI offerings do not always mitigate the supply disruption, it gives our planning organization more time to find solutions, and our customers early warning signals about potential disruptions on the horizon." 

Bennett says it makes sense to deploy AI a little bit at a time. When Cardinal Health conducted a gap analysis of their end-to-end supply chain, they pinpointed areas where AI could be leveraged quickly. 

“Being able to augment small pieces (like utilizing digital workers to obtain estimated time of arrivals or lead time variation information) immediately frees up time for planners to solve complexities within our network," Bennett said. 

“Our approach was to start small and scale, that has been successful Contently 4 throughout all of our AI solutioning within our Medical Segment business," he said. 

“You do not need to replace your entire supply chain management solution with an AI solution in order to be effective," he said. “I look at AI solutions as complimentary to our advanced planning systems and appreciate the flexibility and agility of AI solutions and the data scientists we employ."

Visit Cardinal Health’s Supply Chain Center to find more resources to further innovate your supply chain.

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