Healthcare Supply Chain Management and AI

Better supply chain management can enable health systems to reduce their supply expenses by an average of 17.7% or $11 million annually per hospital, according to a new report.

The report, from Syft, a healthcare supply chain management company based in Stratford, Connecticut,  cites secondary research from a number of sources as well as a market survey conducted by Sage Growth Partners for Syft earlier this year. The survey of 100 healthcare C-suite and supply chain leaders queried healthcare leaders on their supply chain priorities, existing capabilities, and challenges; as well as what solutions they use to analyze and manage their supply chain costs.

“This is a serious motivator for healthcare leaders, who are turning to sophisticated technologies like artificial intelligence (AI) to help standardize processes and reduce these expenses. Machine learning, a type of AI where computers continually refine algorithms as additional information is captured, is proving to be especially valuable in supply chain applications after being used extensively in the areas of imaging and population health,” says Kishore Bala, Chief Technology Officer, Syft.

According to Bala, with growing downward pressure on revenues and the likelihood of supply chain costs surpassing labor costs by 2020, the need to reduce waste and optimize supplies throughout the enterprise and care continuum has never been more urgent.

“Ninety-eight percent of healthcare leaders we surveyed earlier this year say supply chain management is a moderate to high priority, so this is an extremely pressing issue,” he says. “The potential rewards for health systems that optimize their supply chain management are significant.”

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A McKinsey study referenced in the report found that using AI to enhance supply chain management could cut forecasting errors by 20% to 50%.

To take that a step further, more than half of the survey respondents said that SCM can increase hospital margins by 1% to 3%, and nearly one-third said it can increase margins by more than 3%. “For a hospital with $900 million annual revenue, that would translate to between $9 million and $27 million in savings that flow directly to the bottom line,” Bala says.

Machine learning

“Machine learning is truly poised to revolutionize the operating room and materials manager’s ability to plan for and deliver critical supplies at the right time and place, and at the right cost,” he says. “These AI algorithms have the ability to provide real-time, accurate cost variance analysis, and procedure/inventory demand intelligence. With this level of holistic analysis of usage and cost variance, health systems have visibility into opportunities to align clinical and business optimization goals across the entire system.”

However, Bala notes that ideal is not where the industry is now. “Without an automated supply chain management system, hospitals responding to events like product recalls need to use laborious manual processes to find and pull out these products,” he says. “With an automated system, recalled products can get automatically flagged so they’re prevented from being issued or received without requiring additional staff effort.

“Healthcare leaders also need to understand that commodity items, like as needles, labels, and surgical drapes, account for only about 18% of the supply spend in a typical hospital,” Bala says. “Meanwhile, provider preference items like as drug-eluting stents and implants can drive over half (56%) of that spend. Using a supply chain management platform with advanced analytics capabilities to focus on these high-cost preference items are critical to optimizing the supply chain.”