AI: Opportunities, threats meet, tease healthcare supply chain

Dec. 27, 2023
Does haste, hype and demand signal actual or superficial benefits?

To adapt a refrain from the cartoon philosopher Homer Simpson, “AI – the cause of and solution to ALL of supply chain’s problems.” 

For some (e.g., entrepreneurs, innovators and opportunists, etc.), AI represents shortcuts and workarounds to be more efficient at work and either enjoy more recreational time or focus on other expedient tasks courtesy of the additional time.

For others (e.g., conspiracy theorists, cynics and skeptics, etc.), artificial intelligence (AI) calls to mind robots besmirching humans through deep fake audio, video and text or even replacing humans for jobs and revenue and taking over the world. 

Still for others (e.g., forward-thinking dreamers and open-minded schemers), AI represents the first-generation leap to Star Trek-like automated decision support with automatic universal translators (no need to take language courses in high school and college to graduate!) that should accurately second-guess even the second-guessers on a global scale.

Recently, the U.S. Food and Drug Administration jumped into the frenzy by creating a new Digital Health Advisory Committee “to explore the complex, scientific and technical issues related to digital health technologies, such as artificial intelligence.”

At least one study posits that AI likely will reach a half-a-trillion-dollar industry by 2027, giving additional meaning to “generative” AI. That’s within three years and represents a faster market stickiness (in terms of prospective adoption and implementation) and predictive revenue trajectory than the internet and online exchanges, internet-of-things (IoT) and blockchain achieved during their hype cycles.

But beyond the hype, pomp and circumstance, does AI amount to anything more than a decision-support tool that requires training for the end user as well as the application itself? Is it a generative relative of blockchain? A veritable electronic Swiss Army Knife for healthcare supply chain? A “shadow” database/virtual professional assistant not unlike Amazon’s Alexa, Apple’s Siri, Google’s Assistant with Bard or Microsoft’s Cortana?

Healthcare Purchasing News reached out to numerous corporate executives on the supplier and provider segments of the industry to gauge their anticipation, application and prognostication of AI as it relates to supply chain operations. The wide-ranging exploration spanned the promise of process effectiveness and efficiency to the precariousness of potential abuse, control and cybersecurity dangers.

Optimists foresee much promise; pessimists highlight areas of concern that if left unchecked could have considerable repercussions. Overall, optimism, which is shared here and on HPN Online, seems to surpass pessimism, which can be found on HPN Online.

Several experts salute the promise that AI brings to supply chain, particularly around data collection and number crunching.

“Artificial intelligence will be able to make use of large amounts of data to make accurate predictions around usage and supply availability,” said Jack Koczela, director, Sourcing & Transformation, Supply Chain, Froedtert Health. “It can also comb through data from different sources, such as clinical systems and business systems, to create insights that we have not been able to process at scale in the past. For me, this is one of the most exciting applications of artificial intelligence. As we move into a clinically integrated supply chain, we have many small, manual models that indicate we could see significant improvement if we could just get through all the data that is available at our fingertips.”

Archie Mayani, chief product officer, GHX, marvels at the past decade of development and growth of interest in AI, particularly for supply chain applications.

“Ten years ago, the healthcare industry was just beginning to talk about AI,” she recalled. “Now, it is beginning to transform healthcare and, more specifically, the supply chain. As the industry continues to use generative AI to sort and manage the influx of data, supply chain management processes will become more predictive, effective and efficient. If used correctly, AI has the potential to refocus healthcare teams on higher-value work such as supply chain resiliency and aid in future crisis mitigation, waste reduction, and cost-containment efforts that will help hospitals reinvest funds into patient care. In the era of smart supply chains, AI is the prescription for optimizing, not just organizing.”

But another supply chain expert offers a profound observation seasoned with a pinch of irony that suits today’s workforce.

“AI can help us make better use of scarce labor and rid ourselves of repetitive low cognitive work,” noted Joe Dudas, division chair, Supply Chain Innovation and Strategy, Mayo Clinic. “It can change our jobs to be more human.”

Accentuate the obvious

The mere notion of AI motivates many experts to list a bevy of obvious applications that can benefit supply chain operations. A dozen experts share their insights with HPN that at times converge on common themes.

“Proper inventory management is a core competency for efficient supply chain management and an area where AI applications will continue to emerge for the foreseeable future,” assured Vasco Kollokian, director, Innovation and AI, Tecsys. “By analyzing historical supplies consumption data and using machine learning for demand forecasting, AI can predict accurate levels of inventory to ensure optimal stock levels.

“Once optimal levels have been established for a particular healthcare provider network, maintaining suitable levels of inventory through location-specific replenishment becomes another clear application for AI,” he continued. “Leveraging dynamic real-time inventory visibility through IoT-based technologies, AI-driven processes can optimize the minimum and maximum inventory counts and automatically replenish to match various hospitals’ consumption-driven needs for specific locations, such as operating rooms, catheter labs, nursing floors and in-hospital pharmacies.”

Kollokian further believes that AI will interface or integrate with materials management information systems (MMIS), enterprise resource planning (ERP) systems, item masters and chargemasters, regardless of brand. “ERPs typically have control over item masters,” he indicated. “If visibility into fine-grained location-specific consumption and replenishment information is provided by other, more specialized, vendors, then integration is often necessary.”

The root of such connectivity varies between built-in product programming, “bolt-on” software application or online download.

“Usually, integration APIs are provided by vendors to accommodate information exchange among disparate supply chain management solution systems,” Kollokian explained. “For example, order quantities in ERPs typically are at the ‘pallet’ level, yet Automated Dispensing Cabinets (ADC) replenish at ‘each’ or ‘box’ level. Such system disparity often results in fragmented information fabric, requiring a ‘bolt-on’ approach. Whether vendors agree to provide such integration (usually considered competitive) is an orthogonal matter.”

Demand planning rules

The global pandemic elevated predictive demand planning to the forefront of “must-have” capabilities on automated wish lists, and Kollokian agrees that it’s ripe for AI.

“While some demand spikes are quite apparent, like seasonal demand for flu vaccines, others are less so,” he indicated. “For example, states not mandating motorcycle helmets are likely to see increased head trauma cases resulting from motorcycle accidents, prompting the need for proactive prior stock-up. AI can detect epidemiological healthcare patterns for less apparent trends without reliance on tribal knowledge.”

John Freund, president and CEO, Jump Technologies, zeroes in on demand forecasting as the most obvious use. “AI algorithms can analyze historical sales data, patient demographics and other relevant factors to predict future demand more precisely than traditional methods,” he told HPN. “This can help healthcare providers optimize inventory levels, reducing costs and improving access to essential medications and supplies.”

Connectivity throughout multiple systems for supply chain is essential, according to Freund.

“The demand forecasting engine needs consumption information so the inventory management tool would be needed either from the ERP system or third-party system like JumpStock,” he indicated. “It also needs vendor performance information at the product level, so it needs receipt data either from the ERP system or third-party system. It needs census data, so it needs data from the EHR. There may also be a variety of other third-party data sets that are needed to do this properly. An AI-based demand forecasting engine will need data from a variety of sources.”

Expanding supply chain automation and optimization beyond current capabilities is another valuable contribution, Freund posits.

“AI can automate various tasks throughout the healthcare supply chain, from procurement and ordering to warehousing and distribution,” he said. “For example, AI-powered chatbots can handle routine customer inquiries, while robotic process automation (RPA) bots can automate repetitive tasks such as data entry and order processing. This can free up human workers to focus on more complex tasks, improve efficiency, and reduce errors.”

But AI doesn’t necessarily duplicate other pre-existing automated tasks, he insists.

“Today in many hospitals requisitions flow down to purchasing, who in turn place those orders with vendors, in some cases considering multiple sources for a given product,” Freund explained. “AI can automate that entire process, taking the human element out of it. Think of the number of calls to purchasing looking for order status. AI chatbots can analyze data on product defects and customer complaints to identify potential quality issues. Chatbots can [help] hospitals with regulatory requirements by tracking and reporting recall levels. There is a wide variety of ways that AI can work here.”

Arnold Chazal, CEO, VUEMED Inc., remains solidly behind AI as creating more accurate predictive models for supply re-ordering and replenishment and balance for inventory management. “AI can help ensure that proper orders with the adequate quantities are placed in time to support the clinical work regardless of the cyclical or seasonal variations and take into account the greater supply chain challenges such as backorders and recalls,” he noted. 

“AI can optimize even more dynamically the on-hand inventory to meet the needs of each individual clinician by avoiding stock-outs while at the same time avoiding over-stocking, Chazal continued. “AI can do this by learning to predict what each clinician will actually need and use 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 will definitely be helpful for addressing the case cart and preference cards disconnects with the clinical requirements once the patient is being treated, thus ensuring the preparation of a case cart that is more adapted to the physician’s practice and habits and the patient being treated. It will advance customized care to benefit both the patient and the clinicians.”

AI can enable supply demand forecasting for providers and suppliers alike, observes Tom Redding, senior managing director, Healthcare, St. Onge Co.

“Unfortunately, the healthcare supply chain primarily has a transactional relationship with their health system,” Redding lamented. “There is a significant potential to gain insights from transactional history to inform future purchasing decisions. Additionally, it may inform any contract negotiations with their suppliers to ensure they can meet the organization’s total demand requirements and the anticipated demand variation. Health systems are stuck in the allocation model, which drives them to seek other product options during peak periods.”

AI can assist supply chain operations within clinics, surgery centers and other non-acute facilities, too, Redding stresses.

“With more ‘low-demand’ non-acute facilities coming online in the coming years, there’s a need to continually monitor demand and adjust the available units of measure,” he noted. “There are opportunities to right-size the required units of measure. Manufacturers spend a considerable amount of time trying to determine the packaging size to optimize their costs but that doesn’t mean it meets the needs of their customers.”

GHX’s Mayani sees AI improving demand planning and forecasting overall based on data access. “AI can analyze historical and current inventory utilization data to help a hospital more accurately predict the demand for various medical supplies and equipment,” she said. “With the help of AI, high-performing hospital supply chains have an opportunity to reduce supply expenses as well as ensure the right product is available at the right time for the right procedure to support better clinical outcomes.”

Yet AI makes sense for improving clinical inventory management at the point-of-use level as well, according to Mayani. “As healthcare decision-makers consider inventory levels and their related inventory systems, inaccuracy at the point-of-use can create significant operational hurdles when trying to either reduce costs or maintain high service levels. AI models can help drive supply capture at the point-of-use and automate and streamline these workflows with seamless and interoperable data with electronic medical records.”

St. Onge’s Redding foresees AI driving inventory management optimization. “In most cases today, the process to manage clinical inventories is still a predominately manual and an arduous process,” he noted. “AI can provide a real-time monitoring and provide adjustments to how much inventory is needed and make decisions on when to order supplies to eliminate guess work.”

If anything, AI can help the healthcare industry speed up demand planning communications, according to Josh Wolfe, senior vice president, Inventory Management, Medline. “In an industry where data sharing and integration between trading partners has lagged, AI could help provide more accurate demand signals, resulting in fewer supply disruptions,” he noted.

Bruce Lieberthal, vice president and Chief Innovation Officer, Henry Schein, agrees on demand forecasting’s inherent benefits. “AI can be used to consider the myriad of factors affecting demand to help create forecasting models that are more accurate and cost-effective,” he said. “This is important so that healthcare companies can reduce stockouts or over-ordering.”

Maintaining equipment

Predictive maintenance for medical equipment is another function that AI could manage, according to JumpTech’s Freund. “Medical equipment is expensive and critical for patient care,” he indicated. “AI-powered predictive maintenance systems can analyze sensor data from medical devices to identify potential malfunctions before they occur. This proactive approach can help prevent costly downtime, extend the lifespan of equipment, and ensure that critical devices are always available for patient care.”

This extends a step further than the real-time maintenance programs offered by several original equipment manufacturers that automatically alert the OEM when something goes wrong onsite at the provider facility to minimize operational downtime.

“AI algorithms can go deeper than purely maintenance data,” Freund assured. “For example, AI could study sensor data from machines used to coat the joints with a biocompatible material. The algorithms can detect defects in the coating, such as air bubbles or uneven thickness. AI can be used to optimize the coating process by adjusting the machines' speed, improving the joint's quality or reducing recalls.”

For James Kollai, senior director, Client Solutions, PartsSource, AI reaches into the parts and inventory management aspects of equipment maintenance. “AI could optimize inventory management by predicting which parts are likely to fail and when,” he said. “This would then help clinical engineering and healthcare technology management teams ensure that the right components are available when needed without excessive stockpiling.” 

PartsSource strives to integrate AI in critical decision support for dual and digital marketplace of providers and suppliers, according to Kollai. “One of the more obvious applications where we have used AI is through lead time predictions,” he noted. “In recent months, we have established a process leveraging our industry leading supply chain and transactional data sets along with a proprietary AI to predict lead times for a large portion of items purchased from our online marketplace. Approximately 90% or more vendors do not provide inventory feeds with accurate shipping information, which is crucial to customers who need parts to repair their medical equipment. Our AI can predict shipping times based on data from previous purchases by customers from the supplier to accurately fill in those inventory gaps.

“Additionally, PartsSource uses AI to help inform our Guaranteed Stock offering for our customers,” Kollai added. “Our teams are currently working on integrating AI for forward stocking, also called local stocking, options to minimize equipment downtime.”

Accentuate the alternatives

In addition to demand planning, AI can help manage clinically acceptable alternative products and sourcing, which attained a fever pitch during the COVID-19 pandemic.

“Given the new normal of supply disruptions, the ability to efficiently and accurately recommend clinically viable alternate products is a necessity in today’s healthcare supply chain,” indicated Medline’s Wolfe. “AI provides some exciting opportunities to meet that need.”

GHX’s Mayani contends AI can compile a list of substitutes to help avoid backorders and shortages, effectively serving as an extension of demand planning. “AI has the ability to offer more efficient and effective ways to manage supply chain disruptions and shortages,” she indicated. “Machine learning (ML) and AI algorithms can easily analyze historical data and market trends to predict potential backorders and make recommendations on product alternatives and substitutes based on evidence-based clinical outcomes. This not only helps limit overstocking and reduce waste, but also helps drive value-based care upstream and across the continuum of care.”

Carl Natenstedt, CEO, Z5 Inventory, concurs. “AI can be used to automate the ‘alternate sourcing scurry’ that occurs during backorders and product shortages,” he said. “AI can learn from past product shortage resolutions to mimic the human sourcing function and identify alternate sources of the same or similar product and place emergency orders.”

Evaluating supplier service

AI can assist in making considerable progress with supplier performance measures and programs, St. Onge’s Redding suggests. “Health systems will gravitate to AI to inform their decision-making process on which suppliers will get their business in the future,” he said. “Health systems can’t simply look at the cost of the product only. They will need to take into consideration the total cost of the relationship. If the health system takes into the consideration all their internal time and effort to resolve issues with a supplier, substituting supplies from a different supplier potentially at a higher cost, expediting freight costs and many other factors, the supplier relationship may not be worth all the effort.”

Product access and availability are the triggers, Redding insists.

“Every health system will share without pause that product availability is their biggest challenge daily; it was a challenge prior to the pandemic, and it has accelerated since the pandemic,” he noted. “Health systems will need leverage AI to continually monitor supplier performance and gain insights into the health of their suppliers. Creating a reliable, efficient and resilient supply chain starts with collaboratively sharing data/information between the health system and suppliers to ensure there is end-to-end visibility and each party can inform their internal decisions. Too often, we see reluctance to share data/information because there is concern that the other party may take advantage of the situation, either from a cost savings point of view and/or shifting away from the supplier because of lack of internal performance.”

Homing in on the granular

Demand management and monitoring warehouse performance top the list for George S. Godfrey, chief supply chain officer and corporate vice president, Financial Shared Services, Baptist Health South Florida, but he takes a cue from Amazon Prime, FedEx and UPS and points to delivery calendars for inbound and outbound shipments to manage “real-time scheduling that adjusts to traffic delays, urgency of goods, etc.”

Z5 Inventory’s Natenstedt sees benefits to automated and constant PAR area optimization and physician preference card management and maintenance from AI deployment, too.

“AI can learn from each replenishment assessment the consumption patterns of each specific PAR area and apply intelligent adjustments on a near-daily basis to streamline and reduce investment in inventory,” he indicated. “AI can learn from past case records and post-case waste and returned product assessments to constantly update and adjust the items on preference cards and their classifications to reduce product waste in the OR.”

Editor’s Note: For additional exclusive coverage of AI’s not-so-obvious uses, potential dangers and specific areas where AI is and will make clinical, financial and operational inroads relating to supply chain, visit HPN Online. Back in November 2023, HPN conducted an AI Virtual Forum, “Can AI Ease Current Healthcare Burdens?” To view it, click here: