AI In Healthcare Supply Chain for Predictive Analytics

The healthcare industry stands at a crossroads. With supply expenses accounting for approximately 10.5% of the average hospital’s budget and totaling $146.9 billion in 2023, according to the American Hospital Association, healthcare organizations face mounting pressure to optimize their operations without compromising patient care. Enter artificial intelligence and predictive analytics, technologies that are fundamentally changing how hospitals manage their supply chains, predict demand, and ensure that critical medical supplies reach patients exactly when needed.

This transformation is not a distant future possibility. It is happening right now across hospitals and health systems worldwide. From the Mayo Clinic deploying autonomous robotic warehouse fulfillment to the Cleveland Clinic implementing AI for document recognition and invoice management, leading healthcare institutions are already reaping the benefits of smarter, data-driven supply chain management.

In this comprehensive exploration, we will examine how AI in the healthcare supply chain is creating unprecedented opportunities for hospitals to reduce waste, cut costs, improve patient outcomes, and build resilience against disruptions that have historically plagued the industry.

Understanding the Healthcare Supply Chain Challenge

Before diving into AI solutions, it is essential to understand the complexity and scale of healthcare supply chain management. Unlike retail or manufacturing supply chains, healthcare supply chains must navigate unique challenges that make them extraordinarily difficult to optimize.

The healthcare supply chain encompasses everything from sourcing raw materials for pharmaceutical manufacturing to delivering medications and medical devices to patients at the point of care. This journey involves countless touchpoints, including drug manufacturers, medical device companies, distributors, group purchasing organizations, hospital warehouses, and clinical departments. Each step introduces potential for delay, error, or disruption.

According to data from Definitive Healthcare, U.S. hospitals reported over $60 billion in combined medical and surgical supply costs in 2024, averaging $16.5 million per hospital. From 2020 to 2025, these costs rose from $40 billion to $57 billion, reflecting an average annual increase of approximately 8.2%. This growth is driven by rising pharmaceutical costs, physician preference item spending, and ongoing challenges in medical supply chain management.

The financial implications extend beyond simple procurement costs. A study by Navigant found that hospitals are spending about $25.7 billion annually on supply chain products and related operations that could be considered unnecessary. For individual hospitals, this represents an average savings opportunity of $12.1 million, equivalent to the annual salaries of 165 registered nurses or the cost of 3,100 knee implants.

Perhaps more concerning than the financial waste is the impact on patient care. Supply chain disruptions can delay surgeries, interrupt medication schedules, and force clinical teams to scramble for alternatives. The COVID-19 pandemic brought these vulnerabilities into sharp focus, with shortages of personal protective equipment, ventilators, and basic supplies like IV fluids exposing the fragility of traditional supply chain approaches.

The Rise of AI in Healthcare Supply Chain Management

The global healthcare supply chain management market was valued at USD 3.24 billion in 2024 and is predicted to reach approximately USD 9.20 billion by 2034, growing at an 11.05% compound annual growth rate, according to research published by Towards Healthcare. This growth is driven largely by the adoption of advanced technologies like AI, IoT, and blockchain to improve effectiveness and transparency.

Artificial intelligence is particularly well-suited to healthcare supply chain challenges for several reasons. First, healthcare generates enormous amounts of data across electronic health records, procurement systems, inventory management platforms, and patient care delivery. AI excels at finding patterns in these vast datasets that would be impossible for humans to detect manually.

Second, healthcare demand is inherently variable and influenced by factors ranging from seasonal illness patterns to unexpected events like disease outbreaks or natural disasters. AI’s ability to incorporate multiple variables into predictive models makes it uniquely capable of anticipating these fluctuations.

Third, the stakes in healthcare are extraordinarily high. A stockout of a critical medication or surgical supply can have life or death consequences. AI’s capacity for continuous monitoring and real-time alerting provides a level of vigilance that human oversight simply cannot match at scale.

The AI in healthcare market itself is projected to reach USD 110.61 billion by 2030 from USD 21.66 billion in 2025, growing at a compound annual growth rate of 38.6%, according to MarketsandMarkets research. This explosive growth reflects the broad application of AI across clinical, operational, and administrative functions within healthcare organizations.

How Predictive Analytics Works in Healthcare Supply Chains

Predictive analytics in healthcare supply chains uses historical data, statistical algorithms, and machine learning technologies to forecast future events. Unlike traditional inventory management approaches that rely on static reorder points and safety stock calculations, predictive analytics creates dynamic, responsive systems that continuously learn and adapt.

The fundamental components of predictive supply chain analytics include data collection and integration, pattern recognition and modeling, demand forecasting, and automated decision support. Let us examine each of these elements in detail.

Data Collection and Integration

Effective predictive analytics begins with comprehensive data collection from multiple sources. In a healthcare setting, this includes historical consumption data showing how supplies have been used over time across different departments and patient populations. It also incorporates clinical scheduling information, as planned surgeries and procedures create predictable demand spikes for specific supplies.

Patient admission and discharge patterns provide crucial context, since the number and acuity of patients directly influence supply consumption. External factors such as seasonal illness patterns, local disease outbreaks, and even weather events that might affect patient volumes or delivery schedules must be incorporated as well.

Supplier and distribution data adds another layer, tracking lead times, reliability metrics, and inventory levels across the supply chain network. When all these data streams are integrated, they create a comprehensive picture of supply chain dynamics that no single source could provide alone.

Pattern Recognition and Modeling

Machine learning algorithms excel at finding patterns in complex datasets. In healthcare supply chains, these patterns might include correlations between certain diagnosis codes and the supplies used to treat those conditions, relationships between staffing levels and consumption rates, or seasonal variations in demand for specific product categories.

Advanced models can also identify early warning signals of potential disruptions. For example, subtle changes in supplier shipment patterns, unusual ordering behavior across the network, or emerging quality issues might all indicate problems before they become critical shortages.

Demand Forecasting

With patterns identified, AI systems can generate accurate forecasts of future demand. According to research from the Council of Supply Chain Management Professionals, AI-powered inventory management reduces medical supply waste by 30 to 40 percent while maintaining 99 percent availability rates.

These forecasts are not simply extrapolations of past trends. They incorporate multiple variables simultaneously, weighting their relative importance based on historical performance and continuously refining predictions as new data becomes available.

A recent industry study compared AI-based forecasting models with traditional methods and found that AI models achieved a 31 percent improvement in forecast accuracy. This improvement translates directly into better stock replenishment, reduced waste, and lower emergency procurement costs.

Automated Decision Support

The final component of predictive analytics is translating forecasts into actionable recommendations. Modern systems can automatically adjust par levels, trigger reorders at optimal times, suggest alternative products when primary supplies are unavailable, and alert staff to potential issues requiring human intervention.

This automation reduces the manual workload on supply chain teams while improving accuracy. When staff are freed from routine tasks like manual counting and reordering, they can focus on strategic activities like supplier relationship management, cost negotiations, and process improvement.

Real World Applications: AI Hospital Inventory Management in Action

The theoretical benefits of AI in healthcare supply chain management are compelling, but what do these technologies look like in practice? Several leading health systems have implemented AI-driven solutions with measurable results.

Mayo Clinic: Autonomous Operations and Advanced Analytics

Mayo Clinic has emerged as a leader in healthcare supply chain innovation. According to Joe Dudas, Mayo Clinic’s division chair of supply chain strategy, the organization has deployed autonomous delivery and robotic warehouse fulfillment, with robots handling order picking in their distribution operations.

Mayo Clinic is also advancing algorithms for auto-replenishment to improve accuracy and using AI to explore savings opportunities and understand the sustainability of those opportunities over contract terms based on demand patterns. The organization employs advanced analytics in high spending categories to gain precision insights into supply chain performance.

Reports indicate that after implementing AI forecasting, Mayo Clinic and similar organizations have seen reductions of up to 30 percent in inventory costs. The health system has incorporated autonomous robotic warehouse fulfillment, intelligent auto replenishment, and advanced spend analytics tools that have significantly enhanced operational efficiency and financial performance.

Cleveland Clinic: Document Recognition and Process Automation

Cleveland Clinic has taken a different but equally innovative approach to AI in supply chain management. According to Geoff Gates, Cleveland Clinic’s senior director of supply chain management, the organization has used AI to automate processes that previously required employees to click through multiple screens and enter data into 20 or more fields.

These automations save employees approximately 20 minutes per transaction, freeing staff to focus on higher-value activities. Cleveland Clinic has been using AI for document recognition and invoice management through its ERP inventory management system for four years.

The organization has also implemented AI tools for invoice parsing, automated data entry, and predictive supplier stock tracking, transforming how it manages financial and supply chain operations.

University of California, San Francisco Medical Center

UCSF Medical Center provides a compelling example of AI’s impact on specific performance metrics. Using AI-powered inventory management, the medical center reduced back-ordered items by 70 percent and saved more than $300,000 in a single year. This demonstrates how targeted AI implementation can address specific pain points while delivering a rapid return on investment.

Rush University Medical Center: Weighted Bin Systems and Predictive Alerts

Rush University Medical Center has implemented weighted bin systems in all high-volume areas, providing real-time visibility into supply levels. According to Jeremy Strong, Rush University Medical Center’s vice president of supply chain, when a nurse takes something out or puts something back, the system knows immediately.

This real-time data feeds into AI systems that review utilization patterns and predict potential shortages. A back order dashboard creates alerts when automatic supply refill levels across the system are low, inventory is low at the distribution center, or shipments from manufacturers are taking longer than anticipated. This enables the organization to anticipate problems a week or more in advance rather than reacting after shortages occur.

The Impact of AI on Hospital Inventory Management

AI hospital inventory management represents one of the most immediately impactful applications of artificial intelligence in healthcare operations. Traditional inventory management approaches suffer from several fundamental limitations that AI specifically addresses.

Manual counting and tracking are time-consuming and error-prone. Staff estimates of future needs are often based on intuition rather than data. Static par levels fail to account for changing demand patterns. Expiration tracking is difficult to maintain across thousands of items. And emergency orders, which are significantly more expensive than planned purchases, become necessary when these systems fail.

AI addresses each of these challenges through automation, prediction, and continuous optimization.

Reducing Waste from Expired Supplies

One of the most immediate benefits of AI inventory management is reducing waste from expired products. Hospitals carry thousands of items with varying shelf lives, from medications that expire within months to durable supplies that remain usable for years. Managing these expiration dates across multiple locations within a hospital system is extraordinarily complex.

AI systems track expiration dates automatically and can optimize usage to ensure that items with earlier expiration dates are used first. When items approach their expiration without being used, the system can flag them for redistribution to areas with higher demand or for other disposition.

Unique device tracking has cut expired inventory losses by up to 60 percent in a year at some organizations. This saves money and reduces the risk of inadvertent use of outdated products that could compromise patient safety.

Optimizing Stock Levels

Traditional inventory management uses static par levels, essentially fixed quantities that trigger reorders when inventory drops below a certain threshold. These levels are typically set based on historical averages and rarely updated to reflect changing conditions.

AI enables dynamic par-level management that adjusts automatically based on current and predicted demand. When a hospital is preparing for seasonal flu volume increases, the system proactively raises par levels for relevant supplies. When patient volumes decrease during holiday periods, it reduces stock to free up working capital.

Studies indicate that supply chain AI can achieve up to 35 percent improvement in inventory accuracy when systems are properly implemented. This precision ensures supplies are available when needed without tying up excessive capital in inventory that sits unused.

Eliminating Stockouts

Stockouts in healthcare are not merely inconvenient. They can delay patient care, require expensive emergency procurement, and force clinical staff to spend time searching for alternatives when they should be focused on patients.
AI systems can reduce stockouts by up to 95 percent, according to some implementations. This dramatic improvement comes from the combination of better demand forecasting, automated reordering, and early warning systems that identify potential shortages before they become critical.

When a stockout risk is identified, AI systems can automatically suggest alternatives, initiate procurement from backup suppliers, or alert staff to take proactive measures. This shift from reactive to proactive management fundamentally changes how hospitals handle supply availability.

Key Applications of AI in Healthcare Supply Chain Management

Application Area	Traditional Approach	AI Enhanced Approach	Documented Benefits
Demand Forecasting	Historical averages, manual adjustments	Machine learning models incorporating multiple variables	31% improvement in forecast accuracy
Inventory Tracking	Manual counts, barcode scanning	Computer vision, IoT sensors, real-time monitoring	Up to 35% improvement in inventory accuracy
Reorder Management	Static par levels, manual review	Dynamic thresholds with automated ordering	Up to 95% reduction in stockouts
Expiration Monitoring	Periodic audits, manual tracking	Continuous automated tracking with alerts	Up to 60% reduction in expired inventory losses
Supplier Management	Reactive monitoring, manual communication	Predictive risk assessment, automated alerts	30% reduction in supplier-related disruptions
Waste Reduction	Post hoc analysis, periodic reviews	Real-time monitoring, proactive redistribution	30 to 40% reduction in medical supply waste

Predictive Analytics for Drug Shortage Management

Drug shortages represent one of the most challenging aspects of healthcare supply chain management. According to the American Society of Health System Pharmacists, there were a record 323 active drug shortages reported in the first quarter of 2024, affecting everything from basic generic medications to life-saving cancer treatments.

The causes of drug shortages are complex and interconnected. Manufacturing issues, quality control problems, regulatory actions, raw material shortages, and business decisions by pharmaceutical companies all contribute. Traditional approaches to shortage management are inherently reactive, responding to shortages only after they occur.

AI is changing this paradigm by enabling predictive shortage identification and proactive mitigation strategies.

How AI Predicts Drug Shortages?

Predictive models for drug shortages analyze multiple data streams to identify patterns that precede shortage events. These include purchasing data across hospital networks showing unusual ordering patterns or supply constraints, supplier shipment data revealing manufacturing or distribution issues, regulatory information about FDA inspections, warning letters, or import alerts, and market intelligence about business decisions that might affect production.

Premier Inc., a healthcare improvement company with approximately 4,300 hospital and health system members, developed CognitiveRx, an AI-powered analytics tool that uses purchasing data from its thousands of members to predict shortages and their duration. The machine learning model scores individual drugs for vulnerability to going into shortage, evaluating parameters including historical performance, current supply patterns, and the status of equivalent or alternative drugs.

TraceLink’s Product Availability Intelligence platform leverages data associated with more than 35 billion unique products on its network to predict drug shortages up to 90 days in advance with up to 90 percent accuracy. This early warning capability gives hospitals and health systems time to secure alternative supplies, adjust treatment protocols, or work with suppliers to prevent the shortage from affecting patient care.

The global AI-driven drug shortage prediction platform market reached USD 1.42 billion in 2024 and is projected to expand at a compound annual growth rate of 20.8 percent through 2033, ultimately reaching an estimated USD 8.97 billion. This growth reflects the critical importance of predictive capabilities in managing drug availability.

Dynamic Redistribution and Alternative Sourcing

Beyond predicting shortages, AI enables more sophisticated responses when shortages do occur. Dynamic redistribution systems continuously monitor inventory levels across different locations and can identify surplus stock in one area that could be redirected to regions experiencing shortages.

This real-time rebalancing of inventory helps maintain consistent drug availability across health systems. When combined with AI recommendations for therapeutic alternatives, these systems ensure that patient care continues even when primary medications are unavailable.

Building Supply Chain Resilience Through AI

The COVID-19 pandemic exposed vulnerabilities in healthcare supply chains that had been developing for years. The just-in-time inventory model that dominated healthcare operations prioritized cost efficiency over resilience, leaving organizations without adequate buffers when demand surged and supply chains fractured.

Before the pandemic, many healthcare organizations relied on just-in-time models that kept inventory lean and costs low. According to GHX data, there has been a 31 percent increase in order delays compared to pre-COVID levels, reflecting ongoing fragility in healthcare supply chains. The pandemic demonstrated that while efficiency is important, resilience must be equally prioritized.

AI contributes to supply chain resilience in several ways that address the specific vulnerabilities exposed during recent disruptions.

Enhanced Visibility Across the Supply Chain

One of the fundamental challenges during the pandemic was a lack of visibility into inventory levels, supplier capabilities, and distribution network status. Organizations did not know what supplies they had, where they were located, or when replacements would arrive.

AI-powered systems provide this visibility by integrating data from multiple sources into unified dashboards. Real-time tracking of supplies at every stage of the supply chain ensures that decision makers have accurate information when responding to disruptions.

A 2023 GHX survey of more than 100 healthcare leaders found that nearly 70 percent of all hospitals and health systems are likely to have adopted a cloud-based approach to supply chain management by 2026, helping them enhance decision making, improve efficiency and agility, reduce costs, and streamline processes.

Predictive Risk Assessment

Rather than waiting for disruptions to occur, AI systems continuously assess risks across the supply chain network. This includes monitoring supplier financial health, tracking geopolitical events that might affect manufacturing or shipping, and identifying single points of failure in supply networks.

When risks are identified, organizations can take proactive measures such as qualifying alternative suppliers, building strategic stockpiles of critical items, or adjusting demand forecasts to account for potential disruptions.

Rapid Response Capabilities

When disruptions do occur, AI accelerates response time by automating analysis and recommendations. During the COVID-19 pandemic, AI models successfully predicted shortages in personal protective equipment and ventilators, enabling suppliers to adjust production and distribution proactively.

These same capabilities apply to future disruptions, whether from pandemics, natural disasters, geopolitical events, or other causes. Organizations with mature AI capabilities can identify problems faster, generate response options more quickly, and implement solutions more effectively than those relying on traditional approaches.

The Integration of AI with Emerging Technologies

AI in the healthcare supply chain does not operate in isolation. Its effectiveness is amplified when integrated with other emerging technologies that provide additional data sources, automation capabilities, or verification mechanisms.

Internet of Things and Sensor Technology

IoT sensors provide the real-time data that AI systems need to make accurate predictions and timely decisions. Temperature sensors ensure that cold chain integrity is maintained for pharmaceuticals and biologics. RFID tags enable automated tracking of supplies as they move through the distribution network. Weight sensors in storage bins detect consumption patterns without requiring manual counts.

The combination of IoT and AI creates autonomous inventory systems that can monitor, predict, and respond without human intervention for routine operations. Staff are freed to focus on exception handling and strategic activities while the system manages day-to-day operations.

Blockchain for Supply Chain Integrity

Blockchain technology provides an immutable record of transactions that can verify the authenticity and chain of custody for pharmaceutical products. When integrated with AI, blockchain can help identify counterfeit products, verify regulatory compliance, and ensure that supplies have been handled properly throughout their journey.

Research suggests that blockchain integration with AI is set to accelerate the drive for increasing transparency, traceability, and security in healthcare supply chains. Such integration could significantly reduce counterfeit products and enhance tracking of sensitive medical supplies.

Robotic Process Automation

Robotic process automation handles repetitive tasks that previously required human attention. In supply chain operations, this includes processing invoices, updating inventory records, generating purchase orders, and reconciling shipments against orders.

When RPA handles these administrative tasks, AI can focus on higher-level analysis and decision making. The combination creates supply chain operations that are simultaneously more efficient and more intelligent.

Comparison of Traditional vs. AI-Enabled Healthcare Supply Chain Operations

Operational Aspect	Traditional Operations	AI Enabled Operations	Performance Improvement
Inventory Counting	Weekly or monthly manual counts	Continuous automated monitoring	90% reduction in labor for counting
Demand Forecasting	Quarterly reviews based on averages	Real-time updates with multiple variables	31% improvement in accuracy
Order Processing	Manual review and approval	Automated with exception handling	20 minutes saved per transaction
Shortage Response	Reactive after the shortage occurs	Predictive 60 to 90 days in advance	Up to 90% shortage prediction accuracy
Expiration Management	Periodic audits	Continuous tracking with automatic alerts	60% reduction in expired inventory
Supplier Monitoring	Annual reviews	Continuous risk assessment	Early identification of 80% of supply issues
Cost Management	Post-period analysis	Real-time spend visibility	17.4% average supply expense reduction opportunity
Staff Utilization	67% time on disruption management	Automated disruption response	Refocus on strategic activities

Challenges and Considerations for AI Implementation

While the benefits of AI in healthcare supply chain are substantial, implementation is not without challenges. Organizations considering these technologies must address several important considerations to achieve success.

Data Quality and Integration

AI systems are only as good as the data they receive. Healthcare organizations often have data scattered across multiple systems that do not communicate effectively with each other. Electronic health records, enterprise resource planning systems, inventory management platforms, and purchasing systems may each contain pieces of the puzzle without providing a complete picture.

Before AI can deliver value, organizations must invest in data integration and quality improvement. This includes establishing data governance practices, cleaning historical data, and creating reliable data pipelines from source systems to AI platforms.

Change Management and Staff Training

AI systems change how supply chain teams work. Staff who have spent years developing expertise in manual processes may be resistant to automation that seems to diminish their role. Successful implementation requires careful change management that helps staff understand how AI will enhance rather than replace their contributions.

Training is equally important. Staff must understand how AI systems make decisions, when to trust automated recommendations, and how to override the system when human judgment is required. This balance between automation and human oversight is critical for both effectiveness and safety.

Regulatory Compliance

Healthcare is heavily regulated, and AI systems must operate within these constraints. This includes maintaining audit trails for supply chain decisions, ensuring that AI recommendations do not conflict with clinical protocols or regulatory requirements, and demonstrating that AI systems are properly validated and controlled.

Organizations must work closely with compliance and quality teams to ensure that AI implementations meet all applicable requirements while still delivering operational benefits.

Cost and Return on Investment

AI systems require significant investment in technology, integration, training, and ongoing operations. Organizations must carefully evaluate the expected return on investment and ensure that benefits justify costs.

The good news is that many AI implementations demonstrate rapid payback. One hospital case study projected $61.5 million in savings across two rollout phases, delivering a 4x return on investment from labor savings and smarter inventory management. However, results vary based on organizational size, baseline efficiency, and implementation approach.

The Future of AI in Healthcare Supply Chain

Looking ahead, AI capabilities in healthcare supply chain will continue to advance. Several trends are likely to shape the future of this technology.

Increased Autonomy

Current AI systems primarily provide recommendations that humans review and approve. Future systems will increasingly operate autonomously, making and executing decisions without human intervention for routine operations. Human oversight will focus on exception handling, strategic decisions, and system governance rather than day-to-day operations.

Expanded Data Sources

AI systems will incorporate increasingly diverse data sources to improve predictions. This might include social media sentiment analysis to predict disease outbreaks, satellite imagery to assess supplier facility conditions, or economic indicators to forecast demand patterns. The more data AI systems can access, the more accurate their predictions become.

Personalized Supply Chain Management

As AI becomes more sophisticated, supply chain management will become more personalized. Rather than managing supplies at the department level, systems will anticipate the needs of individual patients based on their treatment plans, adjusting inventory positioning to ensure supplies are available precisely when and where needed.

Ecosystem Integration

Healthcare supply chains will become more integrated across organizational boundaries. AI systems at hospitals, distributors, manufacturers, and regulators will share data and coordinate decisions to optimize the entire supply chain rather than individual nodes. This ecosystem approach will improve efficiency and resilience for all participants.

Getting Started: A Practical Approach to AI in Healthcare Supply Chain

For organizations considering AI implementation in their supply chains, a practical, phased approach typically yields the best results.

Start by assessing your current data foundation. Identify what systems contain supply chain data, how complete and accurate that data is, and what integration work would be required to create a unified data platform. This assessment provides the baseline for AI implementation.

Next, identify high-impact use cases where AI can deliver rapid value. Many organizations start with demand forecasting for high-volume, high-cost items where improved accuracy translates directly into savings. Others begin with expiration tracking for pharmaceuticals or automated reordering for consumable supplies.

Pilot implementations in focused areas before expanding system-wide. This allows organizations to learn what works in their specific environment, adjust approaches based on experience, and build internal expertise before tackling larger transformations.

Build internal capabilities alongside technology implementation. Staff who understand both supply chain operations and AI capabilities become invaluable resources for ongoing optimization and expansion. Invest in training and development to build this expertise internally.

Finally, plan for continuous improvement. AI systems get better as they accumulate data and experience. Organizations should establish processes for monitoring system performance, identifying opportunities for enhancement, and implementing ongoing improvements.

Conclusion

The transformation of healthcare supply chains through AI and predictive analytics represents one of the most significant opportunities for improving healthcare operations and patient outcomes. With supply expenses approaching $150 billion annually across U.S. hospitals and potential savings opportunities exceeding $25 billion, the financial imperative is clear.

But the benefits extend far beyond cost savings. When supplies are available when and where needed, patient care improves. When staff are freed from manual inventory management, they can focus on higher-value activities. When organizations can predict and prevent shortages rather than react to them, healthcare becomes more resilient.

The technologies enabling this transformation are maturing rapidly. AI systems that seemed futuristic just a few years ago are now deployed in leading health systems, delivering measurable results. The question for healthcare organizations is no longer whether to adopt AI in their supply chains, but how quickly they can implement these capabilities.

As we have seen through examples from Mayo Clinic, Cleveland Clinic, UCSF Medical Center, Rush University Medical Center, and others, the path forward is proven. Organizations that embrace AI in healthcare supply chain management are already gaining competitive advantages through reduced costs, improved service levels, and enhanced resilience.

The healthcare supply chain of the future will be intelligent, automated, and responsive. Organizations that begin building these capabilities today will be best positioned to deliver on healthcare’s fundamental mission of providing excellent care to every patient, every time.