Wednesday, October 28
11:00 a.m. – 12:00 p.m. CT

Artificial intelligence has become the most talked-about topic in healthcare, yet many supply chain professionals find themselves in conversations about AI without a clear understanding of what the technology actually does or where its capabilities truly stand. This session provides the foundational knowledge needed to navigate these conversations with confidence and understand where the industry is headed.

Beginning with a brief history, the session traces AI's evolution from Alan Turing's foundational concepts through expert systems, machine learning, deep learning, and today's large language models and autonomous agents. Understanding this progression helps contextualize where we are now and why certain capabilities emerged when they did. The session will unravel the differences between types of AI that are often conflated: What distinguishes machine learning from deep learning? How are large language models different from traditional automation? What does it actually mean when a solution is described as "agentic"? These distinctions matter because they determine what the technology can and cannot do.

From there, the session examines where AI adoption actually stands today. Drawing on recent industry research, healthcare has moved from digital laggard to AI leader in specific domains, with adoption rates exceeding those of finance and manufacturing in certain applications. But adoption is uneven. The session explores where AI is delivering proven, measurable value in healthcare today: ambient clinical documentation, automated coding, diagnostic imaging, drug discovery. It also examines where the technology is still maturing and where early implementations have fallen short of expectations.

The healthcare supply chain presents a unique opportunity. Supply chain operations generate structured, high-volume data that AI systems can leverage effectively. The session will highlight real examples of AI delivering results in healthcare operations, including publicly available case studies that demonstrate both successes and lessons learned. The goal is to give attendees an honest picture of the current state: what's working, what's promising but unproven, and what remains hype.

Perhaps most importantly, the session addresses a practical reality: supply chain leaders are increasingly expected to have informed opinions about AI. Whether in budget discussions, strategic planning, or conversations with clinical leadership, the ability to speak knowledgeably about AI capabilities and limitations has become a professional necessity. This session equips attendees with that knowledge, not as technical experts, but as informed leaders who understand the landscape.

The session is intentionally vendor-neutral. Rather than promoting specific solutions, the goal is to elevate the industry's collective AI literacy. The presenter draws on experience presenting this content at regional conferences, where attendees across career levels reported that the foundational context helped them better understand technology discussions in their own organizations.

Healthcare Provider Co-Presenter Justification:
This session provides vendor-neutral industry education on AI fundamentals and current state rather than a specific implementation case study. The presenter has been a member of AHRMM's Education and Conference Education Committees with multiple publications in AHRMM's Supply Chain Strategies & Solutions on AI topics, and has presented this content at regional AHRMM conferences with strong attendee feedback across career levels. The session draws on cross-industry research and publicly available healthcare examples without promoting any specific vendor solution.

The session will open with 2-3 polling questions to gauge audience familiarity with AI concepts and their current challenges in AI-related discussions. This real-time feedback will help calibrate the depth of explanation during the presentation. The session will include 5-7 minutes of Q&A integrated throughout rather than held until the end, allowing attendees to ask clarifying questions as concepts build on each other.

Learning Objectives:

• Explain the differences between machine learning, deep learning, large language models and AI agents.
• Describe where AI is delivering proven value in healthcare versus areas still maturing.
• Discuss AI capabilities and limitations with confidence in strategic and operational conversations.

Takeaway Tool: Attendees will receive an "AI Concepts Quick Reference Guide," a one-page resource that includes plain-language definitions of key AI terminology (machine learning, deep learning, neural networks, large language models, agents), a visual timeline of AI evolution, and a summary of current healthcare AI adoption by application area. The guide is designed as a reference tool for future conversations and strategic discussions.

Speaker: Reuben Philip, Director of Product, Clarium

Wednesday, October 28
1:00 p.m. – 2:00 p.m. CT

Healthcare supply chains are under pressure to reduce waste, improve resiliency, and maintain patient safety. This session explores how circular economy principles - such as product recovery, recycling, and vendor partnerships - can transform waste into value, reduce operational risk, and strengthen supply chain continuity while supporting workforce efficiency.

Learning Objectives:

• Explain how circular economy principles reduce waste and strengthen supply chain resiliency in healthcare.
• Identify strategies for collaborating with suppliers to implement product recovery and recycling programs.
• Engage suppliers effectively by asking targeted sustainability questions and aligning on shared goals for operational improvement.

Takeaway Tool: Attendees will receive a Supplier Engagement Checklist designed to guide conversations, identify key questions, and align on mutual goals for implementing circular supply chain practices.

Speaker: Mellissa Nguyen, Senior Manager, Sustainability, Ambu

Thursday, October 29
11:00 a.m. – 12:00 p.m. CT

Managing inventory levels of physician preference items, like medical implants, can be complicated due to organizational politics, clinical considerations, and vendor involvement, but a leading academic health system took a unique approach grounded in advanced analytics and process improvement to achieve significant inventory reductions and multi-million dollar financial improvement. The initiative aimed to right-size orthopedic total joint implant levels by bringing shelf stock in line with clinical consumption patterns. The health system was able to use innovative statistical models to challenge PAR levels, identify excess inventory, lower holding costs, manage burn-down schedules, and negotiate credit / consignment agreements with vendors.

Learning Objectives:

• Learn how to convert physicians from skeptics into champions on inventory decisions related to physician preference items like implants. Go beyond basic system inventory reports to find insights and rationale for right-sizing decisions.
• Identify a series of analytical methodologies to size excess inventory levels of physician preference items.
• Select a strategic inventory management approach to reduce excess inventory levels through burn-down monitoring or negotiated solutions with vendors.
• Evaluate complementary process improvement changes to strengthen inventory controls, instill vendor accountability, and generate cross-functional support for revised holding levels.

Takeaway tool: PDF of a case study detailing the organizational approach to physician preference item inventory right-sizing with methodology and tactical suggestions. The case study detail will have sample analytical outputs, process improvement tips, change management advice, and organizational tactics to sustain progress including governance and reporting.

Speaker: Brad Helfand, Program Director, Healthcare, HAVI & Gerald Tisdale

Thursday, October 29
1:00 p.m. – 2:00 p.m. CT

As health systems face mounting financial pressures and operational complexity, the ability to leverage data effectively has never been more critical. Contract digitization—the process of transforming service and supply agreements into actionable, structured data—offers a powerful solution to optimize performance across the enterprise. By digitizing contracts from PDFs to actionable assets, organizations can unlock hidden value, reduce waste, and improve decision-making in ways that directly impact cost of care and operational efficiency.

Learn how digitization transforms static contracts into dynamic assets that drive savings, enhance operational performance, and support strategic sourcing initiatives. Real-world examples will illustrate how health systems can implement these practices to achieve sustainable financial and clinical outcomes.

Learning Objectives:

• Improve liquidity through better discount capture, pay term alignment, and payment type strategies.
• Maximize rebate revenue by aligning spend patterns with vendor incentives.
• Balance cost and clinical outcomes by leveraging data-driven sourcing strategies.
• Standardize critical clauses across agreements to reduce risk and improve compliance.
• Identify and recover funds lost through incomplete processes or control gaps.

Speakers:

• Matthew Mentel, President TAG Inc
• Rodney Erb, Vice President, Clinical Data Integration & Analytics, TAG
• Bradley Wright, Senior Solutions Consultant, TAG