HCMC On-Demand Spring Symposium 2026

Description

Fee: $29
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Focused on the operational realities faced by hospital capacity teams, this symposium examines how systems translate forecasts into real-time action. Through a series of forward-looking presentations, attendees will examine emerging strategies for surge planning, real-time capacity management, and the evolving role of AI in supporting demand forecasting and patient flow.

Sessions Include:

Effective Surge Planning: University of Virginia Health System
Build flexible, scalable capacity before a crisis event. Effective surge planning and anticipation relies on defined triggers, adaptable care spaces, coordinated resource deployment, and real-time situational awareness. Surge plans should include strategies for staffing management and resilience, maintenance of essential services, and rapid restoration of normal operations quickly once the environment stabilizes.

Learning Objectives:

• Identify and apply key surge triggers, adaptable care-space strategies, and resource deployment models to effectively scale capacity before and during crisis events.
• Develop staffing management and resilience approaches that support workforce sustainability while maintaining essential services under surge conditions.
• Implement processes for real-time situational awareness and rapid operational recovery, enabling organizations to stabilize quickly and return to normal operations after a crisis.

Intelligent Capacity Management: How AI is Reshaping Hospital Operations
As hospitals face growing complexity and demand volatility, traditional approaches to capacity management are no longer enough. AI is transforming hospital operations by unifying data across clinical, operational, and workforce systems to provide real-time visibility into capacity. This session will explore how predictive and prescriptive analytics help leaders anticipate bottlenecks, optimize resources, and take proactive action. Attendees will gain a clear view of how AI can strengthen throughput and improve access to care.

Learning Objectives:

• Understand how AI is transforming enterprise operational visibility. Explore how AI integrates disparate operational, clinical, and workforce data to create a dynamic, real-time view of capacity across units, service lines, and facilities.
• Examine how predictive analytics can anticipate capacity strain and patient flow bottlenecks. Learn how AI models forecast demand surges, discharge delays, transfer congestion, and staffing imbalances—enabling earlier intervention.
• Identify how AI-driven insights support proactive and prescriptive operational decision-making. Review how AI can move beyond dashboards to recommend prioritized actions that improve throughput, optimize resource utilization, and enhance access to care.

Center Spotlight: University of Michigan M2C2 – Designing a Command Center with Proven ROI
Michigan Medicine’s M2C2 model delivered dramatic operational gains, including a 33% reduction in adult inpatient bed wait times and 37% shorter ED waits, translating into throughput improvements equivalent to opening 13 new beds — and generating $19.5M in annual revenue. Beyond flow improvements, the command center contributed to an 8% reduction in adult length of stay, effectively creating 50 beds of new capacity without construction — a powerful example of ROI through operational redesign.

Learning Objectives:

• Examine how centralized or coordinated operational models can significantly reduce inpatient and emergency department wait times, leading to throughput gains equivalent to adding staffed bed capacity without construction.
• Assess the financial and operational impact of reducing adult length of stay, including how even modest LOS reductions can create substantial “virtual capacity” and yield meaningful revenue growth.
• Identify key design elements of high performing command center or flow coordination models that drive measurable ROI through improved patient flow, resource allocation, and real time operational decision making.