Healthcare is one of the most complex systems in the world. Patient needs shift by the hour, staff schedules fluctuate, and unit occupancy can swing from half-empty to overflowing in a single shift.
A decision in one area, such as opening a new observation unit, adjusting surgical schedules, or adding beds to a specialty ICU, can ripple across the entire hospital.
Traditional planning tools, often built on averages, can’t keep up. “Average” length of stay, “average” acuity, and “average” demand don’t reflect the reality that no two patients, days, or hospitals are the same.
That’s why GE HealthCare built the Digital Twin - an industrial-grade simulation engine designed specifically for hospitals and health systems.
Why Generic Models Fall Short
Discrete-event simulation has been used in industries like manufacturing and logistics for decades. But applying those models directly to healthcare is problematic. Unlike assembly lines, hospitals deal with:
- Variability
Patient arrivals fluctuate unpredictably, from seasonal pediatric surges to trauma cases. - Interdependence
The effects of a discharge delay on one floor can cascade to the ED, OR, and post-acute units. - Dynamic environments
Staffing, acuity, and bed availability change by the minute.
Generic models typically assume static or average values. In healthcare, this leads to misleading results and a false sense of confidence in plans that won’t hold up under real conditions.
The GE HealthCare Digital Twin was engineered to capture healthcare’s true statistical behavior, ensuring simulations mirror real-world dynamics.
What Makes It Different
The Digital Twin’s power lies in four attributes designed for healthcare:
- Speed
Hospitals can be modeled in months, not years. This allows leaders to run scenarios and make decisions while conditions are still relevant. - Modularity
Hospitals can begin modeling at either end of the spectrum: micro (a single ED, OR, or unit) or macro (an entire hospital or multi-hospital system). The model can then expand in either direction, layering insights from unit-level detail to system-wide dynamics. - Longevity
With data updates every 6–12 months, a Digital Twin can serve as a reusable planning tool for years, not just a single project. - Dynamic Simulation
Instead of relying on averages, the model learns the statistical behaviors of patients, staff, and resources so simulations are realistic and actionable.
A Six-Phase Methodology
Developing a Digital Twin is as much about governance and stakeholder alignment as it is about modeling. This data-driven, collaborative planning process is designed to deliver results in less than six months:
- Launch & Governance
Establish goals, form a steering committee, and set up communication and decision-making structures. - Assessment
Gather retrospective data, document workflows, and interview stakeholders to capture operational realities. - Scenario Framework
Define the “what if” questions, such as: Should we build a new tower or reallocate existing capacity? Should we change surgical schedules? - Iterative Modeling
Build and refine simulations, test multiple scenarios, and measure tradeoffs. - Recommendations
Present data-driven insights, showing leaders the impact of different paths. - Go-Forward Plan
Deliver a roadmap with priorities, accountability, and next steps.
This methodology transforms the Digital Twin from a one-off model into a decision-support system that organizations can rely on repeatedly.
Technology in Action
The best way to understand the Digital Twin is to see how it works in practice. Across hospitals and health systems, it has been applied to challenges ranging from seasonal surge planning to command center design, capital investment decisions, and surgical scheduling.
Each example highlights how simulation translates complexity into clarity and why a purpose-built model for healthcare makes the difference.
1. Preparing for Seasonal Surges
Pediatric hospitals face sharp seasonal spikes in respiratory illness. Using Digital Twin, Children’s Mercy Kansas City can forecast when surges will hit, what diagnoses are likely to dominate, and what resources will be needed. Instead of reacting when the ED is already overflowing, the hospital can preemptively open beds, onboard additional staff, and ensure the right resources are available.
“It’s important that we’re prepared for surges, and the Digital Twin has been remarkable in helping us do that,”
- Stephanie Meyer, Senior Vice President and Chief Nursing Officer.
2. Deciding on Expansion vs. Optimization
For some systems, the key question is whether to build new capacity or get more out of the current footprint. A Digital Twin can simulate both scenarios: adding a new tower vs. redistributing patient programs across existing units. Leaders can weigh costs, patient access, and staffing implications side by side, making multimillion-dollar investment decisions with clarity.
3. Optimizing Surgical Schedules
Operating rooms are the heartbeat of hospital activity. A poorly balanced surgical schedule can create bottlenecks in ICUs, PACUs, and inpatient units. With Digital Twin, hospitals can test alternative scheduling models, predicting downstream impacts and identifying the optimal balance between throughput and resource strain.
4. Redesigning Units and Department
Physical layouts can have a dramatic impact on patient flow and staff efficiency. Before construction begins, Digital Twin simulations allow leaders to test new footprints and workflows, projecting how different designs will affect care delivery and team performance.
5. Balancing Programs Across a Network
In multi-hospital systems, deciding where to place or consolidate clinical programs is a complex, system-wide challenge. A Digital Twin can model how shifting certain services — such as opening observation units or concentrating stroke care — would influence patient access, staffing requirements, and utilization across the network.
Why It Matters
Healthcare leaders don’t have the luxury of trial and error. A poorly informed decision can lead to wasted capital, staff burnout, and reduced patient access. The Digital Twin provides:
- A shared reality for diverse stakeholders so high-stakes decisions aren’t based on subjective and often conflicting viewpoints.
- Faster iteration so systems can test dozens of scenarios before acting.
- Evidence-based recommendations that align operations with strategic goals.
It’s not just about modeling hospitals. It’s about building a future where every major healthcare decision is tested, measured, and informed by accurate, data-driven projections.
Check out the Executive Brief “Capacity Strategy Powered by a Digital Twin: Examining Use Case Scenarios” to learn more about the research and technology powering GE HealthCare’s Digital Twin and how it’s helping providers design the future of hospital operations.