From Simulation to Reality: Planning Efficient Robot Workflows in Healthcare
For many healthcare leaders, the promise of robotics is clear, but making it work in daily operations is a different challenge. What separates a successful deployment from an expensive experiment is rarely the robot itself. It is the workflow around it.
At Done Robotics, that work begins long before a robot enters a hospital corridor. Every deployment starts in a digital twin, a virtual replica where logistics flows, elevator usage, and peak-hour bottlenecks are simulated in advance. The goal is to ensure the robot fits into reality, not the other way around.
In practice, trial and error in live environments is both disruptive and costly. Many hospitals find that even small workflow issues can create friction for staff and patients. Simulation allows teams to identify and solve these problems early, building confidence before implementation.
However, hospitals are not predictable systems. They are dynamic environments filled with people. To prepare for this, robots are trained in simulated “human rich” scenarios using generative AI. They learn to navigate crowded hallways, handle unexpected situations, and behave in a way that feels natural in daily operations.
This is critical. In healthcare, how a robot moves often matters as much as what it does. If it feels disruptive, adoption slows down. If it behaves predictably and calmly, it becomes easier to integrate into existing workflows.
Speed is another area where expectations and reality differ. A human porter might complete a task in 8 minutes, while a robot may take 12.5. On paper, that seems inefficient. In practice, robots operate continuously without breaks or sick leave, creating consistent performance over time.
In one simulated scenario, a fleet of five robots was estimated to save between €93,000 and €123,000 annually by reducing manual workload and injury-related costs. This shifts the focus from task speed to total operational efficiency.
Simulation also helps solve the practical details that define reliability. Robots can handle unexpected obstacles by navigating around them and continuing their task, and they can adapt their navigation strategy in tight spaces to avoid hesitation. These small behaviors are often what determine whether staff trust the system.
The same applies when scaling. Determining the right number of robots requires understanding real demand, including peak times, elevator delays, and charging cycles. In one large hospital project, simulation showed that 17 robots were needed to handle daily logistics efficiently. Without this level of planning, investments risk being either insufficient or excessive.
In the end, successful robotics deployments are not driven by technology alone. They depend on how well the solution fits into workflows, how predictable it is in daily use, and how quickly staff come to rely on it.
Simulation, AI-driven training, and data-based planning are how that trust is built before deployment even begins. And in healthcare, trust is what turns robotics from a pilot into part of everyday operations.
