Fraser Health Authority
A Robotic Lab Sample Delivery System
Facilitating more efficient lab sample transport between hospital departments
1
Project Overview
Optimizing Laboratory Logistics in a Complex Care Environment
Royal Columbian Hospital (RCH), part of Fraser Health Authority, manages a large volume of clinical samples and waste that must be transported safely and efficiently across multiple floors and departments. Currently, this work relies on a combination of manual hand-offs, cart-based transport via porters and staff, and the hospital’s pneumatic tube system. While functional, these methods place significant demands on staff time and create workflow bottlenecks.
There are over 15 different sample collection areas in the hospital and some areas require staff walking up to 12 minutes between areas one way as they are in buildings far from the Central Lab room.
With RCH’s growing patient population and expanding lab workloads, the hospital sought to explore robotics as a way to augment existing workflows. The goal was not to replace staff, but to reduce their burden, improve sample tracking, and enhance operational efficiency—all without disrupting established clinical routines.
In collaboration with Ma Robot AI, a local robotics startup, the build and programming of the robot are now underway. My contribution has centered on uncovering pain points in the existing lab transport process and designing a future-state service blueprint to define the essential tasks and capabilities the robot will need to perform.
Shown above is a mockup representing the robotic testing mechanism that is currently being trialed.
2
The Opportunity
Increasing Efficiency Through Robotic Vehicle Transportation
Observations revealed several pain points: staff frequently travelled back and forth to deliver or retrieve samples, porters were sometimes unavailable to transport samples, and the lab would become bottlenecked if many samples were delivered at one time.
Additionally, while pneumatic tube systems offered speed, they were limited in size and accessibility as not all sample types could be sent through them. These constraints presented a clear opportunity for robotics to improve efficiency and reduce the risk of travel delays and free porters to complete higher value tasks.
The hospital envisioned a proof-of-concept robotic system that could autonomously transport samples and waste between the lab and designated drop-off points, such as the MHSU entrance or GI Clinic. This first phase focused on creating a minimal viable product (MVP) capable of:
Self-driving navigation
Navigating autonomously in hallways and elevators
Maintain sample integrity
Maintaining temperature control and security for sensitive samples
Track chain-of-custody
Logging timestamps and chain-of-custody information
Integrate into hospital workflows
Integrating with existing hospital systems for monitoring and alerts
The image below showcases one of the long hallways lab assistants and porters have to travel through in order to obtain and deliver patient samples from different departments.
3
Building Understanding
Immersive Observation and Human-Centered Research
Designing a robotic system for hospitals meant starting with establishing an understanding of the people who would interact with and use it. I spent time in the labs, shadowing lab assistants as they prepared samples and coordinated deliveries. I also observed lab assistants collecting specimens during rounds, and the lab dispatcher planning which departments needed sample collections.
Each role had distinct needs: lab assistants required straightforward ways to load and dispatch the robot, nurses needed timely updates on sample transport or if transport was on the way to their department, and dispatchers needed visibility into the robot’s journey for scheduling and regulatory compliance. Interviews and observation highlighted that while staff were enthusiastic about automation, any system that slowed them down or added complexity would not be adopted.
The below storyboard illustrates the sample collection and transportation of the sample start from the lab, traveling to the specific pickup unit, and then returning the sample back to the lab.
4
Synthesizing Insights
Designing Around Real-World Workflows
Through this research, several critical insights emerged:
Workflow integration is essential
The robot could not operate in isolation; it needed to complement existing lab and clinical routines.
Safety cannot be compromised
Temperature control, contamination prevention, and chain-of-custody tracking were non-negotiable.
Minimal cognitive load
Interfaces and interactions had to be intuitive for staff across multiple roles.
Physical environment constraints
Hallways, elevators, and docking stations required careful planning for autonomous navigation.
As shown in the image below, hospital hallways are ever-evolving. A robotic solution would need to accommodate and account for changing environments that include people and medical equipment.
5
Considerations and Constraints
Balancing Automation with Human Factors
Designing for a hospital robotics pilot required attention to both technical and human factors. The robot needed to navigate crowded spaces safely, avoid interfering with patient care, and maintain secure handling of clinical materials.
Interfaces had to be simple and communal, supporting both on-board and secondary monitoring. Regulatory compliance, including lab safety standards and pneumatic tube system rules, guided every design choice.
The images below represent a lab assistant persona and their typical workday structure. Please contact me for additional workflows and personas related to this project.
6
Early Iterations
Mapping Workflows and Developing the MVP
I began by mapping current-state workflows, identifying key touchpoints where the robot could provide value. The MVP focused on a single route: from the lab to a drop-off location, with the robot returning to its charging dock.
Key design decisions included:
Simple dispatch and stop functions on the robot interface.
Clear visual feedback for robot status and location.
Integration points with existing hospital tracking systems.
Modular design to support future expansion.
The images below represent task flows of some of the current primary tasks lab assistants and porters perform during their workday. A robotic system would have to take these tasks into account during the transport of a sample.
The current output of this project consists of two future-state service blueprints, each designed to capture how robotic transport could support laboratory workflows at Royal Columbian Hospital. These blueprints map out the essential tasks, touchpoints, and safety considerations required for the system to function within the hospital’s complex environment. They serve as a foundation for testing the initial minimum viable product (MVP), ensuring that both technical feasibility and user needs are considered from the start.
Moving forward, these service blueprints will act as living documents. We are iterating on them in close collaboration with Ma Robot AI, the local robotics startup developing and programming the prototype. As real-world testing begins, we will refine the blueprints to address emerging insights, adapt to hospital constraints, and evolve the MVP into a safe, reliable, and human-centered solution for robotic transport.
7
Reflection
Early Outcomes and Next Steps
This project is still in its beginning stages, and the emphasis remains on testing, learning, and adapting. The MVP is intentionally minimal—focused less on delivering a polished product and more on uncovering how robotics can fit into the realities of hospital environments. Each test run offers new insights, whether about navigation challenges, user interaction patterns, or workflow integration.
The image below is the real test robot outside of the Central Lab. It is currently being tested within Royal Columbian Hospital by Ma Robot AI. The first tests are to ensure it can complete the above service blueprint steps in order to integrate in the existing lab systems.
By grounding the process in observation, co-design, and staff feedback, we are ensuring that the system evolves in a way that respects the realities of clinical work.
