Case Study:

Optimizing Medical Space Utilization with AWS: How Data Changed the Story

Are We Really Running Out of Rooms?

If you have worked in a busy hospital, you have probably heard the same frustration we heard on this project:

“We don’t have enough exam rooms.”

But here is the interesting part. Sometimes the issue is not space at all.

Sometimes the real issue is that we cannot see what is actually happening.

This is exactly what happened at one of Toronto’s major healthcare facilities. Staff were convinced space was stretched thin. Bookings looked full. Everyone felt the pressure. But when we walked through the area, we kept seeing empty rooms.

• So which version was true?
• The feeling or the data?

This is where our story begins.

How We Turned Assumptions Into Clear Insights

The organization wanted clarity. They needed accurate and continuous occupancy data that respected patient privacy and provided the truth behind how their spaces were used. Not estimates. Not assumptions. Real signals and real patterns.

This opened the door to something exciting for us:

How could we combine IoT sensing with AWS analytics to reveal the real story about space usage?

Smart Sensors: Privacy First

We started by choosing sensors that provide insights without compromising privacy.

mmWave sensors that detect micro-movements like breathing
Multi-room 3D stereoscopic sensors for higher coverage
Sustainability Design Reviews providing design modification considerations to enhance energy efficiency or carbon reduction
No cameras, no audio, no identity data

These sensors became a quiet and reliable way to understand room activity without collecting any sensitive information.

Security Built In

Every sensor connection was designed with security in mind
mmWave sensors authenticated using mutual TLS
Sustainability Design Reviews providing design modification considerations to enhance energy efficiency or carbon reduction
3D sensors validated through custom AWS Lambda authorization
Only approved devices were allowed to publish data into the system

How We Used AWS to Bring Everything Together?

Once the sensor data reached AWS, the cloud pipeline took over.

Ingest: Amazon Data Firehose streams raw data immediately
Store: Amazon S3 provides a centralized and secure data lake
Process: AWS Lambda cleans, transforms, and enriches incoming events
Query: AWS Athena runs SQL queries directly on S3 without servers
Visualize: Amazon QuickSight delivers real-time dashboards for staff and leadership

This gave us a fully automated flow from raw motion signals to dashboards that decision makers could trust. No servers, low maintenance, and near real-time visibility. For the first time, the team could see exactly how their exam rooms were being used.

The Plot Twist:
The Data Revealed a Different Story

When we analyzed the first 11 monitored rooms, one insight stood out:

Actual utilization was only 33 percent
This challenged everything the staff believed
The issue was not the number of rooms
The issue was lack of visibility

This new understanding reshaped how the organization planned schedules, allocated rooms, and set expectations. And this was just phase one. As more clinics open, the system will continue to expand and build a long-term library of usage trends. Over time, this will help teams predict room demand more accurately.

Accurate Utilization Data:

We revealed true usage patterns and eliminated assumptions.

Better Resource Allocation:

Teams now have data-driven guidance for planning and scheduling.

Scalable Architecture:

The design easily supports additional sensors and new clinics.

Privacy by Design:

No cameras, no audio, and fully encrypted communication.

Predictive Insights:

Historical trends now help staff plan for peak times.

Improved Patient Care:

More efficient workflows create a smoother experience for both patients and staff.

Building Smarter Spaces Together