The following is a guest article by HT Snowday, Senior Director at Midmark RTLS
A charge nurse trying to determine which patients are ready to move. A nurse searching for an available IV pump. A clinician navigating multiple systems to piece together what’s happening in real time.
In each of these moments, outcomes don’t just depend on access to information; they depend on how quickly that information turns into action.
Healthcare is undergoing a fundamental shift in how technology is used at the point of care. Artificial intelligence is accelerating decision-making. Real-time data is becoming more accessible. And yet, many systems still rely on clinicians and staff to log in, navigate, interpret and act.
That model is starting to break.
One of the most significant changes happening in healthcare technology isn’t just AI itself, it’s how AI agents are redefining the interface. Instead of dashboards and reports, clinicians receive real-time, contextual guidance embedded directly into their workflows.
When that happens, the value of software shifts from a point-solution interface to the quality, accessibility and immediacy of the data powering it.
Agent-enabled RTLS sits at the center of that shift.
This article explores how RTLS is evolving from a visibility tool into a foundational layer for automation, and why that evolution is critical to reducing friction in care delivery.
RTLS is More than the Map
It’s time to start thinking about RTLS as more than a point solution, more than a tracking tool.
The RTLS map is just the interface. The real value is in what location data enables: automation of manual tasks, location-aware safety alerts and operational insights that clinicians and administrators can act on immediately.
When viewed this way, it becomes clear that visibility alone is only the first step. The real opportunity is to use that data to drive action, without requiring clinicians or staff to go looking for it.
The Breaking Point: Caregiver Reality
Care delivery has changed significantly over the last decade. Patients are arriving sicker. The number of devices in each room has increased. Documentation requirements continue to expand. And clinicians are managing a constant stream of interruptions and alerts.
At the same time, something as simple as finding equipment still consumes a meaningful portion of a shift, often 20 to 60 minutes per nurse, per day.
Caregivers don’t need another system to log into. They need fewer steps to complete routine work.
The Interface Is Changing: From Software to Agents
One of the most significant shifts in healthcare IT is how AI agents are redefining the interface.
Today, RTLS and most healthcare technologies are delivered as point solutions. Users log in, navigate dashboards or lists of data, interpret what they see and decide what to do next.
That model doesn’t scale.
A new model is emerging where the interface becomes the agent itself. Instead of navigating systems, clinicians interact through natural language or receive real-time guidance embedded directly into their workflows.
Instead of logging in and looking for data they need, clinicians simply ask:
- “Where’s the nearest available IV pump?”
- “Which patients are ready to move?”
- “Is there a room available in the ED?”
Or increasingly, they don’t ask at all, because the system tells them.
The real shift isn’t just AI generating insights: it’s agents acting in real time. It’s the difference between informing decisions and executing them.
This shift changes the role of RTLS entirely. It moves from being a standalone application to becoming a critical data layer that feeds real-time, contextual decision-making across systems.
From Insight to Action: Where AI Is Already Making an Impact
AI’s first impact in RTLS has been making data easier to access.
What once required reports, filters or analysts can now be handled through simple, conversational queries. Staff can get immediate answers to operational questions, and leaders can surface insights that were previously buried in data.
But the more meaningful shift is happening beyond insight.
The healthcare data evolution we’re seeing is:
Visibility → Insight → Action → Automation
RTLS-driven workflows are already pointing in this direction:
- Alarm systems suppressing alerts when staff are already present
- EMR updates happening in real time as patients move through care settings
- Equipment located instantly and tracked for utilization
These are early examples of automation where location data triggers an outcome without manual input.
AI accelerates this progression by removing the final barrier: execution. Instead of navigating multiple systems to complete tasks, staff can rely on agents to handle them, whether that’s locating equipment, assigning resources or triggering workflows.
Why Context Matters More Than Location
For AI-driven workflows to be effective, raw location data isn’t enough.
Context is what makes location meaningful.
It’s not just: Where is the IV pump?
It’s:
- Is it appropriate for this patient?
RTLS, when implemented correctly, connects location to workflow, status and intent. That’s what allows systems (and eventually AI agents) to move from answering questions to making decisions.
Without that context, automation breaks down. With it, RTLS becomes a critical enabler of intelligent operations.
The Hybrid Future: Scale + Precision
While AI is increasing the value and role of software overall, RTLS itself is evolving.
Historically, when it came to RTLS, healthcare organizations had to choose between scale and precision (broad visibility across a facility or highly accurate, room-level certainty in specific areas).
That tradeoff is disappearing.
A hybrid RTLS approach is emerging, combining Bluetooth Low Energy (BLE) technology for enterprise-wide visibility with infrared technology for clinical-grade, room- and bed-level accuracy where it matters most.
This model allows organizations to:
- Deploy scalable tracking across the entire environment
- Enable staff safety and asset visibility at scale
- Layer in precision only where workflows depend on it
In practice, this eliminates the need to compromise between “find it fast” and “know exactly where it is.”
More importantly, it creates the foundation required for automation. Because while broad visibility enables awareness, precision enables action.
The Strategic Shift: Agent Enablement vs. Agent Ownership
As AI adoption accelerates, many vendors are racing to build proprietary agents.
But that approach introduces risk.
AI is evolving too quickly to lock into a single agent or execution model. More importantly, it fragments the ecosystem, forcing healthcare organizations to manage multiple agents across disconnected systems.
A more sustainable approach is emerging—agent enablement.
Instead of owning the agent, we see an opportunity for RTLS platforms to enable any agent to interact with their data and workflows. That means:
- Structuring data so it’s usable and contextual
- Building connectors that allow external agents to access and act on that data
- Supporting enterprise AI environments where agents can reason across systems
Healthcare organizations are already moving toward enterprise-wide AI strategies. The systems that will succeed are the ones that integrate rather than isolate.
From Visibility to Automation
When you bring these shifts together, the trajectory becomes clear.
RTLS is evolving from a tracking tool to an operational intelligence layer, and ultimately, to an automation engine. AI agents accelerate that evolution by closing the gap between insight and action, turning real-time data into real-time decisions.
But the foundation remains unchanged:
- High-quality, integrated data
- Systems that work together
- A strategy built for flexibility and scale, not data siloes
Because ultimately, this transformation isn’t about RTLS or AI.
It’s about reducing friction in care delivery. Fewer clicks. Less time spent searching. More time focused on patients.
Visibility made progress possible.
Automation is what’s next.
About HT Snowday
HT Snowday is Senior Director at Midmark RTLS, where he focuses on advancing the role of real-time location systems as a foundational layer for intelligent healthcare operations. With deep expertise in healthcare technology, data strategy and workflow optimization, he is particularly focused on how artificial intelligence and agent-driven systems can transform real-time data into automated action at the point of care. Snowday works closely with health systems to align emerging technologies with clinical and operational needs, helping organizations reduce friction, improve efficiency and enable more responsive, data-driven care delivery.
Midmark RTLS is a proud sponsor of Healthcare Scene.
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