Doceree Dialogue – Chapter HIMSS: HT Snowday on Giving Clinicians Back Their Time

At HIMSS 2026, it can feel like every conversation eventually turns to AI. But some of the most interesting ideas at the show came from a discussion about something far less flashy — the invisible infrastructure that keeps a hospital running, and how it quietly gives clinicians their time back. In the final episode of Doceree Dialogue – Chapter HIMSS, HT Snowday, Senior Director of Midmark RTLS, joined host Ritesh Patel, EX- Chief Growth Officer at Doceree, for a wide-ranging conversation about real-time location, automation, and a refreshingly different philosophy on where AI actually belongs in healthcare. 

Midmark RTLS, a business unit of Midmark Corporation, works exclusively in healthcare. At its most basic, the company's technology tracks where assets and people — both staff and patients — are within a care environment. But as Snowday is quick to point out, location is only the starting point. 

What Real-Time Location Really Means in Healthcare 

"Real-time locating" may sound like a mouthful, but Snowday describes it plainly: it's about knowing where things and people are, moment to moment, across a healthcare environment. That includes mobile assets — the portable EKG machines and, most commonly, the IV pumps that every facility manages in fleets — as well as staff and patients. 

Yet knowing a location, on its own, isn't the goal. 

"What we're really after is where we can provide efficiency and automation," Snowday said. 

The deeper aim, he explains, is to take work off clinicians' plates. If the system already knows where people and equipment are, it can quietly handle tasks that would otherwise fall to a nurse or provider — freeing them to focus on the patient in front of them.  

Automating the Moments That Matter 

The clearest example Snowday offers is one Midmark RTLS has automated for years: the nurse call cycle. Every licensed bed in the US has a nurse call system — the button a patient presses to ask for help. It's a closed loop: the call triggers a notification, and when a nurse responds, they're expected to press a button in the room to close it out. 

But that final step assumes an ideal scenario. 

"Imagine you're a nurse who has entered the room and the patient is in distress or on the floor. The last thing you want to do is press a button," Snowday said. "You're going to care for the patient first." 

When location makes that loop close automatically, the nurse never has to choose between documentation and care. It's a small thing that turns out to matter enormously — and it's just one of many. The same badge that enables it can also carry a staff duress function, letting a nurse signal that they themselves need help. Much of this happens passively, in the background. 

"Just wear the badge, and things will happen in an automated fashion that make your life easier," he said. 

Where a real-time view is genuinely useful, Midmark surfaces it — through what the company calls its "glance and go" board, a dashboard that shows every patient, their room, and their stage in the clinic process at a glance. 

Two Sides of the Same System 

For all its real-time capability, Snowday says the technology has a second, equally powerful dimension. 

"There are two sides to our system: the real-time automation, and the retrospective data collection," he said. 

That second side is where optimization lives. By analyzing every interval in a clinic workflow — across every patient, in aggregate — organizations can spot where time is being lost and test changes to fix it. The payoff can be substantial. Snowday points to one large oncology facility that credits the system with a dramatic gain in throughput. 

"They believe they're driving 30% more patients through the environment with the use of our system," he said. 

What makes that facility exceptional, he notes, is that it uses both sides well — real-time visibility to drive efficiency in the moment, and rigorous data analysis to drive testable, experiment-based change over time. Make an operational change, then use the data to confirm whether it actually helped. 

The Data Science Gap — and How AI Can Close It 

That kind of analysis, though, requires a skill set most healthcare organizations simply don't have. Snowday is candid that even with tools designed to make the data as accessible as possible, turning it into insight remains a leap many customers can't make on their own. 

"There aren't that many data scientists on the planet you can hire to do this," he said — and most IT teams consuming the data don't have them either. 

This, he believes, is where AI-driven analytics could be transformative — not as hype, but as a way to bring genuine data-science depth to a far broader set of customers. Organizations often already know the open-ended question they want answered: How do I improve the execution of my process? How do I improve the distribution of my assets? The answer is in the data. AI could finally make it reachable. 

A Different Philosophy on AI: Connect, Don't Compete 

Here Snowday's perspective diverges sharply from much of the HIMSS floor. Rather than racing to build its own AI tools, Midmark RTLS has chosen a more deliberate role. 

"Our point of view is not to drive AI tools ourselves, but to be very good at providing connection to AI — providing data formatted in a way that's very accessible to AI," he said. 

In practice, that means building connectors that let AI agents operate the system through natural language. Snowday imagines the mundane made effortless: assigning a new badge to a staff member by simply asking, or posing an ad hoc question mid-shift. 

"Tell me where the free IV pump nearest to me is right now," he offered as an example — a question the system can already answer, but which today takes a few deliberate steps through the interface. An agent, or even a voice command, could collapse that into a single sentence, putting the system's power in reach of far more people in the environment. 

Crucially, Snowday wants that intelligence to live inside the tools customers already trust — a point sharpened by a growing trend he's seeing among large health systems: many have deployed their own enterprise AI assistant "this side of the firewall," giving them data sovereignty and a tool that's both technically and perceptually safe for staff to use. 

"We want to build a connector to that. We don't want to go in and deploy an agent," he said. 

It's a philosophy he says has been repeatedly validated in his conversations at HIMSS: meet customers inside their trusted, enterprise-standard environment and let them use it to control your solution — rather than adding yet another app for clinicians to learn. 

The Road Ahead: Domain Expertise in a Fast-Changing World 

As the conversation drew to a close, Snowday reflected on a market in constant motion — one where large platforms increasingly move into categories pioneered by specialists, and where the pace of change can be dizzying. His take is neither defensive nor starry-eyed. The big systems, he acknowledges, will capture a great deal. But the specialists who truly understand a specific clinical moment — the nuance of what happens in a room, in a workflow, at the bedside — will keep finding ways to win, precisely because they understand something the larger systems don't. 

"All software is under threat," he said. "But it's an amazing world — I feel like we're lucky to be in it." 

It's a fitting note to close the Chapter HIMSS series on. Amid all the noise about what AI might someday do, Snowday's vision is grounded and humane: technology that works quietly in the background, connects to the tools people already trust, and — above all — gives clinicians back the time and attention to care for their patients.