After two decades of selling RTLS into hospitals across New England, I’ve had the same conversation hundreds of times. Nurses can’t find IV pumps. Every dashboard says everything is fine. So why can’t they find a pump when a patient needs one? Because this is a complex problem — and complexity requires a different kind of solution.
After two decades of selling RTLS into hospitals across New England, I’ve had the same conversation hundreds of times. It goes like this.
Nurses on the floor are spending too much time searching for IV pumps. So naturally, they ask for more.
Procurement flags it. Their ERP dashboard shows enough pumps on hand, allocated to meet PAR levels. On paper, they’re fine.
Maybe it’s a maintenance issue? Nope. The CMMS dashboard confirms 100% compliance with every preventive maintenance schedule. Clinical Engineering is clean.
Is the RTLS system working? Absolutely. Every pump accounted for. We know exactly where they all are.
So why can’t nurses find a pump when a patient needs one?
This Is a Complex Problem. Not a Complicated One.
Because this is a complex problem. And I don’t mean that as an adjective. I mean that in a very specific sense.
There’s a decision-making framework called Cynefin that classifies problems by their relationship to cause and effect. Complex problems are where cause and effect only make sense in hindsight. The same action produces different results every time depending on the environment, the people, and variables nobody is tracking.
Sound familiar?
Same hospital. Same policy. Same pumps. Availability is fine on days. Falls apart on nights. That’s not a complicated problem waiting for the right solution. That’s complexity.
And you can’t buy your way out of complexity. You have to design your way through it.
What Designing for Complexity Actually Looks Like
That’s where Precursor comes in.
Precursor is an AI-native intelligence platform built specifically for complex healthcare operations. But here’s what makes it different. We don’t start with technology. We don’t start with the problem. We start with the goal.
We build a semantic model of how your hospital actually operates — the spaces, the workflows, the assets, the people, the interdependencies. We map where you are today (your as-is) and where you need to be (your to-be). Then we use AI to analyze the gap between those two states and determine the most intelligent path forward.
The Difference Between Solving a Problem and Achieving a Goal
Not “here’s where your pumps are.”
“Here’s why availability is at 40%. Here’s what’s driving it. Here’s the process change that gets you to 95%. And here’s what that’s worth in dollars and hours.”
That’s the difference between a system designed to solve a problem and a platform designed to achieve a goal.
And in a complex environment like a hospital — where cause and effect only make sense in hindsight — designing for the goal isn’t just a better approach.
It’s the only approach that actually works.
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