Do you have a Helicopter Rating
Years ago, in my first performance review at a multinational company, I encountered something I’d never seen before: a “Helicopter Rating.” I remember pausing. What does that even mean? The explanation stuck with me. The most valuable employees, I was told, were those who could operate like a helicopter, hovering high enough to see the full landscape, but ready to drop down instantly into the weeds when something demanded attention.
At the time, it felt abstract. Maybe even a bit corporate-mumbo-jumbo (I was young!). But looking back, I realize something. That wasn’t a business concept.
It was triage.
It was the same mental model used in emergency departments across the country, constant scanning, prioritization, zooming in, zooming out. Knowing where to focus, when to act, and when to move on.
And now, years later, that “helicopter” capability is no longer just a human skill.
It’s becoming the defining requirement for how we interact with Agentic AI, especially in healthcare.
The Healthcare Problem Isn’t a Lack of Data. It’s Too Much of It.
Healthcare in the United States doesn’t suffer from a data shortage. It suffers from a data overload crisis. Every patient generates notes, labs, imaging, and reports, and that’s before you get to the Prior Authorization and billing data. And now, layer on top of that AI systems capable of generating summaries, recommendations, scores and so much more. The output is staggering.
We are rapidly approaching a reality where AI can produce more “insight” than any human could ever reasonably read or let alone verify. That’s not a future problem. That’s happening now. And it creates a new bottleneck in the workflow the issue of trust for decisions
If Agentic AI can generate 10,000 data points, who decides which 10 actually matter? If an AI agent drafts a denial appeal, flags a clinical risk, and recommends a course of action who validates it? If everything is prioritized, then nothing is. This is where most conversations about AI in healthcare fall short. They focus on automation with faster documentation, faster coding, faster workflows. But is speed the constraint? The better measure is Judgement.
Healthcare doesn’t need more output. It needs better orchestration. It needs a helicopter. And in the case of Agentic AI the key to the value proposition and overcoming the fluff and “we do AI” that seems to be everywhere, you must have “orchestration.” The real promise of Agentic AI isn’t that it can do tasks.
It’s that it can decide which tasks should be done, in what order, and why.
That’s a fundamentally different paradigm.
A well-designed agentic system should:
- Continuously scan the full patient and financial landscape
- Identify anomalies, risks, and opportunities
- Prioritize actions based on impact
- Drop into the workflow only when human intervention is required
- Learn from outcomes to improve future prioritization
In other words, it should function exactly like that “helicopter” employee, but at scale, across thousands of patients, across millions of claims and across an entire health ecosystem.
The New Skill: Auditing the Machine
As I thought about this, I found myself humming along to Pink Floyd’s “Welcome to the Machine” from their album of the same name, relevant for so many different reasons, but most of all, I like Pink Floyd’s music!
Welcome my son
Welcome to the machine
Where have you been?
It’s all right, we know where you’ve been
You’ve been in the pipeline filling in time
Provided with toys and scouting for boys
You bought a guitar to punish your ma
You didn’t like school and you know you’re nobody’s fool
So welcome to the machine
(Side note on that stanza…. The scouting for boys is a reference to a foundational book, “Scouting for Boys” a handbook for Boy Scouts.
Here’s the uncomfortable truth: as Agentic AI becomes more powerful, humans will spend less time doing and more time judging what’s been done. That’s a massive shift. Clinicians, revenue cycle leaders, and physician advisors won’t just review charts, conduct peer-to-peer discussions, and write appeals. The job changes, and increasingly, we will find ourselves in clinical medicine
- Validating AI-generated recommendations
- Audit prioritization decisions, and
- Intervening only in high-stakes scenarios
The job becomes less about volume and more about precision oversight. But this raises a critical question:
How do you evaluate something you don’t have the capacity to fully review?
Trust Will Be Built on Transparency, Not Volume
The answer isn’t to slow AI down (were that even possible?). It’s to make it explainable, auditable, and selective. Agentic AI in healthcare must evolve with
- Clear reasoning trails (“Why was this case prioritized?”)
- Confidence scoring tied to real-world outcomes
- Escalation logic that earns trust over time
- Feedback loops that incorporate human judgment
In other words, the system itself must develop a helicopter rating. Not just generating output, but knowing when its own output matters.
No more information overload but rather intelligent focus. If we get this right, the impact will be profound. Instead of drowning in data, healthcare teams will:
- Focus only on the highest-impact decisions (can anyone say job satisfaction!)
- Reduce administrative burden without sacrificing quality
- Improve patient outcomes through better prioritization
- Navigate (or perhaps this is remove) payer complexity with precision
And perhaps most importantly, we restore something that’s been eroding for years, confidence in decision-making.
The Future Isn’t More AI. It’s a Better Perspective.
Now that the old performance review metric suddenly feels a lot less abstract to me. The ability to zoom out, see the full healthcare system, and then drop into the exact right moment. But this time it’s not just a human skill. It’s the design challenge of our time. Because in a world where AI can generate everything…
The real value isn’t in producing more.
It’s in knowing what actually matters.
