Algoricum

12/03/2025

Everyone says they want innovation.
But what they really want is consensus - and consensus kills speed.

Half of the great ideas never die because they fail.
They die because no one has the nerve to say, “Let’s do it.”

You don’t need more meetings.
You need fewer maybes.

11/28/2025

Everyone’s obsessed with response time.
Nobody’s tracking persistence time.

The data doesn’t lie - most clinics go silent after the 2nd follow-up.
After that, it’s basically a free-for-all for whoever still remembers to reply.

Speed gets you seen.
But persistence - that’s where the conversions live.

11/19/2025

Everyone wants efficiency.
Nobody wants to admit they’re addicted to tweaking instead of doing.

Most “process improvements” are just sophisticated procrastination.
We call it optimization because “avoiding hard work” doesn’t look good on a project plan.

The most efficient teams aren’t the ones with the best tools.
They’re the ones who remember what the tools were actually for.

11/12/2025

People love to sound sure of themselves - especially when they shouldn’t be.
The loudest opinions usually live right before the learning curve dips.
The experts? They’re too busy figuring out what they don’t know to shout about what they do.

So the next time someone sounds 100% certain, ask yourself one thing:

Are they experienced… or just at the top of the first hill?

11/05/2025

10/13/2025

The $120,000 Problem Nobody Measured
A clinic owner once bragged to me that their team responds to new patient inquiries in under five minutes.

They were proud of that stat.

Said it like it was the thing keeping their calendar full.

Here’s the kicker: they were still losing nearly $120,000 a year in potential revenue.

Why?

Because speed wasn’t the problem anymore.
The real money was leaking out after that first reply
When the staff sent one message and never followed up.

When a patient said, “Let me think about it,” and nobody checked back in.

When conversations went cold because someone “didn’t want to bother them.”

Speed is table stakes. It’s the bare minimum.
It gets you seen.
But being seen doesn’t fill your schedule.
Being remembered does.

The clinics that win don’t stop after the first reply.
They follow up like professionals - patiently, persistently, personally - until one of two things happens:
✅ The patient books.
❌ Or they politely decline.
Everything else is just money evaporating into thin air.
Speed gets you seen.
Personalization gets you heard.
Persistence gets you paid.

02/21/2025

AI in healthcare is incredible - until it isn’t.

Right now, hospitals are rolling out AI-driven tools without questioning the one thing that will make or break their success:

Where is the data coming from?

The reality?
✅ AI doesn’t “think” like a doctor. It repeats patterns from historical data.
✅ If that data is biased, flawed, or incomplete, AI will scale those same mistakes across entire hospitals.
✅ AI models can be wildly confident and dangerously wrong at the same time.

So what happens when we train AI on:
⚠️ Decades of misdiagnosed conditions?
⚠️ Unequal treatment data that favors some patient groups over others?
⚠️ Incomplete EHR records that miss key health outcomes?

We don’t get better healthcare.

We get automated bias.

AI mistakes are harder to detect because they look precise.

No one questions an algorithm that sounds confident.

So what should we be doing differently?
• Audit the data before trusting the output. If your AI model is trained on garbage data, the results will be garbage - just delivered faster.
• Look for blind spots in decision-making. AI isn’t a black box, it should be explainable.
• Keep humans in the loop. AI should assist decision-making, not blindly dictate care plans.

AI can truly transform healthcare, but only if we fix what’s feeding it.

02/20/2025

A hospital I spoke with recently tested an AI tool to flag high-risk patients.

One day, it flagged a perfectly healthy patient as needing immediate ICU admission.

The doctors were confused.
• Vitals? Normal.
• Labs? Unremarkable.
• Symptoms? None.

So they did what any rational clinician would do: They asked why.

And AI? It had nothing to say.

No reasoning. No breakdown. Just a black-box decision.

Turns out, the model had been trained on billing codes instead of clinical markers.

Since this patient had a history of expensive treatments, AI assumed they must be critically ill.

That’s the problem with AI in healthcare right now.

It’s confident but not always correct.

And if doctors can’t question its logic, then what’s the point?

What needs to change?
✅ AI must be able to explain itself - no more “just trust the algorithm.”
✅ Hospitals and clinics need to question how AI reaches decisions before trusting them.
✅ AI isn’t replacing human judgment; it should be enhancing it.

Would you trust an AI that can’t explain its decisions?