Algoricum

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.

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.