Healthcare AI Isn’t Early. It’s Unusable
Modern tools. Legacy systems. Zero results.
When we come across AI in healthcare that fails or doesn't scale.
It's not because the model is wrong. Not because the tech isn’t clever. But because it arrives into chaos and expects order.
I coach CEOs across healthtech, AI, and SaaS - and this pattern keeps repeating:
Great model. Great minds. Great pitch. And then… crickets.
Nothing ships. Nothing scales. Nothing sticks.
And here’s the thing no one wants to admit:
The failure is rarely technical. It’s operational.
We built Spotify. But our buyer still has a cassette player.
Let me show you how that plays out in real deals:
You’ve got a brilliant readmission prediction model.
- AUC is solid
- Calibration is tight
- It’s peer-reviewed and clinician-backed
But how is that prediction actually used?
To take action, the HCP has to:
→ Exit the EHR
→ Log in to a standalone dashboard
→ Decode a probability score with no context
→ Phone care management
→ Fill out a paper form
→ Go back to the original record and update it manually
That’s six steps. For one prediction. In a 12-hour shift already held together with caffeine and good intentions.
And we wonder why usage drops off a cliff by week two.
Let’s be honest - it’s not a model problem. It’s a workflow tax.
Clinicians aren’t lazy. They’re overloaded. If your AI tool adds friction instead of removing it, it dies. Full stop.
And this is the part that hurts to say, but you need to hear it:
If you're designing AI that informs decisions instead of automating them, you’re building slideware. What we need is:
Zero dashboards
Zero extra training
Zero clickpaths
Just the right action, at the right time, in the system they already use
Because in healthcare, success isn’t when the model performs. It’s when the clinician doesn’t even realise it’s there.
That’s the bar.
So if you’re a CEO building in this space, ask better questions:
Not “What’s our model’s accuracy?” Ask:
What decision does this automate, not just inform?
How many steps did we replace, not add?
Who uses it? How often? In what setting?
And here’s a CEO-level question that saves you 18 months of post-pilot pain:
If I ripped this tool out tomorrow, would anyone notice?
If the answer is “probably not” - you’ve built a feature, not a product.
AI in healthcare doesn’t fail because it’s bad. It fails because it's bolted on.
Your buyer wants a plug socket. You’re selling a solar panel.
If you want adoption:
Embed inside existing systems
Automate away work, don’t create more of it
Prove value without needing to explain it
The best tools disappear. Invisible. Effective. Boring.
Don’t sell AI. Sell a better day for the person using it.
That’s what scales. That’s what sells. That’s what survives first contact with reality.
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Thanks for reading.
Until next time!
Regards,
MBA | Healthcare Project Management | Insurane Partnership | Commercial Strategic Partnership Management | B2B Solutions
5mowhat a directly thoughtful post! thank you
Nurse Executive | Systems-Driven Leadership | Patient Safety, Workforce Resilience & Process Innovation (LSSGB) | Educator
5moThis article captures something I’ve witnessed firsthand. Technology, no matter how promising, struggles to deliver meaningful impact, real adoption, and long-term sustainability when introduced into antiquated hospital systems, layered onto fragmented data, unclear workflows, or disconnected from frontline realities. In my experience, sustainable tech integration doesn’t come from simply pushing adoption and practice integration. It comes from shared ownership, iterative design and rollout, engaged teams, and a deep commitment to change management across processes. I’ve had the privilege of working with incredibly talented people. From tech sales, implementation teams, programmers, developers, and tech support to clinical leaders and end users who recognize the value of integrating substantive technology at the front lines of care. But that vision only becomes reality when leadership aligns on both sides, and the technology is given the space to evolve and embed meaningfully within the clinical setting. Thank you, for putting words to the integration barriers many tech teams, clinical leaders, and end users are quietly navigating every day. We need more honest conversations like this in the healthcare innovation space.
“If I ripped this tool out tomorrow, would anyone notice?” This. Loved the article and addresses many of the questions we should be asking before implementing AI or jumping on the “AI Bandwagon”. I think AI offers promising and exciting possibilities but healthcare really needs to match the capabilities of AI and the gaps in healthcare. Clinicians aren’t in the era of “nice to have” innovation but rather “need to have”. And agreed, never a technical disconnect but rather operational. Great article!