AI Made the Call. Patient Crashed. Who Gets the Blame?
AI Handoff Insider
Handoff #9 (Insider Edition) | Reading time: 6 minutes
A misdiagnosis.
A missed dose.
An AI-powered tool quietly nudges a clinician toward the wrong decision. No one notices, until the patient crashes.
And then?
Silence.
No clear protocol. No audit trail. No governance structure that knows what to do when the harm wasn’t human… but also wasn’t not.
This is the uncomfortable reality in most hospitals today. We’ve rushed to adopt AI tools without building the safety nets to catch them when they fail. And they do fail.
What follows isn’t a hypothetical. It’s already happening. But no one’s owning it, because no one’s ready.
The First AI Error Isn’t a Question of If, It’s When
Here’s the part no one likes to say out loud: AI will make mistakes. So will the people using it.
We’ve seen algorithms rank patients unfairly based on cost, not clinical need. We've watched large language models hallucinate clinical facts into patient notes. And we’ve imagined (realistically) what happens when outdated training data tells an AI tool to recommend the wrong medication dosage.
These aren’t bugs in the system. They're design flaws in the process. The real problem isn’t the error, it’s the governance vacuum waiting behind it.
Everyone’s Involved. No One’s Responsible.
Who owns the failure when an AI-assisted decision leads to harm?
The physician who followed the AI’s advice?
The hospital that deployed it?
The vendor who built it?
Or the patient who never even knew AI was involved?
Right now, the answer is murky. And that's the problem.
Most hospitals still rely on traditional clinical governance built for human errors. But AI introduces variables no one trained for: black-box logic, automation bias, shifting algorithms that learn post-deployment.
We’re stuck trying to assign blame in a system that doesn’t yet know how to track or explain what just happened.
The Silent Risk: No Audit, No Oversight, No Action
In the event of an AI-related incident today, here’s what typically happens:
There’s no audit trail explaining how the AI made its decision.
The clinician didn’t document that they relied on AI, because they weren’t trained to.
The governance committee isn’t equipped to review machine errors, because it wasn’t built to.
The system continues learning, but no one’s monitoring what it’s learning from.
If this feels like malpractice waiting to happen, it is. Not because people are careless. But because hospitals haven’t caught up with the tools they’re rolling out.
Why This Matters Now
AI isn’t just decision support anymore. It’s embedded in triage. Imaging. Discharge planning. Documentation. Clinical pathways.
When something goes wrong, the stakes are no longer theoretical. Patients are impacted. Lawsuits are coming. And leadership teams will be asked why no one saw it coming.
Spoiler: they should have.
Here’s What Needs to Change, Today
Enough handwringing. Let’s talk action. If you’re a healthcare leader, informaticist, or clinical director, here’s where to start:
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