AI and automation in healthcare operations
Deterministic automation handles the volume: the same task, the same way, every time, at effectively zero cost per transaction. A model handles the work where the input varies too much for a rule to hold. Choosing wrong in either direction is the most common and most expensive mistake in this category.
This page is a map. If you already know what you want fixed, skip it and go straight to the relevant service.
Choosing between them
Does the work have a defined right answer given the inputs? If yes, use a rule. It is cheaper, auditable by construction, and fails loudly rather than quietly. If the input is unstructured or the judgment is interpretive, a model earns its place.
If you are not sure which row you are in, that is what the teardown is for. It is thirty minutes and the answer is often “a rule and a queue,” which is a useful thing to find out before spending money.
Everything on this topic
The boundary
Where patient data goes
De-identify, infer, re-identify, and where that pattern breaks down. The question that unblocks everything else.
Read it →The mechanism
Agents in revenue cycle
What an agent does that a scripted bot cannot, and the four places they are not reliable enough yet.
Read it →The evidence
What we build
Our own product work, which is where our current-generation AI engineering can be judged directly.
See it →Free · 45 minutes
Shadow AI review
Where AI is already touching your patient data, and what to sanction or shut off. You keep the map.
See the scope →The other line
Healthcare automation
The deterministic side, which is where most of the recoverable hours actually are and involves no model at all.
See the hub →For your reviewers
Security and compliance
Written in questionnaire language, so security review starts from a document rather than a discovery call.
Read it →Worth saying directly
The incentive in this market runs toward putting a model in front of everything, because that is what gets funded. We think that produces expensive, fragile systems that get switched off after the first incident, and it has been the pattern in this industry for two technology cycles now.
Our bias is the opposite: automate deterministically wherever the work permits it, use a model only where the input genuinely varies, and keep a person between the output and the payer. If that describes a smaller engagement than you expected, that is the honest answer and it is also the one that survives its third year.
Next step
Thirty minutes on one workflow. We will tell you which side of the line it sits on and what it would take.
Book a teardown