What is realistic for AI in a healthcare organization right now?

Administrative burden is the tractable problem

Ask clinicians where the time goes and the answer is documentation, prior authorization, and messages. None of that is the work they trained for, all of it is high volume, and most of it is assembly and drafting rather than judgment.

That is also the part where a mistake is caught before it matters, because a clinician signs.

What we build

Prior authorization packets assembled against the specific payer’s requirements, with missing elements flagged before submission rather than after denial. First pass approval rate is the metric that moves, and it is measurable within a month.

Documentation support that drafts from the encounter for a clinician to review and sign. The clinician remains the author. The model removes the typing.

Coding and billing review against what the documentation actually supports, which finds both under-coding and compliance risk. Both matter, and most organizations only look for one.

Referral correspondence and loop closure, which is a patient-safety issue disguised as an administrative one.

The boundary, and it is firm

We do not build clinical decision support, diagnostic tools, or anything that recommends treatment. That is regulated as a medical device, needs clinical validation and a regulatory pathway, and is a different engagement with a different kind of partner. We will tell you that on the first call.

Data handling is designed for HIPAA before anything is built: what may be processed where, under what agreement, with what retention, and with local models where protected data cannot leave your environment.