Statera Health.

The problem

Clinical practices run on paper that arrives as files. Referrals, results, reports, correspondence. Every one has to be identified, filed against the right patient and the right episode, and made findable again.

Nobody chooses to do that work. It falls to practice managers, and it eats roughly thirty per cent of their time. That is not a small inefficiency. It is close to a third of a senior administrator’s week spent reading documents to work out what they are.

The volume also makes it unfixable by hiring. More documents means more hours, in a straight line.

What I built

An AI classification system that reads each incoming clinical document, decides what it is, and routes it.

The important design decision was not the model. It was what happens when the model is unsure. A classifier that is right ninety per cent of the time and silent about the other ten per cent is worse than useless in a clinical setting, because somebody still has to check all of it.

So the system reports its own confidence. High-confidence documents are classified and filed automatically. Anything below the threshold is held for human review. Staff stopped reviewing every document and started reviewing only the exceptions.

That is the difference between a demo and a system people trust.

The result

99.9% of documents are now classified without a human touch.

The remaining fraction surfaces as a short exception queue rather than a full inbox. The time that was going into identifying and filing documents goes back to the practice.

The measure that mattered to the client was not model accuracy in isolation. It was how much of the work disappeared.