An Evidence-to-State architecture for reliable AI in Retail Pharmacy

The Problem - A true event can still be a wrong context:

Imagine a retail prescription that hits a prior-authorization reject at 9:01 AM. At 9:20, the PA gets submitted. At 11:42, a pharmacist talks to the PBM and learns that the case is still under clinical review. While inventory is available and no action is required from patient. The next useful workflow action would be to simply monitor the payer decision.

Each one of those facts can be accurate. Yet an AI assistant that sees only the original reject, can still make the wrong recommendation. It might tell pharmacist to resubmit the claim, ask the patient to call the insurer, or prioritize outreach that is no longer meaningful. Here the failure is not necessarily bad data event. It is a failure to reconstruct the current operational state from a sequence of otherwise valid observations, i.e. the lack of clarity...

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