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Most healthcare intelligence failures are not data failures. They are perception failures introduced by language abstraction — long before data ever enters a system.
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This project was designed to test a narrow but critical question:
When language is already disciplined, where should automation stop — and where must human intelligence begin?
The answer turns out to be more instructive than the signals themselves.
In consulting and pharma intelligence teams, automation is often treated as an end in itself.
Signals are scraped, scored, ranked, and summarized. Often without questioning whether the language being processed can support that interpretation.
This project deliberately restricted scope to a setting where language, actors, and intent are already aligned:
The objective was not prediction.
It was boundary-finding.