CareCompile is a live HL7-native clinical intelligence platform. MediFlow is the synthetic hospital it is tested against — every night, on real infrastructure, with zero real patients. This is what that loop looks like in numbers.
You cannot develop against real patients. Real HL7 feeds mean PHI, BAAs, IRB questions, and a hospital partner willing to let you learn on their data — which no early team has and no responsible team wants. The common workaround is a folder of stale sample messages, and it produces software that works in the demo and breaks on the first real ADT quirk.
CareCompile took the other path: build a synthetic hospital first, and make passing through it the price of every release.
In its first 48 hours of operation, the nightly evaluation caught three distinct model-layer failure modes — none of which had produced a visible error before:
A cloud reasoning model silently exhausted its token budget mid-analysis and returned empty content. Fixed with automatic budget-doubling retry. Found by a failed hyperkalemia case, not by a clinician.
Provider latency at 5 a.m. exceeded the client timeout — responses were arriving and being thrown away. Fixed the same morning. Found by three failed cases with identical signatures.
When the primary model pool rate-limited, the fallback model answered in a format the parser rejected. The eval distinguished this infrastructure failure from a clinical failure automatically — because valid-but-wrong medicine is never retried, only re-examined.
Every one of these would have surfaced eventually. The point is where they surfaced: on synthetic patients, at night, in an email — not in an interpretation a clinician was relying on.
If you're evaluating CareCompile: this is the verification story. The platform is exercised by hundreds of synthetic encounters weekly, regression-tested nightly against known answers, and drilled on failure recovery — with the evidence in queryable tables, not slideware.
If you're evaluating MediFlow: this is the realism proof. The synthetic hospital is not a demo generator — it is the daily test harness of a production clinical AI platform. Its scenarios carry declared expected outcomes precisely because a real system's correctness is asserted against them every night.
A 30-minute live session: pick a scenario, watch the full loop — generation, delivery, AI analysis, assertion — on real infrastructure.
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