Deep dive — chart 3

Chart 3 — lane rate per patient group, baseline vs recent

The chart with the operational + regulatory bite. Where score-distribution shifts translate into actual patient routing. H3 urgent-intervention rate collapsed 43% → 9%. Other patient groups stable. That is the exact adverse-impact signature regulators look for.

Triage-lane rate per patient group, baseline vs recent

Triage-lane rate per patient group — baseline (left) vs recent (right). H3 urgent-intervention rate dropped sharply while other patient groups stayed stable.

What you are looking at

Grouped bars per patient group (H1 / H2 / H3 / H4 / H5)
Three colors per patient group: Fast-Track (green) / Standard Workup (blue) / Urgent Intervention (red)
Left cluster: baseline (first 30 days) — the "normal" routing per patient group
Right cluster: recent (last 30 days) — the current routing per patient group

What the chart shows

H1, H2, H4, H5 — lane rates unchanged between baseline and recent.
H3 — urgent-intervention rate collapses (43% baseline → 9% recent). Fast-track rate grows (0.7% baseline → ~14% recent). Standard workup absorbs the remainder.
The differential exists only for H3. Other patient groups are unaffected.

34 percentage-point differential in urgent-lane routing. One patient group. Silently. Over 45+ days. This is the exact operational pattern the CMS AI Interpretive Guidance survey question and the HHS Section 1557 adverse-impact analysis are calibrated to detect.

Why aggregate metrics missed it

Because H3 is one patient group among five, the aggregate urgent-lane count barely moved: the H3 collapse rounded to noise across the whole ED. That is why the throughput dashboard stayed green. Aggregate metrics are inherently blind to group-differential patterns, and group-differential patterns are inherently what disparate-impact analysis is built to surface.

The signal that fired the A/B/C determination

Day 89 — the sensor's cross-group differential signal fired on the Fast-Track lane. Fast-track rate shifted +11.3% for H3 vs -1.1% for H1. Differential of 12.4% exceeds the 12% threshold. Once this signal fires, the Snapshot lands on Category C by rule — no matter what any other metric shows.

See the Category-A/B/C decision logic →

The exhibit-quality read

This chart is what a plaintiff's expert witness or state AG investigator asks the operator for first. It is also what the operator's GC should have on file before either lands — because the same chart, in the operator's hand, tells a different story:

Same chart, operator stance: "we identified this pattern through independent verification on Day X, initiated the following remediation on Day X+N, and here is the timeline of that remediation." The chart becomes evidence of good-faith investigation, not evidence of missed monitoring.

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Same lane-shift chart for your hospital's actual AI surface + full determination + 3 fix-first items.

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