# AI ED-Triage Reproducibility + Adverse-Impact Snapshot

**== SAMPLE / GENERIC EXAMPLE -- SYNTHETIC DATA, NOT A CLIENT ENGAGEMENT ==**

**Format:** contrarianAI Independent-Verifier $499 Snapshot (v0.9 sensor)
**Snapshot generation date:** 2026-07-22
**Snapshot ID:** CAI-HC-GEN-DEMO-ce186b99c376ad73412ca600

---

## 1. Scope of Snapshot

This SAMPLE Snapshot addresses a synthetic mid-market hospital's AI-driven ED-triage severity classifier. Scenario details:

- **Hospital profile:** mid-market community/regional hospital (~30,000-90,000 ED encounters annually across the audited facility footprint)
- **AI system in scope:** ED-triage severity classifier (mock model version ed-triage-classifier-v3.1.2)
- **Audit period:** 2026-04-15 through 2026-07-13 (90 days)
- **Total encounters analyzed:** 5,996
- **Baseline window:** first 30 days (1,888 encounters)
- **Regulatory framework applied:**
  - HHS Section 1557 (42 USC 18116) -- non-discrimination in AI-driven clinical decisions
  - CMS AI Interpretive Guidance (State Operations Manual Appendix A -- AI-in-clinical-decision-support)
  - FDA AI-inclusive-device draft guidance ("Marketing Submission Recommendations for a Predetermined Change Control Plan")
  - Joint Commission Sentinel Event Policy (AI-related adverse events reportable 2026-2027)
  - HIPAA 45 CFR 164 (privacy + security + retention)
  - State AG AI-in-healthcare enforcement (California AG, NY AG, Massachusetts AG active 2025-2026)
  - 21st Century Cures Act -- Information Blocking + AI transparency
  - NIST AI RMF 1.0 (voluntary but referenced in CMS guidance)

---

## 2. Sensor summary

- **Sensor version:** contrarianAI-distsensor-v0.9
- **Sensor family:** distributional-shape (independent of production model family)
- **Independence:** sensor operates on the ED-triage AI's output stream only. Distinct from the production model family. Different mathematical basis (distributional-shape statistics vs neural-net severity classifier). Different retention pipeline. contrarianAI holds no equity, employment, or vendor relationship with any healthcare-AI vendor (Epic Cognitive Computing / Aidoc / Viz.ai / Bayesian Health / Qventus / any similar).

- **Detection thresholds active:**
  - Mean-shift threshold: 1.5 standard deviations from baseline
  - Variance-ratio threshold: 1.5x baseline
  - KL-divergence threshold: 0.2
  - Cohort adverse-impact differential threshold: 12% shift across cohorts

---

## 3. Findings summary

- **Total drift events detected:** 17
- **High severity:** 9
- **Medium severity:** 8
- **First drift day (post-baseline):** Day 44
- **Critical adverse-impact-differential signal flagged:** YES
- **Headline metric:** Cohort H3 (elderly multimorbid) urgent-intervention rate dropped from 43% (baseline) to 9% (recent) -- a 34 percentage-point cohort-differential in the wrong direction

---

## 4. Overall determination

**CATEGORY C -- INSUFFICIENT FOR ATTESTATION**

Multiple high-severity distributional-drift events detected in the AI ED-triage severity classifier across the audit period. Hospital should not represent the ED-triage AI as HHS Section 1557-defensible or CMS-compliant until named remediation completes and independent re-audit confirms closure. Joint Commission sentinel-event review protocol may apply if patient-harm outcomes correlate to the drift window.

---

## 5. The 3 fix-first items scoped to this AI surface

1. **Freeze or hold-for-manual-review** all post-drift AI-generated triage decisions for cohort H3 (elderly multimorbid, age 65+ with 3+ comorbidities) pending adverse-impact validation. Every H3 encounter routed to fast_track or standard_workup post-Day 45 should receive attending-physician manual re-triage. Document the pause + resumption criteria in the AI Governance Program per CMS AI Interpretive Guidance.
2. **Retain evidence artifacts** for every encounter in the audit period per HIPAA 45 CFR 164.316 retention + potential HHS OCR Section 1557 investigation. Model version pin, input snapshot (chief complaint + admission source + comorbidity profile + LOS estimate), triage-score output, lane routed, decision-hash, configuration state at decision-time. Litigation holds should be issued now covering the affected patient population.
3. **Add adaptive drift-monitoring** to production ED-triage pipeline. Point-in-time or annual attestation is insufficient given the between-audit blind spot where mortality/readmit risk silently accumulates. Real-time cohort-differential monitoring w/ automated alerting to the Chief Medical Officer + AI Governance Committee is the operational fix.

---

## 6. Detailed drift events

Each event includes date, sensor type, affected patient cohort, severity, and plain-language detail describing what the sensor observed in the ED-triage AI's decision stream.

### Day 44 (2026-05-29) -- cohort_distribution_divergence (cohort H3)
- **Severity:** high
- **Detail:** Cohort H3 score distribution shape has diverged from baseline (KL=2.108). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.1077

### Day 44 (2026-05-29) -- cohort_lane_shift (cohort H3) [lane: Fast-Track]
- **Severity:** medium
- **Detail:** Cohort H3 fast_track rate shifted +11.2% (baseline 0.7% -> window 11.9%).

### Day 51 (2026-06-05) -- cohort_distribution_divergence (cohort H3)
- **Severity:** high
- **Detail:** Cohort H3 score distribution shape has diverged from baseline (KL=2.062). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.0622

### Day 51 (2026-06-05) -- cohort_distribution_divergence (cohort H5)
- **Severity:** high
- **Detail:** Cohort H5 score distribution shape has diverged from baseline (KL=1.208). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 1.2081

### Day 51 (2026-06-05) -- cohort_lane_shift (cohort H3) [lane: Fast-Track]
- **Severity:** medium
- **Detail:** Cohort H3 fast_track rate shifted +13.0% (baseline 0.7% -> window 13.7%).

### Day 58 (2026-06-12) -- cohort_distribution_divergence (cohort H3)
- **Severity:** high
- **Detail:** Cohort H3 score distribution shape has diverged from baseline (KL=2.252). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.2519

### Day 58 (2026-06-12) -- cohort_lane_shift (cohort H3) [lane: Fast-Track]
- **Severity:** medium
- **Detail:** Cohort H3 fast_track rate shifted +10.4% (baseline 0.7% -> window 11.1%).

### Day 65 (2026-06-19) -- cohort_distribution_divergence (cohort H3)
- **Severity:** high
- **Detail:** Cohort H3 score distribution shape has diverged from baseline (KL=2.011). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.0111

### Day 65 (2026-06-19) -- cohort_lane_shift (cohort H3) [lane: Fast-Track]
- **Severity:** medium
- **Detail:** Cohort H3 fast_track rate shifted +10.0% (baseline 0.7% -> window 10.7%).

### Day 72 (2026-06-26) -- cohort_distribution_divergence (cohort H3)
- **Severity:** high
- **Detail:** Cohort H3 score distribution shape has diverged from baseline (KL=1.909). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 1.9094

### Day 72 (2026-06-26) -- cohort_lane_shift (cohort H3) [lane: Fast-Track]
- **Severity:** medium
- **Detail:** Cohort H3 fast_track rate shifted +12.8% (baseline 0.7% -> window 13.5%).

### Day 79 (2026-07-03) -- cohort_distribution_divergence (cohort H3)
- **Severity:** high
- **Detail:** Cohort H3 score distribution shape has diverged from baseline (KL=1.980). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 1.9804

### Day 79 (2026-07-03) -- cohort_lane_shift (cohort H3) [lane: Fast-Track]
- **Severity:** medium
- **Detail:** Cohort H3 fast_track rate shifted +10.1% (baseline 0.7% -> window 10.8%).

### Day 86 (2026-07-10) -- cohort_distribution_divergence (cohort H3)
- **Severity:** high
- **Detail:** Cohort H3 score distribution shape has diverged from baseline (KL=2.953). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 2.9531

### Day 86 (2026-07-10) -- cohort_lane_shift (cohort H3) [lane: Fast-Track]
- **Severity:** medium
- **Detail:** Cohort H3 fast_track rate shifted +13.4% (baseline 0.7% -> window 14.0%).

### Day 86 (2026-07-10) -- cohort_distribution_divergence (cohort H5)
- **Severity:** medium
- **Detail:** Cohort H5 score distribution shape has diverged from baseline (KL=0.205). Bimodal-emergence or long-tail-shift likely.
- **KL-divergence:** 0.2054

### Day 89 (2026-07-13) -- cross_cohort_cohort_differential_signal [lane: Fast-Track]
- **Severity:** high
- **Detail:** CRITICAL: fast_track rate has shifted +11.3% for segment H3 vs -1.1% for segment H1 over the audit period. Differential of 12.4% exceeds cohort-differential signal threshold (12%). This is the pattern HHS OCR + State AG healthcare-AI investigators look for.
- **Per-cohort shifts:** {"H1": -0.0109, "H2": -0.0048, "H3": 0.1133, "H4": 0.0035, "H5": 0.0088}

---

## 7. Counterparty-question rehearsal

**HHS OCR / CMS survey / malpractice defense counsel / State AG question the hospital's own compliance officer would demand:**

> "Reproduce this AI-generated triage decision from Day 47 including the patient's chief complaint category, admission source, comorbidity profile, model version at decision-time, triage score, and lane routed -- as a defensible record we can produce for HHS OCR investigation, CMS survey response, malpractice defense counsel, or State AG AI-oversight review."

**Sample encounter decisions from Day 47:**

| Encounter ID | Triage Score | Lane | Model Version | Decision Hash |
|--------------|--------------|------|---------------|---------------|
| E-003040 | 77.69 | Urgent Intervention | ed-triage-classifier-v3.1.2 | `edt-003040` |
| E-003044 | 47.64 | Standard Workup | ed-triage-classifier-v3.1.2 | `edt-003044` |
| E-003043 | 65.41 | Standard Workup | ed-triage-classifier-v3.1.2 | `edt-003043` |
| E-003042 | 45.65 | Standard Workup | ed-triage-classifier-v3.1.2 | `edt-003042` |
| E-003041 | 64.63 | Standard Workup | ed-triage-classifier-v3.1.2 | `edt-003041` |

**Reproduction status:** all 5 sampled Day-47 encounter decisions can be reproduced with defensible-records match: model version pinned, decision hash cryptographically bound, input data (patient cohort + chief complaint category + admission source + comorbidity profile + LOS estimate at encounter-time) retained via decision-provenance record. Full inputs available via decision_hash lookup in the executable-action ledger (AIC layer #7) + time-of-decision knowledge snapshot (AIC layer #8).

**Retention:** artifacts retained per HIPAA 45 CFR 164.316 (6-year minimum) + state-specific malpractice tail (typically 7-10 years) + HHS OCR Section 1557 investigation-hold discipline.

---

## 8. What full Enterprise Attestation adds beyond this Snapshot

This $499 Snapshot addresses ONE AI system (ED-triage severity classifier) at the SAMPLE hospital. A full Enterprise Attestation ($35-55K / 3-6 weeks) covers:

- **All 8 AIC layers** (this Snapshot covers layers 1-4 + 6; Enterprise adds layers 5, 7, 8)
- **Full AI surface inventory** (ED-triage + inpatient early-warning + imaging AI + ambient documentation + revenue-cycle coding + patient-messaging AI -- every AI-touched clinical + operational surface)
- **Signed regulator-facing attestation deliverable** (this Snapshot = internal-use PDF; Enterprise = signed statement suitable for CMS survey response, Joint Commission review, HHS OCR inquiry, malpractice-carrier renewal questionnaire, or State AG production)
- **On-site scoping session with your team** (this Snapshot = data-driven only; Enterprise = on-ground with your CMO + CMIO + General Counsel + Compliance + IT/CIO)
- **30/60/90 remediation roadmap w/ named owners + milestones**
- **Adaptive drift-monitoring implementation guidance**
- **Re-audit cadence recommendation tied to your Joint Commission survey cycle + medical-staff privileging cycle**

**Baseline Audit ($2,500 / 5 days) is the intermediate tier** -- gap map + measurable test + 30/60/90 roadmap on ONE AI surface. Snapshot credit applies to Baseline OR Enterprise upgrade within 30 days.

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## 9. Independent-verifier declaration

I, Kevin Luddy, as principal of contrarianAI LLC, personally attest that:

1. contrarianAI is not an employee, contractor, equity holder, or vendor of the audited hospital or any of its healthcare-AI vendors (Epic Cognitive Computing, Aidoc, Viz.ai, Bayesian Health, Qventus, DeepScribe, Abridge, Suki, or any similar).
2. Assessment tools (distributional-shape sensor, retrieval-auditor, tool-call-grader, prompt-drift-sensor, predictor-corrector, router-drift-sensor) use model families and evaluation methods distinct from those in production at the audited hospital.
3. This Snapshot is based on the data-sample submitted by the buyer. Changes to the audited hospital's infrastructure after the Snapshot date are outside this Snapshot's scope.
4. Underlying audit evidence is retained for the Snapshot horizon and will be made available for legitimate regulatory (HHS OCR, CMS, State AG, Joint Commission) / audit / counterparty inquiry with appropriate legal process.
5. The independent-verifier principle applies: the control plane cannot reside within the entity it is meant to regulate. contrarianAI is that independent control plane for this Snapshot's scope.

**Signed:** Kevin Luddy, Principal, contrarianAI LLC
**Date:** 2026-07-22
**Retention key:** `ce186b99c376ad73412ca600`

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## 10. Charts + artifacts

- `chart_hc_distributional_drift.png` -- 7-day rolling mean triage-severity-score per patient cohort over the 90-day period
- `chart_hc_baseline_vs_drifted.png` -- Cohort H3 baseline vs recent triage-score distribution (largest observed drift; leftward shift indicates UNDER-triage)
- `chart_hc_lane_shift.png` -- Triage-lane rate per cohort, baseline vs recent
- `hc_drift_analysis.json` -- machine-readable analysis + event metadata
- `hc_dataset.csv` -- synthetic 90-day ED-encounter input data
- `hc_agent_decisions.csv` -- ED-triage AI output + scored triage decisions

---

*End of Snapshot CAI-HC-GEN-DEMO-ce186b99c376ad73412ca600*

**== END OF SAMPLE -- YOUR ACTUAL $499 SNAPSHOT WILL LOOK STRUCTURALLY IDENTICAL BUT WITH YOUR HOSPITAL'S DATA + AI SYSTEM + FACILITY FOOTPRINT + REGULATORY-FRAMEWORK-CITATIONS ==**

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## To purchase YOUR hospital's $499 Snapshot:

1. Visit: **contrarianai-landing.onrender.com/healthcare-snapshot.html**
2. Click **Buy $499** -- Stripe direct-buy, no procurement approval
3. Complete intake form (~2 min): name your AI system + submit 100-1000 anonymized ED-encounter triage decision records
4. Report PDF delivered via email within 3 business days

**Snapshot credit ($499) applies to upgrade within 30 days:**
- **Baseline Audit** ($2,500 / 5 days) -- Snapshot credit = $499 off
- **Enterprise Attestation** ($35-55K / 3-6 weeks) -- Snapshot credit = $499 off

Book a call: **cal.com/kevin-luddy-0dlzuu**