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BUILT INSIDE HEALTH SYSTEMS

Secure, responsible, and explainable AI starts here.

Performance monitoring, explainability, and guardrails for every AI model in your health system: Gen AI, ML, and "black-box" vendor models alike. Validated in production clinical environments.

ExplainerAI — Live Feed Monitoring
Sepsis ML predictorNominal
Radiology Gen AI modelNominal
Spinal cancer predictorDrift flagged
ASA scoring modelNominal
247 models under governance→
ONE PLATFORM TO GOVERN HEALTHCARE AI

Always inside your environment. Always watching the model, not just the output.


ExplainerAI™ uses patented technology to tap directly into AI inferencing across both internally developed and third-party models, continuously assessing performance, safety, and compliance.

Integrated with Epic and leading EHRs, ExplainerAI™ provides full model transparency into AI decision-making — so your organization can ensure that AI tools are compliant, ethical, trusted, and optimized for real-world care delivery.

Read how ExplainerAI™ works under the hood →

Sepsis ML predictorInference trace · live
Monitoring
Performance
98.2%
 
Drift
0.4σ
 
PHI leak risk
Low
 
KEY CAPABILITIES

Every signal a health system needs to trust its AI.

01 · Failure Detection

Hallucinations & drift

A "Judge & Jury" LLM ensemble detects Gen AI hallucinations in real time. Sensors flag drift the moment inputs diverge from training data — before it reaches clinical workflows.

02 · Explainable AI

See exactly why

A suite of explainability dashboards surfaces bias, fairness, PHI leakage, and the variables driving each model's output — for informaticists, data scientists, and clinicians alike.

03 · Epic / EHR Integration

No context switching

15+ years of Epic integration expertise. 3x faster EHR integrations with the AutoETL agent. Predictions, scores, and alerts land directly inside clinician workflows.

04 · Deployment

On-prem or private cloud

Data never leaves your environment. $100M+ in R&D, 15+ years of development inside leading academic health systems, 8 patents issued.

05 · Centralized Alerting

Thresholds, not noise

Define model-specific thresholds and get alerted before AI-driven risk causes harm — with full activity logs ready for auditors.

06 · Compliance & Auditing

HIPAA & NIST by design

Granular security and privacy controls detect models "leaking" PHI. Role-based access, full audit trails, lineage, and traceability built in.

EXPLAINERAI™ IN ACTION

What governance actually looks like on screen.

Results Overview Category: "Hallucination" 15 Total Flagged (12) Clear (3) Test Categories Check Result Confidence Source consistency flagged 0.91 Fact alignment flagged 0.87 Citation match clear 0.22 Hallucination Detected Detection Log Check Timestamp Outcome Reasoning HC-2201 06:21:03 hallucinated Unsupported numeric claim not present in source notes. HC-2202 06:24:51 hallucinated Cited stage inconsistent with chart progression. HC-2203 06:31:18 verified Matches lab values from prior encounter. HC-2204 06:38:02 hallucinated Recommends therapy not supported by record. HC-2205 06:42:45 verified Confirmed against structured EHR field.

Hallucination detection

Drift Scores Feature Δ score resp_rate_min 0.612 spo2_mean 0.588 lactate_max 0.471 map_min 0.203 hr_max 0.184 wbc_count 0.097 temp_max 0.052 Last update 04:12 ago Window 30-day rolling, n=8,420 Live Distribution resp_rate_min, last 7 days Drift Detected → Reference Distribution resp_rate_min, training baseline

Model drift

Model Accuracy Over Time Sepsis ML predictor · rolling 30-day window 100% 95% 90% 97.8% Jan Mar May Jul Accuracy by Subgroup Parity check against overall model accuracy (97.8%) Male Female ≥65 yrs 18–64 yrs Black / African Am. White Training Population n = 84,210 encounters White (41%) Black (29%) Hispanic (19%) Other (11%)

Responsible AI dashboards

Feature Importance Sepsis ML predictor — case #44021 0 WBC count min 0.412 Δ1 creatinine — serum min 0.367 qSOFA score 0.330 Lactate max 0.207 Respiratory rate max 0.154 Mean arterial pressure min 0.114 Temperature max 0.077 Heart rate variability 0.050 Platelet count 0.030 Prior admission count 0.015 Computed via SHAP-aligned attribution · top 10 of 38 model inputs

Clinical-grade explainability

WHERE IT FITS

Operationalize governance across the AI lifecycle.

Pre-Procurement AI Sandbox Validation Training Production Optimization
Pre-procurement

Test the accuracy and performance of vendor models before committing to a purchase — with objective, evidence-based procurement criteria.

Validation & training

Identify bias and failure modes before models ever touch a patient record, while validating, training, and optimizing AI solutions.

Production

Ongoing monitoring of every live AI solution in your health system. Patented sensors tap into black-box vendor models without source access.

Combined with our AI discovery solution, AI Sniffer, Cognome's platform provides end-to-end AI risk management — because you can't govern what you don't know.
REAL-WORLD USE CASES

One platform, every clinical setting.

CRITICAL CARE

Sepsis ML Predictor

The Emergency Department's care team views ExplainerAI™'s clinical explainability and ethical-use dashboards to understand how and why the model made its prediction — including every contributing factor.

RADIOLOGY

Radiology Gen AI model

ExplainerAI™ evaluates model outcomes to confirm it has not hallucinated, giving clinical teams the confidence to act on AI-generated radiology insights.

ONCOLOGY

Spinal Cancer Predictor

A nurse practitioner gets an alert the moment a predictive spinal cancer model exceeds thresholds set in ExplainerAI™, enabling timely follow-up with the right care team.

PREOPERATIVE

ASA Scoring

Operative teams review model accuracy and drift in real time, keeping ASA scoring reliable and clinically trustworthy over time.
WHO IT SERVES

Built for every healthcare AI stakeholder

IT Teams

IT Security & Compliance

 

Equips teams with the controls needed to accelerate AI adoption without exposing the enterprise to unmanaged risk.

Built on the NIST AI RMF — multi-layered risk detection, centralized alerting, agentic data mapping to Epic, and HIPAA/NIST/Joint Commission–level auditing.

Informatics & Data Science Teams

Informatics & data science

 

Observability, transparency, and performance monitoring for any AI or ML model — drift, hallucinations, PHI leakage, and other forms of degradation.

Pre-built analytics aligned with NIH's Responsible AI framework deliver transparency, explainability, and complete model detail.

Clinical Teams

Clinicians

 

Fosters adoption through transparency and actionable insight. Clinicians trust AI when they can understand it.

ExplainerAI™ surfaces the variables driving a model's recommendation, turning "black-box" output into a decision teams can act on with confidence.

DESIGNED ON LEADING FRAMEWORKS FOR RESPONSIBE, SECURE & COMPLIANT AI
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