Interactive Product Demo

See the AI in Action

Explore seven clinical scenarios inside a simulated Epic Hyperspace environment. Risk scores are generated by our validated XGBoost model (AUC 0.862). ESI acuity and workup orders are produced by our clinical decision engine.

Real XGBoost model (AUC 0.862)

Real ESI predictions

7 clinical scenarios

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Triage Data (auto-populated)

Chief Complaint

Vital Signs

Problem List (PMH)

Data auto-populated via FHIR R4 prefetch

0.862

AUC-ROC

Temporal holdout (2022)

0.408

Average Precision

At 2% prevalence

0.017

Brier Score

Well-calibrated

Operating Points

Target Sensitivity	Threshold	Achieved Sensitivity	False Positive Rate
95%	0.0057	95.2%	27.7%
90%	0.0127	90.4%	20.5%
80%	0.0323	80.3%	11.3%

Validation Summary: Model trained on NHAMCS 2018-2021 (n = 70,839), tested on temporally held-out 2022 data (n = 16,025). XGBoost with Optuna hyperparameter optimization and isotonic calibration. No data leakage: RFV features engineered post-split using train-only thresholds; patient-level splitting for MIMIC cross-validation.

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Your Emergency Department

We're seeking one strategic health system to partner on our inaugural prospective trial. If your system runs Epic and cares about patient safety, we'd love to talk.

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See the AI in Action

Let's Bring This toYour Emergency Department

Request a Technical Deep Dive

Let's Bring This to
Your Emergency Department