Free burnout assessment -- no credit card required

Understand performance, stress, and communication in real clinical environments.

GRW Healthcare combines validated burnout assessment with 468-landmark facial action coding to give clinical leaders a dual-signal view of team wellbeing -- without surveillance, without video storage, and without guesswork.

468
Facial Landmarks
90s
To Full Report
0
Videos Stored

The Challenge

Healthcare has a visibility problem

Clinician wellbeing directly impacts patient outcomes. But the signals that matter most -- stress, fatigue, communication breakdowns -- remain invisible until it is too late.

68%
Burnout Rate

of healthcare workers report symptoms of burnout. Traditional surveys catch it months too late.

#1
Cause of Medical Errors

Miscommunication between care teams is the leading cause of preventable adverse events in hospitals.

40%
Cognitive Overload

of clinical decisions are made under significant cognitive load, increasing the risk of decision fatigue and errors.

2.4x
Patient Risk Factor

Burned-out clinicians are associated with up to 2.4x higher rates of patient safety incidents.

How It Works

From recording to actionable insight in under two minutes

A lightweight, non-invasive workflow designed to fit into existing clinical routines without adding burden.

01

Upload or Record

Clinicians record a brief 60-90 second session reflection directly in the browser. No app install required. Video is processed entirely client-side and never leaves the device.

02

AI Analyzes Dual Signals

Our engine maps 468 facial landmarks frame-by-frame using MediaPipe FaceMesh, simultaneously scoring against the Copenhagen Burnout Inventory (CBI) self-assessment to produce a dual-signal analysis.

03

Dual-Signal Insight

Where self-report and biometric signals align, confidence is high. Where they diverge, the system flags it for deeper exploration -- surfacing patterns the clinician may not consciously recognize.

04

Improve Outcomes

Clinical leads receive anonymized, aggregated dashboards. Individual clinicians get private, actionable recommendations. Over time, longitudinal trends reveal the real trajectory of team wellbeing.

Features

Purpose-built for clinical environments

Every feature is designed to respect clinical workflows, protect privacy, and surface insights that actually improve outcomes.

Session Analysis

Frame-by-frame facial action unit analysis across 468 landmarks. Generates composure, presence, and cognitive load scores from a single 60-90 second session recording.

Burnout Assessment (CBI)

Validated Copenhagen Burnout Inventory scoring integrated alongside biometric analysis. Produces a research-backed self-report signal that complements facial coding data.

Divergence Detection

Flags cases where self-reported wellbeing and biometric signals diverge. When someone says "I'm fine" but their micro-expressions suggest otherwise, the system highlights the gap for reflection.

Longitudinal Tracking

Track individual and team-level trends over weeks and months. Spot early warning signs of declining wellbeing before they become critical incidents or attrition events.

Scenario Tagging

Tag sessions by context: post-shift debrief, difficult patient interaction, handover fatigue, night shift recovery. Compare signals across scenarios to identify systemic stressors.

Institutional Reporting

Anonymized, aggregated dashboards for clinical leaders. Identify department-level patterns, track intervention effectiveness, and generate compliance-ready reports.

Scientific Foundation

Built on validated research, not marketing claims

Every signal we surface is grounded in peer-reviewed methodology. We report confidence levels for all outputs and use language like “what this likely indicates” rather than making diagnostic claims.

Copenhagen Burnout Inventory (CBI)

Kristensen et al., 2005

A validated, peer-reviewed instrument measuring personal, work-related, and client-related burnout across three distinct dimensions. Used in over 500 published studies globally.

CBI scores provide a self-report baseline that we compare against biometric signals. When both signals align, confidence in the assessment is high.

Facial Action Coding System (FACS)

Ekman & Friesen, 1978

The gold standard taxonomy for classifying visible facial movements into Action Units (AUs). Our system maps 468 landmarks per frame using MediaPipe FaceMesh.

Facial coding provides an observational signal that may reveal patterns not captured by self-report alone. Confidence scores are provided for all AU classifications.

Cognitive Load Theory

Sweller, 1988

Framework for understanding how mental effort affects information processing and decision-making under pressure -- directly applicable to high-stakes clinical environments.

Our cognitive load indicators draw on observable facial cues that research suggests are associated with mental effort. Results indicate what this likely suggests, not diagnostic conclusions.

Yerkes-Dodson Performance Model

Yerkes & Dodson, 1908

The empirical relationship between arousal and performance, demonstrating an inverted-U curve. Moderate stress optimizes performance; too much or too little degrades it.

We use this framework to contextualize arousal signals relative to likely performance zones. The model helps clinicians understand where they may sit on the curve.

Our commitment to responsible AI

GRW Healthcare does not diagnose medical conditions, predict clinical performance, or make employment-affecting determinations. All outputs include confidence intervals and are explicitly framed as observational signals intended to support -- not replace -- professional clinical judgment. Our system is designed as a wellbeing support tool, not a surveillance instrument.

Output Preview

Actionable intelligence, not raw data

Every analysis produces a structured report with severity bands, confidence scores, and contextualized recommendations.

Session Analysis
Demo Data -- Post-Shift Debrief
SAMPLE REPORT
Composure Index
72
Moderate84%
Cognitive Load
61
Elevated79%
CBI Score
58
ModerateSelf-report
Divergence
24%
Flagged76%
Signal AlignmentSelf-report vs. Biometric
76%

Moderate alignment. CBI self-report suggests lower burnout than biometric indicators. This may indicate an early-stage pattern worth monitoring.

Recommendations
Monitor

Cognitive load indicators are elevated post-shift. Consider reviewing workload distribution or scheduling recovery time before the next high-acuity block.

Explore

Signal divergence (24%) suggests a gap between self-assessed wellbeing and observable indicators. A brief check-in conversation may surface underlying stressors.

Positive

Composure under clinical pressure remains within a functional range. Communication markers suggest effective handover behaviors despite cognitive load.

Demo data for illustration only. Not a real clinical assessment.v2.1.0

Privacy & Security

Privacy is not a feature. It is the architecture.

Healthcare data demands the highest standard of protection. Our system was designed from the ground up to minimize data exposure while maximizing clinical insight.

Client-Side Processing

All video analysis runs directly in the browser using TensorFlow.js and MediaPipe FaceMesh. Video frames are processed locally and never transmitted to our servers.

Zero Video Storage

We never store, upload, or transmit video files. Only derived numerical scores and aggregated metrics are persisted. The raw biometric data is discarded after analysis completes.

Row-Level Security

Database access is enforced at the row level via Supabase RLS policies. Clinicians can only access their own data. Administrators see aggregated, anonymized views only.

PIPEDA-Ready Architecture

Designed to align with Canadian federal privacy legislation (PIPEDA) and provincial health information acts. Consent workflows, data minimization, and right-to-delete are built in.

End-to-End Encryption

All data in transit is encrypted via TLS 1.3. Data at rest is encrypted using AES-256 through Supabase managed infrastructure. API keys and secrets are stored in environment variables, never in client code.

Audit Logging

Every data access event is logged. Administrators can review who accessed what and when, supporting institutional compliance requirements and incident investigations.

SOC 2 Aligned
PIPEDA Compliant
Zero PII in Logs
TLS 1.3 Encrypted

Pricing

Assess for free. Pay only for your report.

Take the full 19-question CBI assessment with dual-signal biometric analysis at no cost. When you are ready for your personalized AI-generated report with clinical recommendations, unlock it for a one-time fee.

Assessment

Free
  • Full 19-question CBI assessment
  • Real-time biometric facial analysis
  • Subscale scores across 3 dimensions
  • Severity band classification
  • Suppression signal detection
  • Confidence score
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Most Popular

Full Report

$29per report
  • Everything in Free, plus:
  • AI-generated clinical narrative
  • 3-5 personalized recommendations
  • Biometric divergence deep-dive
  • Longitudinal trend analysis
  • Evidence-based resource library
  • Downloadable PDF report
  • Email delivery within 60 seconds
Start Assessment

Enterprise

Custom
  • Unlimited assessments & reports
  • Team-wide dashboards
  • Anonymized aggregate reporting
  • Department-level breakdowns
  • Custom SSO / SAML integration
  • Dedicated account manager
  • API access for EHR integration
  • PIPEDA & PIPA compliance docs
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Why this matters

One burned-out clinician leaving costs $50,000-$100,000 in replacement costs. A $29 report that helps you intervene early pays for itself hundreds of times over.

Get Started

Take your burnout assessment today

Complete the 19-question CBI assessment with real-time biometric analysis. Create an account to access your full AI-generated report with personalized clinical recommendations.

256-bit encryption
PIPEDA compliant
Zero video storage
No credit card required

Deploying for your whole team?

Enterprise plans include unlimited assessments and reports, anonymized aggregate reporting, department-level analytics, and a dedicated account manager.

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