Predict. Prevent. Protect.
Transforming Falls Prevention with Predictive AI
Elarin helps care teams act before a fall happens — using personalized, clinically explainable predictions.
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The Problem at Hand
Falls Are Costly
Falls cost the average skilled nursing facility $380,000 annually; larger facilities can face over $700,000 per year.
Current solutions react too late. Elarin predicts risk early.
- Beyond financial costs, falls lead to increased medical care, regulatory penalties, and reduced quality of life for residents.
- Falls are the leading cause of injury-related deaths among seniors.
- Approximately 25% of all preventable hospitalizations from SNFs are due to falls.
Why Elarin?
What Makes Elarin Different
Most fall prevention technologies rely on video monitoring or motion detection to alert staff after movement increases fall risk.
Features
- Predicts fall risk before movement
- AI-powered decision-making
- Real-time, early alerts
- Reduces false alarms
- Integrates with clinical workflows
- Video or motion-based only
- Delayed staff response
Traditional
Elarin
Current fall prevention tools act after risk appears.
Elarin uses AI to predict and prevent falls before they happen.
Technology Overview
Predictive Fall Prevention
Elarin integrates vital sign data, mobility patterns, and environmental conditions into a continuous learning model. At its core is personalization — an individualized, constantly updated profile used to simulate risk scenarios and support timely, proactive interventions.
Updated Patient Profile
Continuously updated indivisual profile simulates each patient’s physiological and environmental profile.
Natural language interface
Natural language interface explains predictions and supports clinical decision-making
Personalized predictions
Personalized predictions support timely, individualized interventions — not generic thresholds
Our Features
Technology Overview
Predictive Fall Prevention
Elarin integrates vital sign data, mobility patterns, and environmental conditions into a continuous learning model. At its core is personalization — an individualized, constantly updated profile used to simulate risk scenarios and support timely, proactive interventions.
Digital Twin
Continuously updated Digital Twin simulates each patient’s physiological and environmental profile.
Natural language interface
Natural language interface explains predictions and supports clinical decision-making
Personalized predictions
Personalized predictions support timely, individualized interventions — not generic thresholds
Our Features