NeuriSight, Inc. offers an AI‑powered wearable monitoring platform that continuously captures physiological signals such as heart rate and skin conductance to generate real‑time agitation risk scores for patients. By analyzing these data streams with adaptive machine‑learning models, clinicians receive early, evidence‑based alerts that enable proactive de‑escalation and reduce incidents of aggression and self‑harm. The system is currently undergoing feasibility studies and pilot validation in clinical settings.
Funding
Funding not disclosed
Founders
Product
Problem
In inpatient psychiatric settings, agitation and violent incidents often occur before staff can intervene, leading to patient self‑harm, workplace aggression, and increased staff burnout. Current monitoring relies on intermittent observations and reactive measures, resulting in delayed response and limited documentation of agitation events.
Solution
NeuriSight offers an AI‑powered wearable platform that continuously captures physiological signals such as heart rate and skin conductance to identify early signs of agitation. The wearables are discreet and designed for constant use, feeding raw biosensor data into a cloud‑based analytics engine. Proprietary machine‑learning models analyze the data stream in real time and generate risk scores that predict agitation episodes up to several minutes before they manifest. Clinicians receive these scores through an integrated dashboard, enabling proactive, data‑driven de‑escalation strategies. The system also logs each event, providing documentation that supports staff safety, liability reduction, and quality improvement initiatives. Ongoing feasibility research and pilot validation are confirming the platform’s predictive accuracy and clinical utility.
Target Audience
Primary customers are inpatient psychiatric hospitals and behavioral health units seeking to reduce aggression incidents and support nursing staff, as well as research institutions partnering on clinical validation of predictive monitoring technologies.
Features
- Discrete wearable sensors that monitor heart rate, skin conductance, and motion continuously
- Real‑time AI analytics that classify long‑stream biosignals and produce actionable agitation risk scores
- Adaptive machine‑learning models that improve detection accuracy as more patient data are collected
- Clinician dashboard with visual risk indicators, trend history, and integration hooks for existing EHR workflows
- Automated event logging to create auditable agitation records for compliance and liability management
- Privacy‑by‑design data handling with encryption in transit and at rest, meeting hospital security standards