Remote Patient Monitoring

AI-powered continuous remote monitoring improves patient care and reduces hospital readmissions.

Business impact

• Hospital readmission rate — Lowered by early identification of high-risk patients and timely clinical interventions
• Patient outcomes — Improved through continuous monitoring and personalized care adjustments
• Operational efficiency — Increased by automating data collection and prioritizing clinical workflows

Data requirements

• Wearable biometric sensors (Numeric) — Collect continuous vital signs data such as heart rate and blood pressure
• Patient-reported symptom surveys (Text) — Provide subjective health status updates to complement sensor data
• Electronic health records (Structured) — Supply historical clinical data for personalized risk assessment

AI methods and techniques

• Predictive AI — Used to forecast patient deterioration and readmission risk from continuous data
• Agentic AI — Automates triage and prioritizes alerts for clinical teams based on patient risk
• Generative AI — Generates personalized patient communication and symptom check prompts

AI models and model families

GPT-4o, Claude, Llama, Custom deep learning models

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: Biobeat, Brook Health, GE Health Care, Partners Health Care, Techstack, Unity Point Health.