Remote Patient Monitoring

Physiological and symptom data from wearables predict patient deterioration and trigger timely clinical interventions

Business impact

• Hospital readmission rates — Early alerts enable timely interventions, lowering avoidable readmissions
• Patient engagement — Real-time feedback and app access increase patient involvement in care
• Healthcare costs — Preventing complications reduces expensive emergency and inpatient care
• Patient adherence — Automated monitoring and reminders improve compliance with care plans
• Patient outcomes — Continuous monitoring supports better management and health stability

Data requirements

• Wearable sensors (Numeric) — Collect continuous physiological data like heart rate and blood pressure
• Mobile apps (Text) — Gather patient-reported symptoms and medication adherence data
• Electronic health records (Structured) — Provide historical clinical context and baseline patient information
• Voice recordings (Audio) — Analyze vocal biomarkers for early signs of health deterioration

AI methods and techniques

• Predictive AI — Forecast patient deterioration by analyzing trends in physiological data
• Agentic AI — Autonomously triage alerts and escalate urgent cases to clinicians
• Generative AI — Generate personalized patient communication and symptom surveys

AI models and model families

GPT-4o, Claude, Llama, Custom clinical predictive models

Industries

Real-world evidence

1 documented case study on record.

Companies using this: General Prognostics.