Predictive Health Monitoring & Alert System with GPT-4o-mini

Description

How It Works
The system collects real-time wearable health data, normalizes it, and uses AI to analyze trends and risk scores. It detects anomalies by comparing with historical patterns and automatically triggers alerts and follow-up actions when thresholds are exceeded.

Setup Steps
Configure Webhook Endpoint - Set up webhook to receive data from wearable devices
Connect Database - Initialize storage for health metrics and historical data
Set Normalization Rules - Define data standardization parameters for different devices
Configure AI Model - Set up health score calculation and risk prediction algorithms
Define Thresholds - Establish alert triggers for critical health metrics
Connect Notification Channels - Configure email and Slack integrations
Set Up Reporting - Create automated report templates and schedules
Test Workflow - Run end-to-end tests with sample health data

Workflow Template
Webhook → Store Database → Normalize Data → Calculate Health Score → Analyze Metrics → Compare Previous → Check Threshold → Generate Reports/Alerts → Email/Slack → Schedule Follow-up

Workflow Steps
Ingest real-time wearable data via webhook, store and standardize it, and use GPT-4 for trend analysis and risk scoring. Monitor metrics against thresholds, trigger SMS, email, or Slack alerts, generate reports, and schedule follow-ups.

Setup Instructions
Configure webhook, database, GPT-4 API, notifications, calendar integration, and customize alert thresholds.

Prerequisites
Wearable API, patient database, GPT-4 key, email SMTP, optional Slack/Twilio, calendar integration.

Use Cases
Monitor glucose for diabetics, track elderly vitals/fall risk, assess corporate wellness, and post-surgery recovery alerts.

Customization
Adjust risk algorithms, add metrics, integrate telemedicine.

Benefits
Early intervention reduces readmissions and automates 80% of monitoring tasks.