Predictive Health Scorer
Anticipates health risks and flags early warnings using real-time data
Description
Challenge:
Insurers often lack early indicators of health deterioration, resulting in reactive care, rising chronic conditions, and costly hospitalizations. Traditional underwriting and care management rely on static declarations and annual health checks, missing dynamic lifestyle or clinical trends. The Predictive Health Scorer bridges this gap by continuously evaluating customer health risk using real-time signals—helping insurers drive early interventions, hyper-personalized care, and proactive risk management.
How It Works:
This agent continuously aggregates multi-source data like wearable metrics, claims history, diagnostic reports, and lifestyle inputs. It applies machine learning models and medical rules to calculate a dynamic health score for each customer, which updates with every new data point. It maps historical baselines and detects abnormal deviations in vitals or habits (e.g., sleep drop, activity reduction, irregular glucose patterns). It segments users by risk levels—low, moderate, or high—and flags potential onset of chronic diseases like diabetes or hypertension. The system also triggers nudges, care pathways, or alerts to health coaches based on thresholds breached.
Benefits
Detects early health risk patterns with 85–90% accuracy.
Increases preventive care engagement by 2.5x.
Lowers hospitalization probability by 30–40% through early alerts.
Helps underwriters and wellness teams adjust plans in near real-time.
Enhances customer lifetime value through healthier outcomes.
Enables dynamic risk pricing and personalized product offerings.
Features
The Predictive Health Scorer continuously monitors customer health patterns and dynamically assigns risk scores. It integrates clinical data with lifestyle behavior, ensuring real-time visibility into health trends. By enabling early interventions, it supports cost control, customer engagement, and adaptive underwriting.
Features & Capabilities:
Real-Time Health Score Engine: Calculates dynamic risk scores based on behavior, vitals, and claims.
Multi-Source Data Fusion: Combines wearables, EHR, self-reported data, and pharmacy records.
Trend Deviation Detection: Identifies abnormal dips or spikes in sleep, activity, vitals, or glucose levels.
Risk Stratification: Classifies individuals into low, medium, or high health risk segments.
Chronic Condition Prediction: Flags early indicators for diabetes, CVD, hypertension, etc.
Personalized Health Nudges: Sends contextual lifestyle suggestions via SMS, email, or app.
Care Pathway Recommendations: Triggers alerts for teleconsultation or diagnostics based on score.
Underwriting Risk Adjustment: Feeds updated scores to dynamic pricing or policy adjustment modules.
Consent-Driven Data Sharing: Allows users to opt-in/out for personalized insights.
Eligibility Logic
Decision Logic Flow:
The agent follows a rule-plus-AI decision sequence, blending predictive analytics with health rule frameworks. It ensures every score reflects recent behavior, evolving health patterns, and policy impact.
Key Logic Pathways Followed:
Health Data Availability Check: → If insufficient data, request wearables or self-report input.
Baseline Health Score Establishment: → Uses initial check-up or claims to set a personal benchmark.
Ongoing Data Sync: → Continuously ingests wearable, EHR, and lifestyle data.
Anomaly Detection: → Flags vitals or behavioral deviations beyond safe thresholds.
Condition Prediction Modeling: → Applies AI models to forecast onset of high-risk conditions.
Risk Scoring: → Updates individual score daily or weekly depending on data flow.
Risk Tier Classification: → Categorizes into green (low), yellow (moderate), red (high) segments.
Intervention Trigger: → Initiates care guidance, alerts, or case manager assignment as per tier.
Data Sharing & Consent Check: → Validates permissions before integrating score with other systems.
Feedback Loop: → Captures intervention results to improve model accuracy and outcomes.
