Predict Risk, Optimize Resources, Improve Outcomes
Move from reactive to proactive care. HDIM identifies high-risk patients using HCC v28, machine learning models, and social determinants. Enable targeted interventions before crises happen.
Risk stratification cohorts dashboard
Resource Allocation is Reactive, Not Predictive
Healthcare organizations today allocate resources based on historical utilization, not predicted future risk. High-risk patients are identified only after expensive ER visits or readmissions occur. Case management targets the wrong populations.
Readmission identification: After event occurs (reactive)
Resource allocation efficiency: 40-50% waste on lower-risk groups
High-risk patient identification: <60% accuracy
Preventable readmissions: 30-40% of total
ML-Powered Predictive Risk Assessment
HDIM calculates HCC risk scores and applies machine learning models to predict readmission risk, ER utilization, and cost. SDOH factor analysis (housing, food insecurity, transportation) adds clinical context. Enables proactive intervention on truly high-risk cohorts.
HCC v28 risk adjustment scoring
ML readmission prediction (87% accuracy)
SDOH factor integration
Real-time cohort stratification
What Sets Us Apart
Clinically Informed Risk
HCC scores (claims-based) combined with ML predictions (EHR-based) and SDOH data create comprehensive risk picture.
87% Readmission Prediction Accuracy
ML models validated in production. AUC 0.91 for 30-day readmission prediction.
SDOH Integration
Address social determinants alongside clinical risk. Identify patients needing social services, not just medical intervention.
Risk Stratification Process
Collect Patient Data
HDIM ingests EHR data (diagnoses, encounters, medications), claims data, and SDOH information from community resources.
Calculate HCC Risk
Diagnoses mapped to HCC v28 categories. Risk scores calculated per CMS methodology. Stratified into quintiles.
Apply ML Models
Readmission, ED utilization, and cost models predict future risk. Models update with new data monthly.
Identify Cohorts
High-risk, medium-risk, and low-risk cohorts defined. SDOH factors overlay to identify intervention needs.
Assign Interventions
Automated workflows route patients to care management, social services, or monitoring. Track outcomes.
Stratification & Prediction Capabilities
Built on production ML models and CMS HCC v28
HCC v28 Risk Adjustment
Diagnoses mapped to 80+ HCC categories. Risk scores aligned with CMS payment models and value-based contracts.
87% Accurate Readmission Prediction
ML models trained on 500K+ patients. AUC 0.91 for 30-day readmission. Explainable feature importance.
SDOH Factor Integration
Housing, food security, transportation, isolation risk. Overlays with clinical risk for holistic view.
Cohort Segmentation
Stratify into 5-10 risk tiers based on HCC + ML scores. Customize cohorts for program eligibility.
Continuous Model Updates
Models retrained monthly. Performance tracked against actual outcomes. Automated alerts on model drift.
Cost & Utilization Prediction
Predict total cost of care, ED visits, and inpatient utilization. Enable budget forecasting.
Customer Success Stories
Customer Success
Large Health System (400-bed)
Challenge
Case management team spread thin across 250K patients. Targeting was reactive—managing patients after readmission.
Solution
HDIM risk stratification identified top 10% highest-risk patients. Targeted intensive case management, ED diversion, and social services.
Impact
23% Readmission Reduction in High-Risk Cohort
- ✓Readmissions prevented: 150 per year
- ✓Cost savings: $3.2M (at $20K per readmission avoidance)
- ✓Resource efficiency: 60% reduction in case management effort per outcome
- ✓Patient satisfaction: Up 35% (proactive care focus)
Customer Success
Medicare Advantage Plan
Challenge
Star ratings impacted by readmission rates. Manual risk identification from claims was 6+ weeks delayed.
Solution
Deployed HDIM to continuously score and update risk. Coordinated with delegated health systems on high-risk members.
Impact
1.5 Star Rating Improvement (Readmission Domain)
- ✓Readmission rate: 24.2% → 19.8%
- ✓Quality bonus impact: +$8M payout
- ✓Member engagement: Real-time high-risk alerts to providers
Customer Success
Community Accountable Care Organization
Challenge
Multi-practice ACO lacked unified risk visibility. Each practice used different stratification methods.
Solution
Single HDIM instance providing standardized risk scores across 20 clinics. SDOH data enriched from community partners.
Impact
Unified Risk Management Across 250K Members
- ✓Risk score consistency: 99.2% agreement across clinics
- ✓SDOH identification: 18K at-risk members for social services
- ✓Intervention impact: $1.4M cost savings (first year)
- ✓Provider adoption: 92% (daily dashboard access)
Technical Specification
Risk Models
Data Integration
API Endpoints
Performance
Compliance & Transparency
Explainable Predictions
SHAP values show which factors drive readmission risk. Clinicians understand and trust predictions.
Model Governance
Version control, validation protocols, and performance monitoring. Monthly model review with clinical team.
Bias Monitoring
Algorithmic fairness checks. Performance validated across demographic groups. Alert on fairness degradation.
Data Privacy
HIPAA compliant data handling. No patient-level data exported. Risk scores and predictions only.
Pricing & ROI
Pricing Model
Per-Patient-Per-Year + Implementation
Typical Investment
$12-18 per patient/year, plus $50K-$100K implementation
Typical health system (400 beds): $1.2M-$1.8M/year
ROI: 3-6 months from readmission prevention alone
Medicare Advantage: $8M+ annual bonus impact
Related Capabilities
Care Gap Detection
Identify and close care gaps in real-time using FHIR queries and automated detection across 52+ HEDIS measures.
Analytics & Reporting
Real-time quality dashboards. Drill-down analytics. Custom reports. Executive-to-clinical visibility.
FHIR Integration
Connect to any FHIR R4-compliant EHR in minutes. Epic, Cerner, Athena, and 20+ others. No data movement.