Healthcare

Clinical Analytics Platform for Regional Health System

Major Regional Health System

Challenge

A 12-hospital health system needed to predict patient readmission risk to enable proactive care management and reduce penalties under the Hospital Readmissions Reduction Program. Existing analytics were retrospective and siloed across facilities.

Outcome

Deployed ML-powered platform predicting 30-day readmission risk with 89% accuracy, enabling care teams to intervene proactively. Reduced all-cause readmissions by 18% and avoided $4.2M in annual CMS penalties.

Services Delivered

AI & Data Science
Product Engineering
Cloud & DevOps
18%
Reduction in readmissions
89%
Prediction accuracy
$4.2M
Annual savings

The Challenge

The health system faced multiple challenges:

  • Fragmented data - Patient data scattered across 5 different EHR instances
  • Reactive care - No systematic identification of high-risk patients
  • Resource constraints - Limited data science capability in-house
  • Integration complexity - Need to integrate with Epic EHR workflows
  • Compliance - HIPAA and patient privacy requirements

Our Approach

Phase 1: Data Foundation (Weeks 1-4)

Built a unified patient data warehouse:

  • Integrated 5 Epic instances via HL7 and FHIR APIs
  • Standardized clinical terminologies (SNOMED, LOINC)
  • Implemented HIPAA-compliant data lake on AWS
  • Created data quality monitoring and remediation workflows

Phase 2: Model Development (Weeks 5-10)

Developed and validated readmission risk models:

  • Analyzed 3 years of historical data (250K+ admissions)
  • Built gradient boosting models with 150+ features
  • Validated across demographic subgroups for fairness
  • Achieved 89% AUC-ROC on held-out test set

Key Features:

  • Prior hospitalizations and ED visits
  • Chronic conditions and comorbidity indices
  • Social determinants of health (SDOH)
  • Medication adherence patterns
  • Discharge disposition and support

Phase 3: Clinical Integration (Weeks 11-16)

Integrated predictions into clinical workflows:

  • Real-time risk scoring at discharge
  • Care coordinator dashboard with patient prioritization
  • Epic integration via FHIR for seamless access
  • Automated alerts for high-risk patients
  • Mobile app for care teams

Phase 4: Deployment & Monitoring (Weeks 17-20)

Launched with comprehensive monitoring:

  • Deployed on HIPAA-compliant Kubernetes cluster
  • Implemented model monitoring for drift detection
  • Built operational dashboards for IT and clinical teams
  • Established model retraining pipeline
  • SOC 2 Type II audit preparation

Technical Architecture

Epic EHR (5 instances)
    ↓
HL7/FHIR Integration Layer
    ↓
Data Lake (AWS S3)
    ↓
Feature Engineering Pipeline
    ↓
ML Model (SageMaker)
    ↓
Prediction API (EKS)
    ↓
Care Coordinator Dashboard
Epic Integration (SMART on FHIR)

Stack:

  • Data Pipeline: Python, Apache Airflow, AWS Glue
  • ML Platform: SageMaker, MLflow, XGBoost
  • API: FastAPI, PostgreSQL, Redis
  • Frontend: React, TypeScript
  • Infrastructure: Terraform, Kubernetes, AWS

Results

Clinical Impact

  • 18% reduction in all-cause 30-day readmissions
  • 24% reduction in heart failure readmissions
  • 15% improvement in care transition completion rates

Operational Impact

  • 500+ high-risk patients identified monthly
  • 200+ care coordinator interventions weekly
  • 92% care team adoption rate
  • <200ms API latency for real-time scoring

Financial Impact

  • $4.2M annual savings from avoided CMS penalties
  • $1.8M reduction in readmission-related costs
  • ROI of 380% in first year

Key Success Factors

  1. Clinical engagement from day one - Care coordinators involved in model design
  2. Seamless integration - Risk scores in existing EHR workflows
  3. Continuous monitoring - Model performance tracked and retrained monthly
  4. Focus on actionability - Predictions paired with recommended interventions
  5. Privacy by design - HIPAA compliance built in, not bolted on

Long-Term Partnership

Following initial success, we expanded the platform:

  • Sepsis prediction - Early warning system for sepsis onset
  • ED diversion - Identifying patients suitable for observation unit
  • Care gap closure - Prioritizing preventive care outreach
  • Population health - Risk stratification for value-based contracts
"NABiM didn't just build a model - they built a production system that our care teams actually use. The impact on patient outcomes has been significant."
Dr. Sarah Chen
Chief Medical Information Officer