Case Study

Patient Readmission Risk Model.

Machine learning system that predicts 30-day hospital readmission risk, enabling proactive care interventions and reducing costly readmissions.

HealthcarePredictive AnalyticsHIPAA-Aware

Overview

A regional hospital network was facing rising costs from preventable 30-day readmissions, particularly among heart failure and COPD patients. They needed a predictive system that could flag high-risk patients before discharge, giving care teams time to intervene with targeted follow-up plans.

We built a machine learning pipeline that ingests EHR data — demographics, diagnosis codes, lab results, prior admissions, and social determinants of health — to generate a real-time risk score for every patient approaching discharge. The model integrates directly into the hospital’s existing care coordination workflow.

27%
Reduction in 30-day Readmissions
89%
Model Accuracy (AUC-ROC)
<3min
Risk Score Generation Time
$1.2M
Estimated Annual Savings

How It Works

1. Data Ingestion & Feature Engineering

  • Automated ETL pipeline pulls from Epic EHR system via HL7 FHIR APIs
  • 47 clinical features extracted including comorbidity indices, medication counts, length of stay, and prior utilization
  • Social determinant overlays from census and claims data
  • Real-time data validation and missing value imputation

2. Predictive Model

  • Gradient-boosted ensemble (XGBoost) trained on 3 years of historical discharge data (~45,000 encounters)
  • Explainable AI layer (SHAP values) so clinicians understand why a patient is flagged
  • Calibrated probability outputs, not just binary classification
  • Monthly model retraining with drift detection alerts

3. Clinical Integration

  • Risk scores surface directly in the Epic discharge planning workflow
  • Color-coded alerts: green (<20%), yellow (20-50%), red (>50% readmission probability)
  • Automated care plan suggestions based on top risk factors
  • Daily digest reports for care coordination managers

Architecture & Infrastructure

Data Layer

Epic FHIR APIs, HL7 integration, HIPAA-compliant data lake on AWS S3

ML Pipeline

SageMaker training, feature store, model registry, automated retraining

Inference

Real-time scoring API via Lambda, <3min end-to-end latency

Monitoring

CloudWatch dashboards, model drift detection, A/B testing framework

Performance

Sensitivity (Recall)

82%

Specificity

91%

Positive Predictive Value

74%

Model Retraining Cycle

Monthly

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