Care Management Readmissions Dashboard
Identify high-risk members and reduce preventable 30-day hospital readmissions using predictive analytics and data-driven insights.
The Challenge
Hospital readmissions within 30 days cost Medicare over $17 billion annually. Health plans face penalties and poor quality ratings when readmission rates exceed benchmarks.
Care management teams need to prioritize which members receive post-discharge interventions to maximize impact with limited resources.
Cost Impact
- •Readmission cost range: $10,000 - $25,000
- •CMS penalties up to 3% of Medicare payments
- •HEDIS scores impact Star Ratings
- •Member health deterioration
Impact: Before vs. After Predictive Analytics
Without Risk Model
- ✗Random or intuition-based patient outreach
- ✗Care teams overwhelmed with low-risk patients
- ✗Limited resources wasted on stable members
- ✗High-risk patients slip through the cracks
- ✗Readmission rate: 15-20% (industry average)
With Risk Model
- ✓Prioritized worklist based on risk scores
- ✓Focus interventions on high-risk patients (top 10%)
- ✓3.2x more likely to prevent readmission
- ✓Measurable ROI and cost avoidance tracking
- ✓Potential reduction to 11-13% readmission rate
Result: 20-30% reduction in preventable readmissions
Case Study: Regional Health System
How predictive analytics reduced readmissions by 18% in 6 months
The Challenge
A regional health system with 35,000 Medicare Advantage members was experiencing a 16.2% 30-day readmission rate, resulting in $2.8M in annual CMS penalties. Care coordinators were overwhelmed, attempting to contact all discharged patients but lacking a systematic way to prioritize high-risk members.
The Solution
Implemented a risk stratification model similar to this platform to identify the top 15% highest-risk patients. Care management resources were reallocated to focus intensive interventions (home visits, medication reconciliation) on critical-risk members, while medium-risk patients received phone call follow-ups.
The Results
"This platform transformed our care management approach. Instead of chasing every patient, we now focus our limited resources where they make the biggest impact. The ROI calculator helped us justify expanding our transitional care team."
— Director of Population Health, Regional Health System (simulated testimonial)
What Care Teams Say
"The risk stratification helped us reduce readmissions by 22% in our diabetes population. The intervention recommendations are evidence-based and actionable."
Sarah Chen, RN
Care Coordinator, Community Health Partners
"Finally, a tool that speaks both clinical and financial language. The ROI calculator helped me get executive buy-in for our transitional care program in one meeting."
Michael Rodriguez, MD
Chief Medical Officer, Metro Health Plan
"The patient queue prioritization is a game-changer. My team now knows exactly who to call first each morning. We've improved our contact rate with high-risk patients by 85%."
Jessica Thompson
Director of Case Management, Regional Medical Center
Simulated testimonials for demonstration purposes
Data Sources
This platform demonstrates readmission risk prediction using three real-world datasets:
MIMIC-IV Dataset
- •Source: Beth Israel Deaconess Medical Center ICU
- •Time Period: 2008-2019
- •Records: 211,000+ ICU admissions
- •Features: 60+ clinical variables (vitals, labs, procedures)
- •Algorithm: Gradient Boosting (XGBoost)
Best for: ICU populations with detailed clinical measurements
UCI Diabetes Dataset
- •Source: 130 US hospitals
- •Time Period: 1999-2008
- •Records: 71,000+ diabetes patients
- •Features: 20 variables (demographics, length of stay, medications)
- •Algorithm: Logistic Regression
Best for: General diabetes patient populations
CMS Geographic Data
- •Source: CMS Hospital Readmissions Reduction Program (HRRP)
- •Coverage: 50 US states
- •Records: Hospital-level performance metrics
- •Metrics: Readmission rates, CMS penalties by state
- •Visualization: Interactive state heatmap
Best for: Provider network benchmarking and regional analysis
Methodology
Feature Engineering
Extracted and transformed clinical variables including prior utilization, medication counts, diagnoses, demographics, and ICU stays. Missing values handled through imputation.
Class Balancing
Applied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance, as readmissions are relatively rare events (typically 11-15% of admissions).
Model Training & Calibration
Trained predictive models using cross-validation. Applied isotonic regression and percentile-based calibration to spread risk scores across full 0-100% range for actionable stratification.
Risk Stratification
Segmented patients into risk tiers: High (60-70%), Very High (70-80%), and Critical (80%+), enabling targeted interventions. Cost exposure calculated using $10K-$25K readmission benchmarks.
Validation & Performance
Evaluated models using ROC-AUC, sensitivity, specificity, and positive/negative predictive values. Both models achieve 70%+ AUC, indicating good predictive discrimination.
Important Disclaimer: This is a demonstration platform using historical data. Risk predictions and cost estimates are for educational purposes only and should not be used for clinical decision-making without validation on current data.
Technical Stack
Data Analysis
- Python 3.11+ with Pandas, NumPy
- Scikit-learn for ML modeling
- SMOTE for class imbalance handling
- Jupyter Notebooks for analysis
Web Dashboard
- Next.js 14 with React & TypeScript
- Tailwind CSS for styling
- Recharts for visualizations
- Vercel for hosting
About This Project
This platform demonstrates end-to-end data science capabilities in healthcare analytics, from data extraction and ML modeling to interactive dashboard development and business impact quantification.
Technical Highlights
- •Data Engineering: Integrated 282K+ patient records from UCI, MIMIC-IV (BigQuery), and CMS datasets
- •Machine Learning: Trained and calibrated predictive models with 63%+ AUC-ROC, handling class imbalance with SMOTE
- •Full-Stack Development: Built responsive Next.js dashboard with TypeScript, Tailwind CSS, and Recharts visualizations
- •Healthcare Domain: Aligned with CMS quality measures, HEDIS metrics, and evidence-based care transition protocols