Healthcare Analytics Dashboard - Predictive Modeling for Hospital Readmissions

Data Overview

Explore the diabetes dataset powering our 30-day readmission predictions.

Dataset Snapshot

Feature	Type	Missing	Unique
Age	Categorical	0%	10
Race	Categorical	2%	8
Diagnoses	Integer	0%	16
Medications	Integer	0%	25

Data Quality Report

✓ 101,766 patient encounters across 130 U.S. hospitals
✓ 50+ clinical and administrative features
! 2.3% missing values handled via KNN imputation
✓ 11% readmission rate within 30 days

Raw Data Capture

Original EHR extracts before cleaning and transformation.

Data Dictionary

Comprehensive schema and definitions for every field.

Analytics Pipeline

Automated ETL ensuring daily freshness and quality checks.

Model Performance

Benchmarked against industry standards with explainable insights.

ROC & Precision-Recall Curves

AUC = 0.84

Validation ROC

Sensitivity 0.79

Specificity 0.82

Calibration Plot

Brier = 0.12

Well-calibrated probabilities

Predicted probabilities align closely with observed readmission rates across risk deciles.

Confusion Matrix

8,432

True Negatives

1,218

False Positives

987

False Negatives

2,765

True Positives

Precision 0.69

Recall 0.74

Model Comparison

XGBoost outperformed Random Forest and Logistic Regression across all metrics.

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Real-time Monitoring

Performance drift alerts trigger when AUC drops below 0.80.

Feature Stability

Top 10 features remain consistent across validation windows.

Risk Stratification Tools

Identify high-risk patients in real time with explainable risk scores.

Patient Risk Calculator

Predicted Risk

68.3%

High Risk

Top risk factors: Age 70-80, LOS >3 days, 7+ diagnoses

Risk Buckets & Interventions

Low Risk

<30% readmission probability

Standard care

Moderate Risk

30-60% readmission probability

Discharge call

High Risk

>60% readmission probability

Care coordination

Air quality monitor shows levels of pollutants

Real-time Alerts

Instant notifications when patients cross risk thresholds.

Telemedicine Integration

Risk scores embedded directly into virtual care platforms.

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Interactive Workshops

Train staff to interpret risk scores and take action.

Interactive Dashboard

Actionable insights in real time for clinicians and administrators.

Live KPI Tiles

12.4%

Readmission Rate

847

Total Discharges

126

High-Risk Patients

.2M

Est. Savings

Trend Analysis

-2.3%

Readmission reduction vs last month

30-day avg 11.8%

Target 10.5%

Gap -1.3pp

Top Risk Factors

Length of Stay >5 days

78%

Age 70-80

65%

8+ Diagnoses

58%

Medications >12

52%

Power BI Integration

Native embedding with row-level security and scheduled refresh.

Drill-through Actions

Click any metric to explore underlying patient-level details.

Mobile Optimized

Full functionality on tablets and phones for bedside access.

Technical Details

Deep dive into architecture, dependencies, and reproducibility.

Tech Stack

Languages

Python 3.10, SQL, R

Core Libraries

scikit-learn, pandas, XGBoost

Visualization

matplotlib, seaborn, Power BI

Data Storage

PostgreSQL, Parquet, CSV

Orchestration

Apache Airflow, cron

Deployment

Docker, Azure Container Apps

Model Pipeline

1

Data Ingestion

Daily ETL from EHR via HL7 FHIR APIs
2

Pre-processing

Imputation, encoding, outlier handling
3

Feature Engineering

LOSRatio, medCount, diagGroup
4

Inference

XGBoost predicts readmission probability
5

Post-processing

Calibration, risk stratification, SHAP

Reproducibility Checklist

✓ requirements.txt pinned to minor versions
✓ Random seeds fixed across train/val/test splits
✓ Docker image tagged with Git commit SHA
✓ MLflow tracking for every experiment run
✓ Data snapshot hashes stored in DVC

Latency Budget

P99 inference <200 ms on 8 vCPU container.

CI/CD Pipeline

GitHub Actions runs unit, integration, and load tests on every PR.

Monitoring

Prometheus metrics: latency, errors, drift, business KPIs.

Deployment

Zero-downtime rollout with automated rollback and monitoring.

Architecture Overview

Containerized Microservice

Docker image <400 MB, based on python:3.10-slim

Orchestration

Azure Container Apps with KEDA autoscaling 2-20 replicas

API Gateway

Azure API Management with rate limiting and JWT auth

Data Layer

PostgreSQL with pgvector for similarity search

CI/CD Pipeline

Build

2-3 min

Test

4-6 min

Deploy

1-2 min

GitHub Actions triggers on push to main; blue-green deployment with automated rollback on health-check failure.

Security Controls

🔒 Network isolation via Azure VNet and NSGs
🔒 Managed Identity for keyless authentication
🔒 Data encrypted at rest with CMK and TLS 1.3 in transit
🔒 Weekly vulnerability scans and dependency updates

Canary Releases

Gradual rollout to 5%, 25%, 100% with automatic rollback on error rate >1%.

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Feature Flags

Toggle model versions and UI components without redeploying code.

SLA & Uptime

99.9% monthly uptime with 15-minute RTO and 5-minute RPO targets.