Overview:
The Patient Readmission Risk Predictor is an AI-driven healthcare analytics tool that predicts the likelihood of a patient being readmitted to the hospital within a specific timeframe (e.g., 30 days) after discharge. By analyzing medical history, demographics, diagnosis, treatment plans, and lifestyle factors, it helps healthcare providers take preventive actions to reduce readmissions and improve patient outcomes.

Key Features:

1. Patient Data Analysis – Processes electronic health records (EHRs), lab results, and previous admission history.

2. Risk Scoring System – Assigns a readmission probability score to each patient.

3. Machine Learning Prediction Model – Uses algorithms like logistic regression, random forest, or gradient boosting for accurate forecasting.

4. Customizable Risk Factors – Allows hospitals to define specific risk indicators based on specialty (e.g., cardiology, oncology).

5. Alerts & Notifications – Notifies doctors and nurses about high-risk patients in real-time.

6. Preventive Action Recommendations – Suggests personalized care plans, follow-up visits, or remote monitoring.

7. Data Visualization Dashboard – Displays trends, readmission statistics, and hospital performance metrics.

8. Compliance with HIPAA & Data Privacy – Ensures secure handling of sensitive patient information.

Technology Stack:

• Backend: Node.js / PHP / Java (for data processing and model integration)

• Frontend: HTML, CSS, Bootstrap, JavaScript (for interactive dashboard)

• Database: MySQL / PostgreSQL (to store patient records and model results)

• AI/ML: Python (Scikit-learn, TensorFlow, or PyTorch for model training)

• Integrations: Hospital EHR systems, API connections for lab reports

Use Cases:

• Hospitals & Clinics – Identify high-risk patients before discharge.

• Insurance Companies – Reduce claim costs by supporting preventive care.

• Remote Health Services – Monitor and assist discharged patients at home.

Expected Outcome:
A proactive healthcare tool that lowers readmission rates, saves hospital costs, and ensures patients receive better follow-up care after discharge.