Auto-Approval Prior Authorization ML | Healthcare Machine Learning

🎯 Project Objective

This project addresses inefficiencies in veterinary prior authorization processes by using machine learning to predict which requests can be safely auto-approved, reducing manual reviews while maintaining clinical appropriateness.

📊 Key Performance Metrics

72.5%

Approval Rate

98%

Precision (Conservative)

~1,900

Training Records

29%

Auto-Approval Rate

🔧 Technical Implementation

Machine Learning Models Evaluated:

Logistic Regression: Baseline linear model
Random Forest: Selected for high F1 score and interpretability
XGBoost: Gradient boosting for comparison

✨ Key Features

🤖 ML Prediction Engine

Random Forest model with engineered features for accurate predictions

📈 Feature Engineering

Prior approvals, claims history, provider patterns, and time-based features

⚡ Automated Pipeline

Seamless integration with existing rule-based systems

🎯 Threshold Optimization

Configurable decision thresholds balancing automation and safety

📋 Model Performance

Threshold	Precision	Recall	F1 Score	Auto-Approval Rate
0.47 (Balanced)	88%	86%	0.87	~74%
0.90 (Conservative)	98%	42%	0.58	~29%

🚀 Getting Started

Clone the repository and explore the machine learning implementation:

git clone https://github.com/elenafmoseyko/Auto-Approval-Prior-Authorization-ML.git

📁 View Source Code 📓 View Jupyter Notebook

🏥 Use Cases & Applications

Healthcare Automation: Streamline prior authorization workflows
Insurance Processing: Reduce manual review burden
Clinical Decision Support: Maintain appropriateness while increasing efficiency
Veterinary Care: Specialized for pet healthcare authorization

🐾 Auto-Approval Prior Authorization ML