Address Matching

Overview

An address matching system designed to ensure the consistency of healthcare provider directories. This project addresses the critical challenge of maintaining accurate provider information across multiple healthcare databases, which is essential for patient care coordination, insurance verification, and healthcare system efficiency.

Key Features

• Fuzzy Address Matching: Handles variations in address formatting and typos
• Locality Sensitive Hashing (LSH): Efficient similarity search for large datasets
• Data Standardization: Normalizes addresses to consistent formats
• Duplicate Detection: Identifies and merges duplicate provider entries
• Confidence Scoring: Provides match confidence levels for manual review
• Scalable Processing: Handles large healthcare provider datasets efficiently

Technical Implementation

The system employs advanced data science techniques:

• Address Parsing: Extracts and standardizes address components (street, city, state, ZIP)
• LSH Implementation: Uses MinHash and Jaccard similarity for efficient comparison
• Fuzzy String Matching: Implements Levenshtein distance and phonetic matching
• Data Validation: Cross-references with authoritative address databases
• Performance Optimization: Vectorized operations using Pandas for speed
• Quality Metrics: Tracks matching accuracy and system performance

Healthcare Impact

This system addresses critical challenges in healthcare data management:

• Provider Directory Accuracy: Ensures patients can find correct provider information
• Insurance Verification: Improves accuracy of provider network verification
• Care Coordination: Enables better coordination between healthcare providers
• Regulatory Compliance: Helps maintain compliance with healthcare data standards
• Cost Reduction: Reduces administrative overhead in provider management

Data Science Challenges

The project tackles complex data science problems:

• Data Quality: Handles inconsistent and incomplete address data
• Scalability: Processes millions of provider records efficiently
• Accuracy vs. Speed: Balances matching accuracy with processing speed
• Edge Cases: Handles unusual address formats and international addresses
• False Positives: Minimizes incorrect matches that could impact patient care

Results & Impact

The system achieved significant improvements in healthcare data quality:

• Duplicate Reduction: Identified and merged thousands of duplicate provider entries
• Data Accuracy: Improved address consistency across provider directories
• Processing Speed: Reduced manual review time by 80%
• Cost Savings: Reduced administrative costs for provider directory maintenance
• Patient Experience: Improved accuracy of provider search results