Automated Healthcare Data
Validation Pipeline

Healthcare data often arrives in raw files containing wrong, missing, or outdated patient and hospital information. If this data is not accurate, it can lead to wrong medical decisions, patient safety risks, compliance failures, and heavy penalties for healthcare providers. Manually checking and fixing this data is slow, error-prone, and not scalable.

In this project, we developed an automated pipeline to validate and enrich healthcare data efficiently.

The system begins by ingesting raw healthcare CSV files, storing them securely, and generating structured search queries to cross-verify patient or facility data across the web. It classifies and filters websites to remove irrelevant sources, then scrapes targeted fields to extract metadata such as patient identifiers, facility details, and treatment information.

To ensure reliability, the pipeline compares scraped data against the original input, calculates match scores, and classifies results based on confidence. If discrepancies are detected, a correction recommendation engine suggests potential fixes, ensuring high data integrity for healthcare records.

Finally, the pipeline generates clean, validated outputs in CSV and JSON formats for downstream systems, with optional enhancements like classifier retraining, historical tracking, and confidence logging for continuous improvement.

This system helps healthcare organizations reduce manual verification efforts, improve data accuracy, and maintain compliance-ready records with minimal intervention.