Development of an Automated Healthcare Data Extraction and Normalization Platform

The client operates over 300 healthcare facilities, requiring the extraction of thousands of patient records daily from multiple sources with varying formats. This results in data duplicates, inconsistencies, and extensive manual effort to identify and correct errors, which hampers operational efficiency and data quality.

A large multi-facility healthcare provider managing diverse patient data sources across numerous clinics and hospitals, aiming to streamline data management and improve data accuracy.

• Implement a scalable ETL platform to efficiently extract, transform, and load patient data from diverse sources.
• Reduce manual data correction efforts by automating detection and correction of data inconsistencies and duplicates.
• Enable fast data downloads through multi-server processing to support rapid data availability.
• Improve data accuracy and consistency to support better analytics and decision-making.
• Automate error alerts for key identifiers such as social security numbers, dates of birth, and healthcare IDs to ensure data integrity.

• Multi-Server Processing for scalable, parallel data workflows to accelerate data downloads.
• Data Normalization algorithms that link and merge data from various sources into unified patient records, eliminating duplicates.
• Automated Error Detection and Alerts for key identifiers to promptly notify users of data anomalies.
• Secure data handling compliant with healthcare industry standards.
• User-friendly interface for monitoring data processing status, errors, and alerts.
• Integration capabilities with existing healthcare data systems and warehouses.

• Multiple healthcare data sources with varying formats
• Centralized data warehouse or database for storage
• Notification systems for alert dissemination
• Existing healthcare management systems for data sync

• Scalability to support processing data from over 300 healthcare facilities.
• High performance with rapid data downloads and processing times.
• Data security and compliance with healthcare data privacy regulations.
• System uptime and reliability to ensure continuous data availability.
• Automated reporting and alerting capabilities.

The new system aims to significantly reduce manual data correction efforts, leading to streamlined operations and resource savings. It is expected to enable faster data extraction and processing, improve data accuracy by automated error detection, and support better decision-making through high-quality data. Overall, the platform will foster more efficient healthcare data management, with measurable improvements in processing times and data integrity, similar to reducing manual work and error detection time as observed in the original case.