Emergency Medicine Provider Automates 20 Million Medical Records

The Challenge

A leading provider of emergency medicine, hospitalist medicine, critical care, and post-acute care services operates across 200 practice locations throughout the United States. Serving approximately 3 million patients annually, the organization generates an enormous volume of medical data that must be processed, standardized, and integrated for billing, compliance, and continuity of care.

The core challenge was daunting: each of the 200 hospital partners uses different electronic health record (EHR) systems, each with its own data formats, export methods, and transmission protocols. This created several critical problems:

• Manual data extraction and reformatting consumed thousands of staff hours
• Inconsistent data formats led to billing errors and compliance risks
• Delays in processing impacted revenue cycle management
• Staff were overwhelmed by repetitive, low-value data tasks
• No unified view of patient histories across facilities

The Solution

The DevOps team implemented Microsoft Power Automate with robotic process automation (RPA) capabilities to create an automated medical records processing pipeline. The solution automatically receives data from hospital partners via secure file transfer, processes it through intelligent workflows, and outputs standardized patient records.

The automated pipeline handles:

• Data Ingestion: Automated pickup of files from secure transfer locations across all hospital partners
• Format Recognition: Intelligent identification of source system and data format
• Data Transformation: Conversion of diverse formats into standardized structures
• Quality Validation: Automated checks for data completeness and accuracy
• Record Integration: Assembly of coherent patient histories from multiple sources
• Exception Handling: Routing of problematic records for human review

The Results

The Power Automate RPA implementation has fundamentally transformed the organization's data operations:

Implementation Approach

The implementation followed a phased approach that minimized disruption while maximizing learning:

• Phase 1 - Pilot: Started with 10 hospital partners representing the most common EHR systems
• Phase 2 - Expansion: Extended to additional partners, building a library of data transformation templates
• Phase 3 - Scale: Rolled out to all 200 locations with automated monitoring and exception handling
• Phase 4 - Optimization: Continuous improvement based on exception patterns and new data sources

Key Takeaways

• RPA Excels at Data Transformation: When dealing with multiple legacy systems, RPA can bridge gaps without requiring system changes
• Start with High-Volume Processes: Focus automation efforts where volume creates the greatest manual burden
• Build Exception Handling First: Plan for edge cases and build robust exception routing from the start
• Template-Based Scaling: Create reusable transformation templates to accelerate rollout to new data sources
• Measure Comprehensively: Track not just time savings, but also quality improvements and downstream impacts

Technology Stack

• Power Automate Cloud Flows: Orchestration and monitoring of the overall pipeline
• Power Automate Desktop Flows: RPA capabilities for interacting with legacy systems and file processing
• Secure File Transfer: SFTP integration for receiving data from hospital partners
• Data Quality Rules: Custom validation logic ensuring data integrity