Data Warehouse Case Study

AI-Powered Revenue Recovery and Data Integration

Business Problem:

Our multi-facility healthcare client suspected that a third-party billing firm was missing revenue due to fragmented data from their Electronic Health Record (EHR), billing system, and clearinghouse.

Our AI-Infused Approach:

Smart Data Consolidation:

• Combined data from the EHR, billing system, and clearinghouse into one central data warehouse.
• Used AI to map and validate data, making sure all formats work together smoothly.

Clear Data Analysis:

• Merged the consolidated data to create a complete view of patient appointments, CPT codes, and billing details.
• Built simple queries and dashboards to quickly spot issues like unfiled claims or coding errors.

Automated Discrepancy Detection:

• Deployed AI to automatically compare billed CPT codes against clearinghouse records to find errors.
• Generated easy-to-understand reports that showed where revenue was being lost, allowing the team to act fast.

Example Timeline (Initial Increment):

• Phase 1: Discovery & Assessment (1 week): Identify fragmented data sources and assess gaps between EHR, billing, and clearinghouse systems.
• Phase 2: Data Consolidation & Validation (2 weeks): Integrate sources into a central data warehouse and validate data consistency using AI.
• Phase 3: Analysis & Dashboarding (2 weeks): Create dashboards and queries to surface CPT code issues and billing errors.
• Phase 4: Automation & Reporting (2 weeks): Deploy AI-driven discrepancy detection and generate actionable revenue recovery reports.

Outcome:

Our AI-powered approach uncovered an estimated $1–2 million in missed revenue. This solution not only improved billing accuracy but also created a solid, scalable system for ongoing data-driven improvements.