A regional healthcare system with 500+ employees was drowning in manual Excel reporting. Revenue cycle data lived in four disconnected systems, and nobody had a clear picture of financial performance. We changed that.
The healthcare system had grown through a mix of organic expansion and acquisitions, each bringing its own systems and processes. The result: revenue cycle data was scattered across an EHR, a billing platform, a claims management system, and a scheduling tool — none of which talked to each other.
Finance staff spent an estimated three days at the end of every month manually pulling data from each system, reconciling it in Excel, and building reports for leadership. The numbers rarely matched between departments, and by the time reports were ready, they were already outdated.
We started where we always start — by talking to the people who actually use the data. Finance, operations, revenue cycle managers, and the C-suite each had different needs but the same underlying problem: they couldn't get reliable numbers without days of manual work.
Spent two weeks mapping every data source, understanding the business logic embedded in those Excel spreadsheets, and documenting the "tribal knowledge" that only lived in people's heads.
Designed and built a centralized Azure SQL data warehouse with a dimensional model optimized for revenue cycle analytics — charges, payments, denials, AR aging, and payer mix.
Built Azure Data Factory pipelines to pull data from all four source systems on automated schedules, with Python scripts handling the complex transformation and reconciliation logic.
Created 12 Power BI dashboards tailored to each audience — executive summaries for the C-suite, operational details for managers, and drill-down analytics for the finance team.
Everything was built on Azure for HIPAA compliance, with proper access controls, audit logging, and data governance from day one. No shortcuts with protected health information.
Within 10 weeks of project start, the healthcare system had a fully operational data warehouse with automated pipelines and 12 production dashboards. But the numbers tell the real story.
Revenue leakage identified in the first 90 days — from missed charges, coding errors, and under-billed procedures that had been invisible in the manual reporting process
Monthly reporting that used to consume three full days of the finance team's time now generates automatically — available in Power BI within minutes of month close
Purpose-built dashboards serving finance, operations, and C-suite — each seeing the metrics that matter to them, from a single source of truth
Denial tracking and AR aging now update daily instead of monthly — enabling proactive intervention instead of reactive firefighting