Ampera Technologies AI Driven Company

Healthcare Revenue Cycle Provider

Industry

Healthcare – Revenue Cycle & Payment Integrity

Industry

Healthcare – Revenue Cycle & Payment Integrity

Engagement Type

Enterprise Data Engineering & Cloud Modernization

Engagement Type

Enterprise Data Engineering & Cloud Modernization

Scope

Migration from legacy SQL/flat-file reporting to cloud-based analytics

Scope

Migration from legacy SQL/flat-file reporting to cloud-based analytics

Geography

Multi-state U.S. hospital network

Geography

Multi-state U.S. hospital network

Data Platform Modernization for Healthcare Revenue Cycle Provider

Reporting processes relied on siloed SQL scripts and manual spreadsheet workflows.
Data refresh cycles took 5–7 days, delaying operational and financial decision-making.
Lack of a unified data model across EHR systems, payer responses, and denial codes limited visibility.
Executives required clear KPIs on expected versus paid amounts, denials, underpayments, and payer behavior.

Reporting processes relied on siloed SQL scripts and manual spreadsheet workflows.
Data refresh cycles took 5–7 days, delaying operational and financial decision-making.
Lack of a unified data model across EHR systems, payer responses, and denial codes limited visibility.
Executives required clear KPIs on expected versus paid amounts, denials, underpayments, and payer behavior.

Designed and deployed a cloud-based lakehouse architecture integrating EHR, clearinghouse, claims, and ERA data.

Built automated ETL/ELT pipelines with quality and exception monitoring.

Established canonical data models for CPT codes, contract terms, denial categories, reason codes, and payments.

Delivered interactive Power BI dashboards for CFO, COO, and RCM directors with drill-downs by facility, payer, specialty, and encounter type.

Reduced data refresh cycles from 5–7 days to near real-time (hourly).
Uncovered approximately 18% revenue leakage from underpayments and avoidable denials within the first 30 days.
Improved follow-up prioritization accuracy by 2.4× using AI-driven payer tendency scoring.
Empowered leadership with predictive insights into monthly cash flow and denial risk.

Reduced data refresh cycles from 5–7 days to near real-time (hourly).
Uncovered approximately 18% revenue leakage from underpayments and avoidable denials within the first 30 days.
Improved follow-up prioritization accuracy by 2.4× using AI-driven payer tendency scoring.
Empowered leadership with predictive insights into monthly cash flow and denial risk.

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