Improve Risk-Based Revenue Accuracy
Oversight into the major factors contributing to revenue allows risk-bearing providers to ensure accurate reimbursement from private payers, CMS and/or state government. This starts with an assessment of data quality and value-based contracts to establish a baseline for subsequent analysis. Then we apply Pareto’s proprietary revenue integrity analytics to identify and prioritize gaps and recommend activities to improve performance.
The potential financial improvement opportunity identified by our Value-Based Reimbursement solution can be more than $8 million for 30K attributed lives.
How it Works
- Risk Coding & Documentation: Identify & prioritize undocumented risk gaps to focus coding improvement efforts & ensure compliance.
- Payer Data Validation: Verify data documented at encounter is completely & accurately transferred, submitted & accepted for regulatory reporting.
- Capitated Payment Evaluation: Validate member status determinations (e.g., MSP, ESRD, Dual Eligibility) to ensure accurate premium payments.
- Performance Improvement: Prioritized risk documentation improvement campaigns, actionable provider-level coding scorecards & proven payer-provider communication tools to improve risk, quality & clinical outcomes.
Why Pareto?
Payer Expertise
Our extensive experience working with payers on these activities gives us the insight necessary to facilitate data access and pinpoint issues contributing to lost revenue.
Root Cause Focus
We go beyond surface-level gap identification to uncover the root cause of revenue losses to resolve issues at the source.
Data on Demand
Pareto Hub offers unparalleled access to insights through a ready-to-analyze data environment, enabling users to build independent analytics in a self-service manner.
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A Powerful Foundation
Our Data Management Platform
Pareto’s Data Management Platform, the foundation to our solutions, is built around data lake, artificial intelligence, and open source big data technologies to allow us to effectively ingest data in all formats and types at scale, organize them with appropriate data governance mechanisms within our data catalog, and provide efficient configuration capabilities to democratize data access and analytics.
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