Risk Adjustment and Analytics Services
Analytics and risk adjustment are hot topics due to the expenses associated with value-based care, government regulations, and CMS programs. Our Revenue & Payment Integrity solution attempts to provide organizations with actionable data to improve their risk documentation outcomes.
KLAS is a data-driven company on a mission to improve the world’s healthcare by enabling provider and payer voices to be heard and counted. Working with thousands of healthcare professionals, KLAS collects insights on software, services and medical equipment to deliver reports, trending data and statistical overviews. KLAS data is accurate, honest and impartial. The research directly reflects the voice of healthcare professionals and acts as a catalyst for improving vendor performance. To learn more about KLAS and the insights they provide, visit KLASresearch.com
Pareto's Customer Experience Pillars
- Culture: A. Excel at the intersection of robust analytics, industry-leading technology, and deep business expertise.
- Loyalty: A. 93% of our clients would buy our products again.
- Operations: B+. Solving our clients' most complex problems by delivering modern, AI-enabled solutions.
- Product: B+. Delivering unparalleled access to insights on a secure, web-based platform.
- Relationship: A. Market-leading business experts who partner with you to improve strategy and performance.
- Value: A. Evaluating revenue from all angles to ensure comprehensive and accurate financial performance.
Why Pareto?
Compliant
We focus on complete and accurate encounter data to mitigate compliance exposure.
Comprehensive
Evaluates revenue from all angles to ensure complete and accurate financial performance.
Flexible
Delivers unparalleled access to insights on a secure, web-based platform across all or select solutions.
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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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