The Problem
Provider data accuracy is often seen as primarily an issue of compliance and member satisfaction. This limited view can cause health plans to overlook significant financial benefits of accurate provider data such as:
- Closing crucial leakage points for quality and risk-related revenue
- Limiting vendor count for provider data management and governance
- Reducing administrative costs
The current approach to solving the pervasive problem of inaccurate provider data requires providers to continuously inform payers of their ever-changing practice and operational details. Providers communicate those details with one of several provider data clearinghouses, which helps to reduce their burden, but falls short of solving the problem. To solve this problem, Curatus offers an effective healthcare data solution in our SaaS products ProviderLenz and ProviderClenz.
Download the ProviderLenz PDF
Our Solutions?
Introducing ProviderClenz and ProviderLenz
A robust data engine that solves for many of the inaccurate data challenges that healthcare organizations face. A proprietary healthcare data solution that:
- Improves consumer experience by providing timely and accurate provider directories
- Reduces consumer frustration when searching for a provider within a defined network
- Matches consumers and provider while assessing network capacity and performance
- Reduces fines, penalties, and increased costs associated with inaccurate data
- Identifies gaps in care and enhances care management opportunities
Use Cases
Risk Analytics
Quality Metrics
Encounter Reporting
Provider Directory
Network Adequacy
Contracting
Claims Adjudication
Care Management
Practice Analytics
Shared Savings Reconciliation
Use Cases
Risk Analytics
Quality Metrics
Encounter Reporting
Provider Directory
Network Adequacy
Contracting
Claims Adjudication
Care Management
Practice Analytics
Shared Savings Reconciliation
Additional Areas that Benefit from ProviderClenz & ProviderLenz
Provider Data Management
- Collect information from active data flows
- Learn from each provider transaction and interaction
- Machine learning to establish patterns and discover new relationships & attributes
- Publish context-appropriate provider datasets
- Supply provider information updates to application systems
- In-stream corrections to provider-referencing datasets
Provider Directories
- Collect provider information from many sources, internal and external
- Clean and scrub provider data from each source
- Relate the many provider representations to individual identities and de-duplicate
- Constantly monitor all sources for new information and hints of change
- Prompt providers for confirmation of change through e-mail, fax, and telephonic outreach
- Collect and confirm new information through provider portal
- Publish updated information via download and file transfer
- Live APIs to power on-line provider queries
Network Adequacy
- Maintain jurisdictional adequacy rules
- Monitor provider credentials and licensing
- Regression analysis to determine optimal representation
- Notifications to network management of inadequacies
- Publish updates to Medicare, Medicaid, Exchange public directories
- Collect and confirm new information through provider portal