Collections Optimization Manager
Experian's data-driven patient collections solution that helps revenue cycle management staff collect more patient balances based on comprehensive segmentation.
Powerful segmentation
- Increase self-pay collections by targeting patients accounts with high propensity-to-pay scores.
- Prioritize high-value accounts to save staff time and resources.
Dedicated support
- Get expert collections advice from consultants who seamlessly integrate with your team.
Advanced reporting
- Report collections success with comprehensive reports and dashboards.
- Benchmark performance, refine patient payment forecasts, and manage bad debt.
How our collections optimization solution works
How Advocate Aurora Healthcare has achieved double-digit increases in patient collections every year with Collections Optimization Manager.
Collections Optimization Manager helps Stanford Health Care reduce their cost to collect through managing vendor performance efficiently, automating assignment to bad debt and identifying uncollectable accounts in their AR.
Novant Health & Cone Health's 7:1 ROI & $14M patient collections success
On-demand webinar
St. Luke’s University Health Network
22% increase in average cash collections for St. Luke's University Health Network with Collections Optimization Manager.
Wooster Community Hospital
Adopting a tailored in-house collections strategy, Wooster significantly improved patient collections and satisfaction in just one year
Success at a glance: improved collections
Since working with Experian Health, Sanford has implemented a focused approach to managing accounts receivable (AR) by identifying patients with a certain propensity to pay.
Tackling healthcare labor shortages with automation
As healthcare labor shortages continue, how can providers maximize the return on their investment in automation?
Expert Q+A: how to optimize patient collections
See how providers are boosting collections with automated solutions and collections optimization tools as consumer behavior shifts back to pre-pandemic patterns.