Optimized account targeting
Maximized recovery
AI-driven predictions
$20MM impact
The challenge
Optimizing self-pay collections in healthcare
Rising healthcare costs are placing a greater financial burden on insured employees, increasing their self-pay responsibilities, including co-pays, coinsurance, deductibles, and out-of-pocket expenses. Hospitals face significant challenges in collecting these payments, which account for 6.1% of all services, according to the American Hospital Association.
Multi-specialty practices recover only 56.6% of receivables within the first 30 days, while many hospitals—especially non-profits—struggle with rising bad debts, weakened capital access, and downgraded credit ratings. To address these challenges, a leading multi-specialty healthcare provider, serving eight million patients annually, sought to leverage analytics to improve the collectability of self-pay medical expenses.
Key challenges
Higher out-of-pocket expenses hinder collections
Only 56.6% of accounts are collected within 30 days
Non-profit hospitals struggle with unpaid bills, impacting financial stability
A scalable analytical approach was required to improve collections
The solution
Optimized collections with AI
Smart segmentation
Segmented accounts by payment potential
Identified discounts and plans
Improved collection
Predictive analytics
Analyzed 50+ data points
Predicted payment behavior
Enabled smart decisions
Implementation approach
1
AI-powered models
Predicted payment and timing
Proactive collection
AI-driven accuracy
2
Targeted segmentation
Automated 40% billing
Offered 20% plans and discounts
Focused on high-risk cases
3
Optimized collections
Plans increased payments
Discounts boosted recovery
Data optimized collections
The impact
Data-driven collections: Maximizing recovery and efficiency
Maximized recovery
Targeted key accounts
Accelerated collections
Increased unpaid fees
Optimized strategies
Optimized discounts
Efficient resource use
Higher returns
Data-driven gains
Predicted trends
Targeted high-yield segments
Recovered $20MM
Looking ahead
Enhanced AI models
Continuous refinement for higher accuracy
Expanded payment solutions
More flexible options for patients
Optimized collection strategies
Data-driven approach for better recovery