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Case Studies

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Enhance recovery of patient self-pay medical expenses

Enhance recovery of patient self-pay medical expenses

Enhance recovery of patient self-pay medical expenses

How data-driven insights transformed healthcare payment collections

How data-driven insights transformed healthcare payment collections

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 hospitalsespecially non-profitsstruggle 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