Detect, recover and prevent claim overpayments
Healthcare claim payments are complex, leaving ample room for inaccurate payments to slip through. Health plans can implement Optum® Data Mining, both pre- and post-pay, to help improve payment accuracy, reduce overpayments and save on medical and administrative costs without requesting provider documentation.
Our robust Data Mining solution can:
- Facilitate compliance with provider contracts
- Identify and resolve outlier billing errors
- Conduct reviews on adjudication and coding errors
- Prevent future errors and overpayments
Maximize savings with pre- and post-pay Data Mining
Health plans save an average of $1.96 PMPM (per member, per month) with our comprehensive, integrated solution.
Blend technology and human intelligence
Our 4-tier analytic process and expert analysts review and deny errant claims before payment and identify overpayments already reimbursed.
Find errors across multiple categories
We identify and prevent claim errors including duplicates, billing issues, coordination of benefits and contract administration.
Shift from recovery to prevention
Our advanced analytics pinpoint the root cause of claim overpayments and apply learnings to prevent future claim errors.
Increase transparency through collaboration
We share concepts and analytics with health plans to help optimize their programs and work with providers to explain and prevent errors.
Drive more value from your data mining solution
We bring unmatched data mining expertise and experience to help health plans achieve payment accuracy and reduce medical and administrative costs. Our results:
- More than 1 million claims reviewed daily
- More than $8 billion in annual pre- and post-pay savings
- 9 million industry rules, regulations and policies
- More than 1,200 algorithms to generate insights and drive greater accuracy
Related healthcare insights
E-book
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Infographic
Learn how a multi-tiered approach to data mining helps maximize savings and achieve payment accuracy earlier in the claim lifecycle.