Statistical Services for Medical Billing Compliance & RAT-STATS Expertise
Consult with a Professional Statistician
Statistical Services for Medical Billing Compliance & RAT-STATS Expertise
Consult with a Professional Statistician
Consult with a Professional Statistician
Consult with a Professional Statistician
Founder and Principal Statistician
100s of Medical Billing Compliance Cases
Totaling $500M in Paid Claims
25 Years Experience in Statistical Consulting
Expert Witness in State and Federal Court Cases
Statistical Expertise with CMS Program Integrity Manual
Cost-effective Sampling Designs for paid claims
Extrapolation of overpayment
Expertise with RAT-STATS software
Customized statistical programming for complex compliance issues
Internal Audits & Self Reporting
Strategy with CMS Contractors, CMS, and OIG
Payor Demand Letters
Expert Witness Services
Sampling Design
When to use a Stratified Design
When to use a Probe Sample
Sample Size Estimation
Random Samples
When to use Post-Stratification
Extrapolation of Reviewed Sample Results
Communicating Findings
Clarify and Strengthen your communications with the Centers for Medicare & Medicaid Services
Self-Audits
CMS Contractor Audits
Targeting a Level of Precision based on a Confidence Interval
Guarantee Your Results are 100% Reproducible
Understanding & Responding to a Demand Letter
Clarify and Strengthen your communications with the Office of the Inspector General
Understanding what OIG means by
"Full Sample" Size
Allocating a Full Sample Size across a Stratified Design
Understanding & Responding to a Demand Letter
But there are some limitations to be aware of...
Whether or not a patient should have been admitted can lead to all-or-nothing overpayment of inpatient claims.
But billing face-to-face physician care (9921x) at the wrong level doesn't result in all-or-nothing overpayments.
We offer customized statistical programming that better reflects what you know about the paid claims process.
Data visualizations are essential to developing efficient and cost-effective sampling designs, especially for identifying stratifications that lower the risk of overpayment extrapolations that are biased high.
Data visualizations are also essential to identify items in the populations that are outliers. Often 100% census review of the outliers is a useful risk-reducing choice, especially when the outliers are large payments.
It's also critical to use data visualizations to check reviewed sample results. This sometimes reveals a need for post-stratification to avoid costly overestimation of overpayment.
We use data visualization in every sampling design.
Overpayment is often agreed to be repaid at the lower confidence limit of a one-side confidence interval.
The lower limit of this confidence interval tells us with a given level of confidence that the overpayment is unlikely to be less than this $ amount.
We can provide any confidence interval required for your situation.
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