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Presenting a live 90-minute webinar with interactive Q&A
Medicaid Overpayments: Challenging State
Audit Allegations of Overpayment and the
Use of Statistical Sampling and Extrapolation
Today’s faculty features:
1pm Eastern | 12pm Central | 11am Mountain | 10am Pacific
TUESDAY, OCTOBER 11, 2016
David R. Ross, Shareholder, O’Connell and Aronowitz, Albany, N.Y.
Dr. Patricia L. Maykuth, Ph.D, President, Research Design Associates, Decatur, Ga.
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MEDICAID OVERPAYMENTS: Challenging State Audit Allegations of Overpayment and the Use of Statistical Sampling and Extrapolation
P re s e nte d b y : Pa t M ay ku th , P h . D. , P re s i dent , Re s e a rc h D e s i g n A s s o c i ate s , I n c . D av i d R . Ro s s , E s q . , S h a re h o l de r, O ’C o nn e l l & A ro n ow i t z , P.C .
Tu e s d ay, O c t o be r 1 1 , 2 0 1 6
1 : 0 0 p . m . t o 2 : 3 0 p . m .
A Primer on Sampling and Extrapolation
PART I OF THE PRESENTATION
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Right Idea, Wrong Answer
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There Are Three Kinds of Lies: Lies, Damned Lies, and Statistics So which is it? The outcome of any statistical study is determined by that characteristic that is sampled
Why are so many polls in this election saying contradictory things?
◦ bias
◦ corrupted frame
◦ probability of selection unknown
◦ polling done for advocacy not to answer the question
◦ sampling error
◦ response bias
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Dewey Wins the Election!!
Pollsters surveyed via telephone
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Ultimately, It Comes Down to Two Basic Questions
1. What is the ‘it’ that is being measured?
2. Is ‘it’ being measured correctly?
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What is Statistics? Statistics is a branch of mathematics dealing with: ◦ the collection,
◦ analysis,
◦ interpretation, and
◦ presentation of masses of quantitative data
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Terms Universe – dollar amount of claims paid to a Provider in a specific timeframe
Sampling Frame – subset of the universe defined as variables of interest from which the sample will be randomly selected and over which the sample will be extrapolated
Sample – a randomly selected subset of a sampling frame to be audited for overpayments
Unit of Analysis (Sampling Unit) – what is measured in the audit: claim line, claim, beneficiary, provider (must be invariant throughout the audit)
(Please see the List of Terms provided with presentation)
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Universe (Provider and all claims)
Frame (All claims for a particular time
frame and code)
Sample (50 claims per sample)
01/01/2010
Code: 12743
Sampling Unit (Individual claim: must be within frame)
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Corrupted Frame
Fra Sampling Frame
Universe
Outside Definition
SAMPLE
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Characteristics of a Valid Medicaid
Overpayment Study
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universe frame
Inferential Statistics Method: following rules required
Sample definition
Simple
Select seed & random number
table
Applied to file that is unbiased
Calculate sample
size
Select probability
sample
Independent observations Randomly selected Normally distributed Representative
Stratified
Audit claims in the field
Prior history of overpayment
error
Define the who, what, time and how overpayment is measured
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Statistics Is Different from Numbers
Statistical analysis that uses probability theory to generate and properly interpret inferences
Probability statistics uses probability distributions for decision making. Probability theory is the mathematical basis of those distributions tested repeatedly
Use subsets of data (sample) to estimate variables in a larger data set (frame/universe)
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What is Statistical Sampling? For the purpose of today’s discussion: A Statistically Valid Random Sample (“SVRS”) from a universe of paid Medicaid claims ◦ Guards against cherry picking or any bias
A sample has to:
◦ meet the requirements the methodology,
◦ meet chosen sampling error,
◦ be of sufficient size to accurately measure the variable,
◦ be random, and
◦ be representative (without bias)
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Probability Statistics and SVRS To know the number of samples of the chosen size
that can be created from the frame
Known likelihood of selection of each sampling unit
Proper randomization
Proper execution of sample methodology
Use correct formulae
Accurate measurement of the variable of interest (overpayment)
Medicare reference: MPIM 8.4.2; Cochran Sampling Methodology
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What is Extrapolation?
Extrapolation takes the results of an audited sample of claims and projects the dollar amount of the overpayment from the sample over the universe of paid claims
The audited sample has a known amount of dollars in error so that amount is projected to the universe for a repayment amount
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Medicare and Statistical Sampling
Medicare Program Integrity Manual (MPIM) Chapter 8
1 provides nineteen pages of guidance “to
provide instructions for [auditors] for the use of statistical sampling in their reviews to calculate and project (i.e., extrapolate) overpayment amounts to be recovered”
1 https://www.cms.gov/Regulations-and-
Guidance/Guidance/Manuals/Downloads/pim83c08.pdf
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Valid Outcomes Require Proper Execution
Defined universe
Defined frame
Defined sampling units
Use proper randomization
Accurately measure overpayments
Use the correct formulas for estimation
Test key assumptions of method
Accurately report actual findings
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How to Properly Execute the Audit Methodology
Choose appropriate sampling unit, methodology and definition for the audit data
Meet the criteria of that statistical model
Exercise knowledgeable statistical oversight and quality control throughout
Document the process so that it can be replicated
Evaluate non-sampling errors and their impact
Calculate the results correctly
Report findings accurately and ethically
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Random Samples A simple random sample is a sequence of independent, identically distributed (IID) random variables.
The term random sample is ubiquitous in mathematical statistics while the abbreviation IID is just as common in basic probability. Much of basic probability and mathematical statistics deal with random parameters constructed from random samples (sample mean, sample variance, sample covariance, and order statistics).
The point estimate (CMS’s common overpayment estimator) is a type of mean making these fundamental concepts critical to the prediction.
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Representative Samples A small yet complete and accurate picture of the data in the frame.
A subset of a statistical universe that accurately reflects the numerical membership of the entire universe and its distribution. A representative sample is an unbiased indication of what the frame is like.
Representativeness is tested mathematically. When a sample is not representative, the result is known as a sampling error.
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Representative Sample
Frame
Sample
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Key Requirements for Use of Parametric Statistics
Use a sample that is: ◦ Made up of independent observations
◦ Randomly selected
◦ Normally distributed
◦ Representative of the frame from which it was chosen and over which it will be extrapolated
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Sample Size Determination Based on Chosen Precision and Confidence
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RAT-STATS Results
80% 90% 95% 99%
1% 75 77 78 79
2% 63 68 71 75
5% 29* 39 46 56
10% 10* 15* 20* 29*
15% 5* 8* 10* 16*
Confidence Level
Precision Level
* Sample sizes less than 30
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Normal Distribution
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What’s the Big Deal About the Normal Distribution?
In probability theory, the normal (or bell-shaped) distribution is continuous probability distribution (a function that tells the probability of a number in some context falling between any two real numbers). The normal distribution is symmetric around the mean. The mean, median and mode are the same number.
The normal distribution is immensely useful because of the Central Limit Theorem - which states that the mean of many random variables independently drawn from the same frame is distributed approximately normally, irrespective of the form of the original distribution. That is, the overpayment means will be randomly distributed if the sample is large ... moving toward infinity.
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Lower Confidence Level
Upper Confidence Level
Point Estimate
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35
Mode
Mean
Median
0 12,072.62 566.09 1,274.35
Where is the Confidence Level? One-sided or Two?
Illustration not based on data
36
Frame Distribution
37
38
Sample Frequency in Dollars N 15
Mean 430.93
Median 112.00
Mode 120
Std. Deviation 525.519
Variance 76,170.210
Skewness 0.816
Kurtosis 0.580
Minimum 10
Maximum 1,300
Sum 6,464
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Non Normal Mean and Standard Deviation
Sample mean +/- 1 standard deviation
Mean = $430.92 + sd = 525.510 = 956.43
430.92 - 525.510 = -94.18
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Sample Overpayment Frequency in Dollars
N 15
Mean 339.333
Median 100.00
Mode 0.0
Std. Deviation 511.62
Variance 261,763.810
Skewness 1.197
Kurtosis -0.524
Minimum 10
Maximum 1,300
Sum 6,464
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Amount Paid is a Proxy for Overpayment
Sample selected from amount paid to provider
Sample analyzed using overpayment data
Never know up front what the overpayment amount is going to be unless ◦ There is a known history of overpayment dollar amount; OR
◦ Conduct a probe
Overpayment amounts must meet criteria for using parametric statistics or the confidence levels are destroyed
If overpayments are not correlated with amount paid the analysis cannot proceed
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RAT-STATS Overpayment Estimation
Formulae:
Confidence Level:
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RAT-STATS Point Estimate & Confidence Interval
POINT ESTIMATE 27,147
90% CONFIDENCE LEVEL
LOWER LIMIT 10,368
UPPER LIMIT 43,925
PRECISION AMOUNT 16,778
PRECISION PERCENT 61.81%
T-VALUE USED 1.761310135775
Lower 27,147 + 16,778 = $43,925
Upper 27,147 - 16,778 = $ 10,368
Confidence Interval = 16,778 + 16,778 = 33,556
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Confidence Levels Non Normal Data
Point Estimate +/- ½ Confidence Interval
Lower 27,147 + 16,778 = $43, 925
Upper 27,147 - 16,778 = $ 10,368
Confidence Interval = 16,778 + 16,778 = 33,556
Lower confidence Level ??? Unknowable
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Poor Audit Design & Execution Produce Only Invalid Results
Statistics in the hands of an inept auditor are like a lamppost to a drunk: they are used more for
support than illumination.
46
Legal Issues Pertaining to Sampling and
Extrapolation
PART II OF THE PRESENTATION
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Legal Basis for Statistical Sampling for Overpayment Estimation
US HHS “may introduce the results of a statistical sampling study as evidence of the number of violations . . . or the factors considered in determining the amount of [a] civil money penalty. Such statistical sampling study, if based upon an appropriate sampling and computed by valid statistical methods, constitutes prima facie evidence. [T]he burden . . . shifts to the [Provider] to produce evidence reasonably calculated to rebut the findings of the statistical sampling study.”
Excerpt from 45 C.F.R. § 160.536(a)-(b) (emphasis added).
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What Are the Legal Issues Pertaining to Sampling and Extrapolation?
Understanding what those issues are
Do I need an expert? (Hint: you absolutely do) ◦ Lawyers are not enough (and that’s coming from a
lawyer)
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Individual States and Statistical Sampling
Unlike Medicare, there is no universal guidance outlining the rules of the process for Medicaid
Your state may have simple or detailed rules, or no rules at all ◦ Texas follows the MPIM and uses RAT-STATS ◦ California and Florida use “generally accepted statistical
standards” ◦ New York has no rules at all
The burden of proof is on the Provider to challenge the statistical sampling and extrapolation ◦ The State’s audit methodology is presumed valid unless
and until the Provider proves otherwise
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The Role of the Expert:
Why You Should Always Have One A statistical consultant is essential to understanding how the audit was conducted and whether the results are statistically valid ◦ Laypersons cannot understand these concepts
absent training
The expert will review both the process actually used and the results in your case
An expert is the only person who will be able to answer important questions pertaining to the audit
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The Role of the Expert: Asking the Important Questions
Is the process used by the auditors properly designed?
Is this process suitable for use in this audit of my client?
Is the process, as applied to my client in this audit, proper?
Did it deviate from the norm? If so, how and why?
How was the sample size chosen? Is it adequate (large enough) for the confidence interval?
Did the auditors properly document the audit?
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The Role of the Expert: Asking More Important Questions Is the software program certified or widely accepted for
this purpose?
Was adequate documentation for replication provided?
Was the software program used working as it was designed? ◦ When in doubt have an expert look at the source code
◦ Can’t get the source code? Argue violation of due process
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The Role of the Expert: Asking Even More Important Questions
How were the random numbers selected? Were they generated by a valid method? ◦ Did the auditors perform appropriate tests for
randomness?
Can the results be replicated by the provider? ◦ If not, there is a serious problem ◦ Retaining the seed is an important factor ◦ Is the frame sorted as it was at the time the random
numbers were used to pick the sample?
What about the extrapolation calculations themselves?
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Questions?
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Faculty David R. Ross, Esq. Shareholder [email protected] (518) 462 5601 O’Connell and Aronowitz, P.C. 54 State Street Albany, NY 12207 www.oalaw.com
Pat Maykuth, Ph.D. President [email protected] (404) 373 4637 Research Design Associates, Inc. 721 E Ponce de Leon Decatur, GA 30030 www.researchdesignassociates.com
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