Presenting a live 90-minute webinar with interactive Q&A Medicare and Medicaid Audits Using Statistical Sampling and Extrapolation: Challenging Methods and Results THURSDAY, JUNE 14, 2018 1pm Eastern | 12pm Central | 11am Mountain | 10am Pacific Today’s faculty features: Anna M. Grizzle, Member, Bass Berry & Sims , Nashville, Tenn. Dr. Patricia L. Maykuth, Ph.D, President, Research Design Associates , Decatur, Ga. The audio portion of the conference may be accessed via the telephone or by using your computer's speakers. Please refer to the instructions emailed to registrants for additional information. If you have any questions, please contact Customer Service at 1-800-926-7926 ext. 1 .
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Medicare and Medicaid Audits Using Statistical Sampling and Extrapolation Anna Grizzle - Bass, Berry & Sims, PLC Pat Maykuth, Ph.D. - Research Design Associates, Inc.
Use of Statistical Sampling for Overpayment Estimation • Medicare or Medicaid audit – MAC audits following TPE reviews – ZPIC or UPIC audits – OIG audits – Medicaid agency audits – MIC audits • OIG self-disclosure protocol • Internal compliance audit • Calculation of damages in FCA case? 6
Legal Basis for Statistical Sampling for Overpayment Estimation “The use of statistical sampling to project an overpayment. . . does not deny a provider or supplier due process. Neither the statute nor regulations require that a case-by-case review be conducted in order to determine that a provider or supplier has been overpaid and to determine the amount of overpayment. ” HCFA Ruling 86-1 7
Legal Basis for Statistical Sampling for Overpayment Estimation Statistical sampling does not violate due process “so long as extrapolation is made from a representative sample and is statistically significant. ” Chaves County Home Health Service, Inc. v. Sullivan , 931 F.2d 914 (D.C. Cir. 1991), cert. denied , 402 U.S. 1091 (1992). 8
Legal Basis for Medicare Statistical Sampling and Extrapolation A Medicare contractor may not use extrapolation to determine overpayment amounts . . . unless . . . – There is a sustained or high level of payment error; or – Documented educational intervention has failed to correct the payment error 42 U.S.C. § 1395ddd(f)(3) 9
Legal Basis for Medicare Statistical Sampling and Extrapolation • The PIM provides basic concepts rather than a checklist to complete a valid statistical sampling. – When applied correctly, the PIM’s concepts can lead to a proper methodology to use as a basis for extrapolation. • The PIM’s concepts often are not applied correctly in developing the statistical sampling methodology used as the basis of extrapolation in Medicare audits. Source: Chapter 8 – Benefit Integrity; Medicare Program Integrity Manual; available at: 10 http://www.cms.gov/manuals/downloads/pim83c08.pdf
Legal Basis for Medicaid Statistical Sampling and Extrapolation • Dictated by state law • If no explicit authority, look to due process requirements 11
Performance of Statistical Sampling and Extrapolated Overpayment • Major Steps – Selecting the provider or supplier • Prior history of overpayment error • TPE Review by MACs – Selecting the period to be reviewed – Defining the universe, the sampling unit, and the sampling frame • Define the provider, issue to be reviewed, time period, and methodology for measuring overpayment Source: Chapter 8 – Benefit Integrity; Medicare Program Integrity Manual; available at: http://www.cms.gov/manuals/downloads/pim83c08.pdf 12
Performance of Statistical Sampling and Extrapolated Overpayment • Major Steps (cont.) – Designing the sampling plan and selecting the sample – Reviewing each of the sampling units and determining if there was an overpayment or under payment – Estimating the overpayment Source: Chapter 8 – Benefit Integrity; Medicare Program Integrity Manual; available at: http://www.cms.gov/manuals/downloads/pim83c08.pdf 13
Documentation Requirements • The following items must be documented: – Universe – Sampling frame (sorted) – Random sampling process (seed, program, inputs and printout) – Sample size determination calculations – Extrapolation formulae, inputs and printouts – Extrapolation recalculation where appropriate 14
Valid Methodologies • MPIM specifically lists: – Simple random – Stratified random – Cluster • MPIM specifically requires proper execution of chosen methodology 15
Valid Statistics • Statistically Valid Random Sample (SVRS) • Probability Sample • Use correct formulae • Follow statistical requirements of chosen statistics • Point Estimates and Confidence Intervals are valid statistics – if properly executed • Minimum Sum Method and Penny sampling not been validated in the statistical literature 16
Probability Statistics • 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) Source: Chapter 8 – § 8.4.2 Benefit Integrity; Medicare Program Integrity Manual; available at: http://www.cms.gov/manuals/downloads/pim83c08.pdf 17
Frame Sample (2, 3, 4, 5, 6) (1, 12, 23, 34, 45) (1, 2, 3, 4, 5) (1, 11, 21, 31, 41) (8, 23, 46, 73, 90) Outside of frame x (1, 2, 3, 4, X) 18
Probability Sample 1. To be able to identify the number of samples of a given size that can possibly be selected from a frame of a frame of a given size claims. How many samples of 5 can be selected from a frame of 100? 2. In a simple sample each claim must have a known and equal probability of selection. Strata take simple samples from strata frames. 19
Probability Yardstick • Single sample chosen for the audit is only one sample (of the chosen size) out of a large number of possible samples • Sampling distribution of all possible means provides mathematical model of what is likely to occur if all possible samples analyzed • If a large number of samples were drawn from the frame: – A mean can be calculated for each sample – Each sample mean would not be exactly the same value as others – Means would be different from one another but would cluster around the frame’s central value (or mean) – Differences in the means of different samples are basis of the error that occurs inferring from a sample to the frame rather than measuring all of the claims in the frame – If repeated random samples (moving toward infinity) were made, means would be expected to fall into a normal distribution 20
Possible Samples That Can Be Randomly Drawn From the Frame 21
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 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 distribution 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. 22
“Normal” Distribution 23
Confidence Levels on Normal Distribution Point Estimate Upper Confidence Limit Lower Confidence Limit 24
25
Illustration not based on actual data Where is the Confidence Level? Median One-sided or Two Mode Mean 0 566.09 1,274.35 12,072.62 26
Distribution of Frame Paid Dollars 27
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