Medicare and Medicaid Audit Sampling Strategies Developing Sampling - PowerPoint PPT Presentation

Presenting a live 90-minute webinar with interactive Q&A Medicare and Medicaid Audit Sampling Strategies Developing Sampling Plans and Challenging Flawed CMS Audit Samples TUESDAY, AUGUST 14, 2012 1pm Eastern | 12pm Central | 11am Mountain | 10am Pacific Today’s faculty features: Anna M. Grizzle, Member, Bass Berry & Sims , Nashville, Tenn. 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. 10 .

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Medicare and Medicaid Audit Sampling Strategies Anna M. Grizzle Patricia Maykuth, Ph.D. Partner President Bass, Berry & Sims PLC Research Design Associates, Inc. August 14, 2012

Agenda • When is statistical sampling and extrapolation used? • What is the legal basis for statistical sampling and extrapolation? • How is statistical sampling and extrapolation performed? • How can I defend against extrapolated overpayment results? 5

Use of Statistical Sampling for Overpayment Estimation • Acceptable tool in different audits: Medicare, Medicaid, tax, financial statements, etc. • Appropriate when records are too voluminous for individual review • Used in Medicare overpayment reviews since the 1970’s 6

Use of Statistical Sampling for Overpayment Estimation • CMS overpayment audit • OIG self-disclosure protocol • Internal compliance audit 7

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 8

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). 9

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) 10

Legal Basis for Medicare Statistical Sampling and Extrapolation • Sustained or high level of payment error can be determined by: – Error rate determinations by MR unit, ZPIC – Probe samples – Data analysis – Provider/supplier history – Information from law enforcement investigations – Allegations of wrongdoing by current or former employees of provider or supplier – Audits or evaluations conducted by the OIG Source: Chapter 8 – Benefit Integrity; Medicare Program Integrity Manual; available at: 11 http://www.cms.gov/manuals/downloads/pim83c08.pdf (Previously found in Chapter 3)

Legal Basis for Medicare Statistical Sampling and Extrapolation • Additional Factors to Consider – Number of claims in universe – Dollar values associated with claims – Available resources – Cost effectiveness of expected sampling results Source: Chapter 8 – Benefit Integrity; Medicare Program Integrity Manual; available at: 12 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 13

Numbers vs. Statistics • Numbers can readily be manipulated and outcomes understood through the use of simple math: addition, subtraction, multiplication, multiplication and division e.g., %s, differences, sums and averages. • Statistics is branch of applied math concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate universe parameters e.g. correlations, t- tests and point estimates 14

MPIM Requirements • Key Rules – Obtain and properly execute “probability sample” – Keep data and records so work can be replicated • More content and direction given in RAT-STATS Manuals and standard of care expected of statisticians under Generally Accepted Statistics Procedures and Policies (“GASPP”) 15

Validity Sample If a particular probability sample design is properly executed, i.e., defining the universe, the frame, the sampling units, using proper randomization, accurately measuring the variables of interest, and using the correct formulas for estimation, then assertions that the sample and its resulting estimates are “not statistically valid” cannot legitimately be made. In other words, a probability sample and its results are always “valid.” MPIM § 8.4.2 16

“Always Valid” Does Not Mean Results Cannot Be Challenged Rather the “always valid” refers to the idea that internal operation of a statistical process which, when executed, will (with respect to its mathematical assumptions) yield internally consistent results. The concept of statistically “valid” includes the understanding that there is an expectation of error. “Valid” results include expectation of error: wrong 10 times in 100, precision demonstrated inaccuracy, validly rejecting hypothesis. 17

Valid Outcomes Require Properly • defined universe • defined the frame • defined sampling units • use proper randomization • Accurate measuring the variables of interest • using the correct formulas for estimation • tests of key assumptions • accurate reporting of actual findings 18

Typical Problems with Extrapolation  Sample size, not associated with precision or confidence  Incorrect use of formulas  Use of wrong formulas - choose wrong method  Use of inapplicable methodology – simple, stratified, cluster, multi-stage  Non-representative sample  Fail to meet key assumptions of statistic – math basis of statistic  Exclusion of zero paid claims  Accuracy outside of recommended range – too little precision  Reporting precision and/or confidence levels that are wrong 19

Unacceptable Departure From GASPP • too excessive a departure from even a lenient interpretation of the MPIM • major departures from methodology • non-trivial mistakes in audit definition application of method • non-sampling errors • lack of statistical oversight and quality control 20

Overview of the sampling process Sample Sample Size Definition Universe (Chosen Frame (dates; (simple; (who; why; precision & units; criteria) stratified; what data) confidence) multi-stage) Seed & Pick Out Random Sample Numbers 21

Calculated Statistics of Sample After claim review Before claim review Choice of methodology Calcula ulate te overpay payment ment Simple  Per claim  For sample le Stratified  Proportion ion of claims in Cluster error Multi-stage Calcula ulate te point t estimate te Sample size determination based  Mean on  Error rates  Universe size  Precis isio ion n for  Standard deviation or probe confide idence nce interval al  Upper and lower CI  Chosen Precision  Chosen Confidence interval 22

Key Requirements for Use of Parametric Statistics Use a sample that: • Is made up or independent observations • Randomly selected • Normally distributed • Is representative of the frame from which it was chosen and over which it will be extrapolated 23

Random – … each distinct sample of the set has a known probability of selection…. – … one of the possible samples is selected by a random process according to which each sampling unit in the target population receives its appropriate chance of selection…. 24

Representative Sample 26

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Medicare and Medicaid Audit Sampling Strategies Developing Sampling - PowerPoint PPT Presentation

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