presents presents Medicare and Medicaid Audit Sampling Strategies Sampling Strategies Creating Sampling Plans and Challenging Flawed CMS Audit Samples A Live 90-Minute Teleconference/Webinar with Interactive Q&A A Live 90-Minute Teleconference/Webinar with Interactive Q&A Today's panel features: Patricia L. Maykuth, Ph.D, President, Research Design Associates, Decatur, Ga. Anna M. Grizzle, Member, Bass Berry & Sims , Nashville, Tenn. , , , , y Wednesday, July 14, 2010 The conference begins at: The conference begins at: 1 pm Eastern 12 pm Central 11 am Mountain 10 am Pacific 10 am Pacific You can access the audio portion of the conference on the telephone or by using your computer's speakers. Please refer to the dial in/ log in instructions emailed to registrations.
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Sampling for Medicare and Medicaid Audits Sampling for Medicare and Medicaid Audits p p g g Strategies for Creating Sampling Plans and Strategies for Creating Sampling Plans and Challenging Improper Samples Challenging Improper Samples Pat Maykuth, Ph.D. Research Design Associates esea c es g ssoc ates pm@researchdesignassociates.com Anna M. Grizzle, Esq. agrizzle@bassberry com agrizzle@bassberry.com Bass, Berry & Sims PLC July 14, 2010
Overview Overview I. Use of Statistical Sampling for Overpayment Estimation p y II. Design and Implementation of Sampling and Extrapolation Plans Sampling and Extrapolation Plans III. Defending Against Audit Results IV IV. Raising Sampling Defenses R i i S li D f 5
Use of Statistical Sampling for Use of Statistical Sampling for Overpayment Estimation Overpayment Estimation Overpayment Estimation Overpayment Estimation 6
Applications of Sampling Applications of Sampling • Used in different audits (Medicare, Medicaid, tax, financial statements, etc.) • Appropriate when records are too voluminous A i t h d t l i for individual review • Increased data mining = increased sampling Increased data mining increased sampling and extrapolation 7
Purposes of Statistical Sampling Purposes of Statistical Sampling • Determine if further investigation is warranted • Project onto period with unavailable records records • Estimate amount of error 8
Legal Authority for Use of Statistical Legal Authority for Use of Statistical Sampling for Overpayment Estimation • Statistical sampling does not violate due process “so long as extrapolation is p g p made from a representative sample and is statistically significant.” Chaves y g County Home Health Service, Inc. v. Sullivan , 931 F.2d 914 (D.C. Cir. 1991), , ( ), cert denied, 502 U.S. 1091 (1992). 9
Legal Authority for Use of Statistical Legal Authority for Use of Statistical Sampling for Overpayment Estimation • A Medicare contractor may not use extrapolation to determine overpayment amounts . . . . unless . . . – There is a sustained or high level of payment error; or t – Documented educational intervention has failed to correct the payment error failed to correct the payment error 42 U.S.C. § 1395ddd(f)(3) 42 U.S.C. § 1395ddd(f)(3) 10 10
Legal Authority for Use of Statistical Legal Authority for Use of Statistical Sampling for Overpayment Estimation • Sustained or high level of payment error determined by: – Error rate determinations by MR unit, PSC, 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 Medicare Program Integrity Manual § 3.10.1.4 11 11 11
Legal Authority for Use of Statistical Legal Authority for Use of Statistical Sampling for Overpayment Estimation • Additional Factors to Consider – Number of claims in universe – Variability between claims – Dollar values associated with claims – Available resources – Cost effectiveness of expected sampling results Medicare Program Integrity Manual § 3.10.1.4 12 12 12
Design and Implementation of Design and Implementation of Design and Implementation of Design and Implementation of Sampling and Extrapolation Plans Sampling and Extrapolation Plans 13 13
Numbers Numbers vs. vs. Statistics Statistics • Numbers can readily be manipulated and outcomes understood through the use of simple math: addition subtraction multiplication 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 quantitative data and the use of probability theory to estimate universe parameters e.g. correlations, t- tests and point estimates. 14
Inferential Statistics Inferential Statistics • A branch of applied statistics drawing A branch of applied statistics drawing conclusions about a population from a random sample drawn from it random sample drawn from it • Mathematical analyses that move beyond mere description of research data to make mere description of research data to make inferences about the larger population from which the sample was drawn from which the sample was drawn. 15
Statistical Terms Statistical Terms • Parametric Statistics • Parameter • Point Estimate • Point Estimate. • Confidence Interval • Statistic 16
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Precision and Lower Bound Precision and Lower Bound Point estimate $1,000,000 Precision amount at 90% two-tailed $ 140,000 confidence Lower bound at 90% two-tailed $860,000 $ confidence = Point estimate minus Precision amount Precision percent = Precision amount 14% divided by Point estimate 18 18 18
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20 11 10 9 8 7 6 5 4 Normal Curve Normal Curve 3 2 1 30 25 20 15 10 5 0
Calculated Statistics of Sample Calculated Statistics of Sample Before Before After claim review After claim review • Calculate overpayment • Choice of methodology – Per claim – Simple p – For sample F l – Stratified – Proportion of claims in error • Sample size estimation • Calculate point estimate based on based on – Mean – Universe size – Error rates – Precision for confidence – Standard deviation or interval interval probe – Upper and lower CI – Precision – Confidence interval 21
Inferential Statistics: universe universe frame frame following rules required Simple Simple Selection random Applied to Calculate of seed Sample sample file that is sample random definition unbiased unbiased size size number number table Stratified Independent observations Selected Randomly selected sample sample Normally distributed Normally distributed Representative 22
D t D t Data Necessary for Data Necessary for Statistical Review N N f f St ti ti t ti ti tatistical Review l R l R i i • Audit methodology and logs Audit methodology and logs • Readable electronic data files for universe, frame sample selection sample and frame, sample selection, sample and overpayment at claim line level • Sample selection methodology size • Sample selection methodology, size estimation, seed, file sorted as applied and output output • Sufficient information to collapse data into CCN strata or overpayment at every level CCN, strata or overpayment at every level 23
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Random Sample Random Sample Without It No Projections Possible Without It No Projections Possible • Independent claims Independent claims • Randomly selected • Yield a set of representative claims • Yield a set of representative claims • Large enough to reflect the statistical characteristics of the universe • Each selected claim is equally likely to be selected as any other observation 25
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30 Boat Ice Water Sky 50 45 45 40 35 30 25 20 15 10 5 0
Universe File All claims for the provider for audit time period 152,480 Strat4 Strat1 Strat2 Strat3 > $500 > $500 >$10 ‐ <$68 >$10 ‐ <$68 $68 ‐ $200 $68 ‐ $200 $200 ‐ $500 $200 ‐ $500 3,658 107,466 30,387 10,969 Sample Sample Sample Sample 31
E E Error Terms Error Terms T T • Variance Variance • Standard deviation • Precision • Precision • Precision percent • Coefficient of Variation C ffi i t f V i ti • Error rate • Confidence Interval • Sampling 32
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