CMS RADV Extrapolation Methodology: In English
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1 CMS RADV Extrapolation Methodology: In English Presented by: Kim Browning, CHRS,CHC, PMP Executive Vice President, Cognisight, LLC & Kathy McGuire, BSN,MS Rochester General Health System Senior Vice President of Long Term Care and Senior Services October, 2013
2 Objectives Quick Overview of Risk Adjustment Data Validation (RADV) Examples of actual Risk Adjustment Data Validation (RADV)audits Understand how CMS will calculate new Risk Adjustment Data Validation (RADV) payment methodology What will CMS do and how will they do it Provide practical ways to use today s learning and apply to your internal controls/internal audit Cognisight, LLC
3 Risk Adjustment Data Validation An annual audit, conducted by CMS to verify plans risk adjustment payments Two types of audits: National Several plans asked to submit risk adjustment documentation to CMS for a very small sample To date, no financial implications Targeted 30 plans a year; full audit Financial implications New validation payment methodology being applied
4 Risk Adjustment Data Validation 101 Risk Adjustment Flow 4 PACE Plan Receive Risk Adjustment Payment from CMS Provide Care Submit RAPS/EDPS to CMS Mine Documentation for Risk Adjustments
5 Risk Adjustment Data Validation 101 Risk Adjustment Data Validation Flow CMS 5 Risk Adjustment Validation Payment Methodology Targeted RADV Risk Adjustment Substantiation CMS Determines Sample from RAPS/EDPS Submit medical records to CMS
6 Results From Risk Adjustment Data Validation Background Using Cognisight clients as the population None of Cognisight PACE plans have been selected Cognisight Medicare Advantage clients selected for National RADV only Results represents client & Cognisight submits 81% client submits, 19% Cognisight submits 6
7 Results From Risk Adjustment Data Validation Medicare Advantage (National RADV) Out of 20 Cognisight serviced health plans: 7 total plans selected by CMS for 10 distinct audits 1 plan selected 2 consecutive years 1 plan selected 2 out of 4 years 1 plan selected but conducted internally, no details to share 14 affected members with a total of 43 HCCs 7
8 Results From Risk Adjustment Data Validation Medicare Advantage (National RADV) 8 Members HCCs Ranges Average 2 6 Mode 1 2* % substantiated HCCs % % non-substantiated HCCs % % unsure HCCs %** Only 2 members had 2 HCCs ** 3 were Cognisight finds
9 Results From Risk Adjustment Data Validation Medicare Advantage (National RADV) Common HCCs Body System HCCs Outcome 9 Cardio-Vascular 105 (5x) 92 (2x) 80 (4x) 79 (2x) all substantiated all substantiated 1 unsure 1 unsure Respiratory 108 (4x) all substantiated Neuro-Ophthal & Psych 71 (2x) 55 (2x) both substantiated both unsure Neoplasms 10 (2x) both unsubstantiated Genitourinary 131 (2x) both substantiated Endocrine 15 (2x) both substantiated 7 total plans; 10 distinct audits; 81% client submits; 19% Cognisight submits
10 Results From Risk Adjustment Data Validation 10 Medicare Advantage (National RADV) 13 Single HCCs Body System HCCs Outcome Cardio-Vascular Digestive unsubstantiated substantiated unsubstantiated substantiated Neuro-Ophthal & Psych 69 unsubstantiated Major Organ Transplants 176 unsubstantiated Neoplasms 7 8 Endocrine unsubstantiated substantiated substantiated substantiated unsubstantiated Infections & Parasitic 27 unsubstantiated Injury & Poisoning 161 unsubstantiated 7 total plans; 10 distinct audits; 81% client submits; 19% Cognisight submits
11 Results From Risk Adjustment Data Validation Medicare Advantage (National RADV) Unsubstantiated Reasons History of vs. active Incorrect ICD-9 or V Code Ineligible provider 11 Handout with ICD-9 & HCC 7 total plans; 10 distinct audits; 81% client submits; 19% Cognisight submits
12 Results From Risk Adjustment Data Validation SCAN Health Targeted RADV 12 Permission to share by T. Pham, SCAN Health
13 Extrapolation Methodology Background This methodology applies to Audits conducted on Payment Year 2011 (dates of service 2010) PY First Year for extrapolated estimates Sampling occurs after close of final reconciliation for payment year under audit CMS goal is to address: The national payment error rate for MA program Quality of risk adjusted data submitted for payment by MA Organizations Cognisight, LLC
14 Extrapolation Methodology Background 14 Easy to Understand, Right? 2013 Cognisight, LLC
15 Extrapolation Methodology Background Sampling and Stratification CMS selects a sample of beneficiaries from each MA Contract Enrollee-based stratification RADV eligible enrollees ranked from lowest to highest based on community score 15 Three groups highest risk scores, lowest scores and the middle stratum 2013 Cognisight, LLC
16 slide 6 Extrapolation Methodology Background Importance of Stratification Random sampling has high risk of not representing your population and or performance Stratification consists of dividing the population into subsets (called strata) within each of which an independent sample is selected. Stratification reduces some of the variability Cognisight, LLC
17 Extrapolation Methodology Background Sample Size CMS performs sample selection of 201 enrollees for medical record review 17 Of the 201 sample size, 67 will be randomly selected from each group or stratum Contracts with fewer than 1000 RADV-eligible enrollees CMS will adjust sample size to lower than Cognisight, LLC
18 To Simplify Think of RADV Extrapolation as a Roadmap #1 Know RADV eligibles #2 Know total CMS payment #3 Divide RADV sample into thirds (High/Med/Low stratum) #4 Determine weighting for each stratum #5 Determine estimated weighted payment error (Point Estimate) #6 Determine Standard Error #7 Calculate Confidence Interval #8 Determine upper and lower bound amounts by adding/subtracting the Confidence Interval to the Point Estimate #9 Take lower bound amount and apply FFS adjuster Cognisight, LLC Slide 21
19 RADV EXTRAPOLATION EXAMPLE # RISK SCORE STRATA MA PAYMENT Hypothetical MA Pymt. Variance Weighted Payment error Mean of Stratum Deviation Deviation Squared 19 Variance (div by 67-1) TOP 3RD $ 47, $ 47, $ - $ - $ 1, $ (1,915.67) $ 3,669, TOP 3RD $ 47, , , (1,915.67) 3,669, TOP 3RD $ 38, , (1,947.76) (9,690.11) 1, (3,863.43) 14,926, TOP 3RD $ 34, , , (1,915.67) 3,669, TOP 3RD $ 33, , , (1,915.67) 3,669, TOP 3RD $ 12, , , (1,915.67) 3,669, TOP 3RD $ 12, , , (1,915.67) 3,669, TOP 3RD $ 12, , , (1,915.67) 3,669, TOP 3RD $ 12, , , , , ,042, TOP 3RD $ 11, , $ - $ 1, $ (1,915.67) $ 3,669, $ 1,309, $ 1,181, $ 128, $ 638, $ 128, $ 0.00 $ 1,989,292, ,140, Error % 9.80% Enrollee Weight Weighted Enrollee Payment Error 4 $ 638, MIDDLE 3RD $ 11, $ 11, $ - $ - $ $ (894.25) $ 799, MIDDLE 3RD $ 11, , (894.25) 799, MIDDLE 3RD $ 11, , (894.25) 799, MIDDLE 3RD $ 11, , (3,725.76) (18,535.66) (4,620.01) 21,344, MIDDLE 3RD $ 10, , (894.25) 799, MIDDLE 3RD $ 6, , , , ,729, MIDDLE 3RD $ 6, , (894.25) 799, MIDDLE 3RD $ 6, , (894.25) 799, MIDDLE 3RD $ 6, , (894.25) 799, MIDDLE 3RD $ 6, , $ - $ $ (894.25) $ 799, $ 580, $ 520, $ 59, $ 298, $ 59, $ 0.00 $ 482,885, ,316, Error % 10.32% Enrollee Weight Weighted Enrollee Payment Error $ 298, Nh=1000 Roadmap Summary BOTTOM 3RD $ 6, $ 6, $ - $ - $ $ (373.75) $ 139, BOTTOM 3RD $ 6, , (373.75) 139, BOTTOM 3RD $ 6, , (373.75) 139, BOTTOM 3RD $ 5, , (373.75) 139, BOTTOM 3RD $ 5, , , , ,683, BOTTOM 3RD $ 5, , (1,707.40) (8,494.32) (2,081.15) 4,331, BOTTOM 3RD $ 1, , (373.75) 139, BOTTOM 3RD $ 1, , (373.75) 139, BOTTOM 3RD $ 1, , (373.75) 139, BOTTOM 3RD $ 1, , $ - $ $ (373.75) $ 139, $ 252, $ 227, $ 25, $ 124, $ 25, $ (0.00) $ 100,460, ,522, Error % 9.93% Enrollee Weight Weighted Enrollee Payment Error $ 124, Total Estimated Payment Error 64,513,183,122 2 Strata Total $ 2,142, $ 1,929, $ 213, $ 1,061, Standard Error (SE) (Sq of PE) 253, Total CMS Pymt $ 10,800,000 Sample Population (Extrapolated) Confidence Interval CI (2.575*SE) $ 654, Average Error % 10% 10% POINT ESTIMATE (PE) $ 1,061, PE + Confidence Interval $ 1,715, PE - Confidence Interval $ 407, Lower Bound CI 407, Payment Recovery Amount subject to FFS Used monthly payment amounts to simplify example 1 1 RADV Eligibles 2 Total CMS Pymt 3 Divide sample into 3 stratum 4 Weight the stratum 5 Point Estimate 6 Standard Error 7 Confidence Interval 8 Upper & Lower bounds 9 Lower + FFS adjuster Cognisight, LLC
20 Correlation to Internal Controls/Audit Give consideration to refining internal controls and audit strategies to accommodate RADV Like you do with Quality, Falls, etc.,focus on your highest risks 20 Focus on Top 1/3 Paid Used approach with a PACE plan in the North East 2013 Cognisight, LLC Slide 23
21 Impact of Risk Verification PACE Plan, 400 Members, $11M Annual Plan Revenue 21 10% Error Rate 20% Error Rate Payment Recovery $0.41M $1.16M Post 100% Review of Top Third Stratum Payment Recovery $0.11M $0.41M Error Rate reduced 4% 8% Disclaimer: This example assumes equal distribution of error in each of the sample stratum Cognisight, LLC
22 Contact Information If you have questions or would like more information, please contact: Kim Browning, Executive Vice President Chetna Chandrakala, Vice President To obtain a more detailed version of the step-by-step portion of the presentation, please contact: 22 Steve Coan, Vice President, Business Development scoan@cognisight.com Cognisight, LLC
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