Session 33 TS, Medicare Risk Scores for Beginners with Intermediate Topics. Moderator/Presenter: Joseph Saul Flaks, FSA, MAAA

Transcription

1 Session 33 TS, Medicare Risk Scores for Beginners with Intermediate Topics Moderator/Presenter: Joseph Saul Flaks, FSA, MAAA Presenter: Christine Sue Bach, ASA, MAAA, FCA

2 2015 SOA Health Meeting Session 33 Medicare Risk Scores Beginning & Intermediate Topics June 15, 2015 Joe Flaks, FSA, MAAA Actuary Milliman, Inc. Chris Bach, ASA, MAAA, FCA Senior Consulting Actuary Wakely Consulting Group, Inc.

3 Risk Adjustment Common Model Types Use of Diagnostic Information Concurrent Model Current diagnoses Current claim costs Prospective Model Current diagnoses Next year s claim costs Acute/Chronic Conditions

4 CMS Medicare Models Prospective Model Risk Score Components Demographic Factors - Age, Gender, Disabled Status, Original Reason for Entitlement, & Medicaid Eligibility Diagnoses (ICD) - Hierarchal Condition Codes (HCC), Interactions, and Hierarchies Types of Models Community Institutional New Enrollee ESRD - various models Part D

5 CMS Medicare Models (cont d)

6 CMS Medicare Models (cont d) Common HCCs - Medicare 2014 CMS-HCC Model Relative Factors for Community and Institutional Beneficiaries HCC Description Community Institutional HCC10 Lymphoma and Other Cancers HCC19 Diabetes without Complication HCC85 Congestive Heart Failure HCC83 Respiratory Arrest HCC107 Vascular Disease with Complications HCC108 Vascular Disease HCC111 Chronic Obstructive Pulmonary Disease HCC135 Acute Renal Failure

7 Risk Scores Sample Calculation Factor Formula Demographics Age: 75 Gender: Female (a) Disabled Status: Not Disabled (b) Original Reason for Entitlement: Beneficiary insured due to age (c) Medicaid Eligibility: Not Eligible for Medicaid (d) Total Demographic Component (e) = (a) + (b) + (c) + (d) Diagnosis Codes Associated with Beneficiary Diabetes Mellitus HCC (f) Urinary Tract Infection No associated with an HCC (g) Regional enteritis (small intestine) HCC (h) Total Diagnostic Component (i) = (f) + (g) + (h) Total Risk Score (i) = (e) + (i)

8 Medicare Risk Scores Timing Evaluating Risk Score at Different Points in time Year Time Period #1 Time Period #2 Time Period # Diagnosis period Additional diagnoses submitted through Date at which you are examining risk scores

9 How are Medicare risk scores used? Risk scores are used by CMS to determine the revenue to reimburse health plans for each of their beneficiaries. Each beneficiary is assigned their own risk score Results in health plans with older and/or sicker members getting relatively more revenue than health plans with younger and/or healthier members Evens the playing field so anti-selection is less of an issue

10 Medicare Risk Score Data Timing Risk score data is collected via Risk Adjustment Processing System (RAPS) data submissions from health plans to CMS. Initial submission for a calendar year is March of the following year, with final submission nine months later Initial submission for a non-calendar year (July 1 June 30) is September of the following year, with final submission nine months later

11 Medicare Risk Score Data Timing Graphic of Calendar Year Data Submissions yy = dates of service for which diagnosis data is collected initial submission final submission yy yy + 1 yy + 2 J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D

12 Medicare Risk Score Data Timing Graphic of Non-Calendar Year Data Submissions yy = dates of service for which diagnosis data is collected initial submission final submission yy yy + 1 yy + 2 J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D

13 Medicare Risk Score Data Sources for Plans Monthly Membership Reports (MMRs) Monthly accounting of plan revenue and risk scores Sent to health plans monthly Accompanies CMS payment to plans Risk scores are presented on a normalized basis Based on most recent risk score timing

14 Medicare Risk Score Data Sources for Plans Annual Beneficiary Files Annual detailed file of risk scores by beneficiary by month CMS produces the files each April Risk scores are presented on an unadjusted, non-normalized basis sometimes referred to as the raw risk score Based on previous year membership and completed, calendar year risk score data submission

15 Calculating Base Period Risk Score to Include in BPT Worksheet 1

16 Options for Calculating Projected Risk Score to Include in BPT Option 1 CMS Preferred Methodology Based on annual beneficiary file data from previous year Option 2 Alternate Methodology Based on YTD MMR file data (usually first quarter of current year)

17 Why choose one method over the other? Credibility considerations Data quality considerations Unusual enrollment situations for new plans

18 Risk Score Credibility - CMS Guidance Risk score credibility guidance memo can be found here: Items/BidGuidance.html?DLPage=1&DLEntries=10&DLSort=0&DLSortDir=descending

19 MA Risk Score Projection PREFERRED METHOD (bene file starting risk score) Development of 2016 Projected MA Risk Scores 2014 Description Model A Starting Risk Score B Convert to Raw - remove Normalization C Convert to Raw - remove MA Coding Pattern Adj D Plan Specific Coding Trend E Starting Data Adjustments i) Transition from lagged to non-lagged diagnosis data ii) Incomplete reporting of diagnosis data iii) Seasonality F Plan Specific Adjustments G Risk Model Adjustment i) Raw 2015 HPMS Posted Data ii) Missing Diagnosis Adjustment iii) Raw 2014 HPMS Posted Data H Raw Risk Scores, Projected to I MA Coding Pattern Adjustment J Normalization Factor K Frailty Factor (additive) - L Final Risk Score

20 ALTERNATE METHOD (YTD MMR starting risk score) Development of 2016 Projected MA Risk Scores 2014 Description Model A Starting Risk Score B Convert to Raw - remove Normalization C Convert to Raw - remove MA Coding Pattern Adj D Plan Specific Coding Trend E Starting Data Adjustments i) Transition from lagged to non-lagged diagnosis data ii) Incomplete reporting of diagnosis data iii) Seasonality F Plan Specific Adjustments G Risk Model Adjustment i) Raw 2015 HPMS Posted Data ii) Missing Diagnosis Adjustment iii) Raw 2014 HPMS Posted Data H Raw Risk Scores, Projected to I MA Coding Pattern Adjustment J Normalization Factor K Frailty Factor (additive) - L Final Risk Score

21 Risk Score Projection Starting Risk Score Risk score projections must start with a raw risk score no coding pattern adjustment or FFS normalization. MA coding pattern adjustment CMS adjustment to account for coding improvement over time Varies by year FFS Normalization factor CMS adjustment to normalize the total risk scores back to 1.0 Varies by year

22 Risk Score Projection Starting Risk Score Development of Raw Risk Score Preferred Method Using the starting risk score from the beneficiary file No need to remove normalization or coding trend since beneficiary file is already raw Raw risk score = A * B / C

23 Risk Score Projection Starting Risk Score Development of Raw Risk Score Alternate Method A Starting Risk Score B Convert to Raw - remove Normalization C Convert to Raw - remove MA Coding Pattern Adj Using the starting risk score from YTD MMR files Need to remove current year FFS normalization and coding adjustment since MMR files contain both Raw risk score = A * B / C

24 Risk Score Projection Plan Specific Coding Trend Applies to both preferred and alternative methods Plans must determine the appropriate trend for risk scores Risk score coding trend represents the plan s expected annual improvement in coding Appendix K of the 2015 MA BPT Instructions includes discussion of considerations for developing risk score trend

25 Risk Score Projection Timing Adjustments E Starting Data Adjustments i) Transition from lagged to non-lagged diagnosis data ii) Incomplete reporting of diagnosis data iii) Seasonality Applies only to alternate method, since beneficiary risk scores used in preferred method are already calendar year and complete. Timing adjustment = E = E.i * E.ii * E.iii

26 Risk Score Projection Timing Adjustment Definitions Lagged to non-lagged data converts data to a calendar year Incomplete data adjusts for final data submission Seasonality converts a partial year of data to a full year

27 Risk Score Projection Plan Specific Adjustments Applies to both preferred and alternative methods Plans must determine if any population adjustments must be made for differences between base period and projection period populations When using population adjustments to the risk score, you must consider the need for a corresponding claims adjustment

28 Risk Score Projection Risk Model Changes Applies to both preferred and alternative methods Accounts for changes in CMS HCC models Missing Diagnosis Adjustment needs to be calculated for any plans that filtered diagnosis data in RAPS submissions

29 Risk Score Projection Risk Model Changes G Risk Model Adjustment i) Raw 2015 HPMS Posted Data ii) Missing Diagnosis Adjustment iii) Raw 2014 HPMS Posted Data Risk model adjustment = G = G.i * G.ii / G.iii Note: Sometimes CMS will instruct plans to use a blend of two different HCC models to mitigate transition changes.

30 Risk Score Projection Projected Raw Risk Score A Starting Risk Score B Convert to Raw - remove Normalization C Convert to Raw - remove MA Coding Pattern Adj D Plan Specific Coding Trend E Starting Data Adjustments i) Transition from lagged to non-lagged diagnosis data ii) Incomplete reporting of diagnosis data iii) Seasonality F Plan Specific Adjustments G Risk Model Adjustment i) Raw 2015 HPMS Posted Data ii) Missing Diagnosis Adjustment iii) Raw 2014 HPMS Posted Data H Raw Risk Scores, Projected to Projected Raw Risk Score = H = A * B / C * D * E * F * G

31 Risk Score Projection Coding, FFS Normalization and Frailty Factor Applies to both preferred and alternative methods Raw projected risk score must be adjusted for projection year coding pattern and FFS normalization Frailty factor is an additive adjustment that applies to Fully Integrated Dual (FIDE) SNPs only.

32 Risk Score Projection Final Worksheet 5 Projected Risk Score H Raw Risk Scores, Projected to I MA Coding Pattern Adjustment J Normalization Factor K Frailty Factor (additive) - L Final Risk Score Final Projected Risk Score = L = ( H * I / J ) + K

33 Risk Scores and Medicare Revenue Raw risk score is sum of coefficients Published risk score calculated as follows: Raw risk score x Coding improvement / FFS Normalization Coding Improvement and FFS Normalization factors vary annually and are published in the CMS Rate Announcement FFS Normalization factor is different for ESRD and PACE Final risk payment is calculated as follows: Published risk score multiplied by ISAR factor (varies by county and is calculated in the bid form) multiplied by Bid submitted to CMS minus Basic Part C premium (if no rebates from bid)

34 Risk Scores and Medicare Revenue Additional payments from CMS: Part C Rebates for reduction in cost sharing or enhanced benefits (does not vary by member) Potential additional revenue from Part C member premium (if plan is not $0 premium)

35 The Influx of Baby Boomers Baby boomers = born between 1946 and 1964 Between 2011 and 2029, 8,000 baby boomers will turn 65 per day (AARP). That s 2.92 million per year! Increased likelihood to select MA plans More familiar with network-based plans, such as HMOs and PPOs More technically savvy Decreasing number of retiree health plan options New Enrollee risk score age/gender basis only After a full calendar year, risk score switched to reflect diagnoses

36 The Influx of Baby Boomers (cont d) Followed New Enrollees longitudinally from 2008 through 2012, 2009 through 2012, etc. $800 $700 $600 $500 $400 $ Actual Expected

37 Opportunities for Risk Score Improvement Dropped diagnoses Missing diagnoses Prioritization of HCCs Home visits Hire vendor Use of Commercial/Other data Other

38 Questions