refinements to the cms-hcc model for risk adjustment of medicare capitation payments

Refinements to the CMS-HCC Model For Risk Adjustment of Medicare Capitation Payments

Contact: John Kautter, PhD, jkautter@rti.org

RTI International is a trade name of Research Triangle Institute.

Presented by:

John Kautter, Ph.D.Gregory Pope, M.S.Eric Olmsted, Ph.D.

RTI International

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History of Medicare Risk Adjustment

Demographics (AAPCC) Doesn’t explain cost variation Favorable selection => higher program costs

Principal inpatient diagnoses (PIP-DCG model, 2000) Incentive to admit Penalizes plans that avoid admissions

Inpatient and ambulatory diagnoses (2004)

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CMS-HCC Model

Centers for Medicare & Medicaid Services (CMS) Hierarchical Condition Categories (HCC) model

Prospective Inpatient and outpatient diagnoses w/o

distinction 70 diagnostic categories (HCCs) Hierarchical within diseases

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CMS-HCC Model (continued)

Cumulative (additive) across diseases

6 disease interactions

Discretionary diagnoses are excluded

Demographic factors included

Calibrated on 1999/2000 Medicare 5% Sample

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CMS-HCC Model Performance

Percentage of cost variation explained Age/Sex: 0.8% PIP-DCG: 5.5% CMS-HCC: 10.0%

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CMS-HCC Models for Medicare Subpopulations

Disabled

End-stage renal disease

Institutionalized

New enrollees

Secondary payer status

Frail elderly

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Disabled

Over 10% of Medicare population

Under age 65

Model estimated separately for aged and disabled Overall cost patterns similar For 5 diagnostic categories, incremental

expense of the disabled is higher

5 disease interactions for disabled in final CMS-HCC model

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End-Stage Renal Disease

About 1% of Medicare population

Very expensive: approximately $50,000/year

3-segment model Dialysis patients

CMS-HCC model calibrated on dialysis patients

Transplant period (3 months) Lump-sum payment

Post-transplant period Aged/disabled CMS-HCC model w/add-

on for drugs

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Institutionalized Beneficiaries

About 5% of Medicare population

Costly, but less expensive than community residents for same diagnostic profile

Combined CMS-HCC model Overpredicts costs for institutionalized Underpredicts costs for community frail

elderly

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Institutionalized Beneficiaries (continued)

Different cost patterns by age and diagnosis for community and institutionalized

CMS-HCC model calibrated separately on community and institutionalized

Current year institutional status reported by nursing homes

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New Enrollees

Lack 12 months of base year enrollment

Two-thirds are 65 year olds

New enrollees versus continuing enrollees Much less costly at age 65 Similar costs at other ages

Merged new/continuing enrollee sample

Separate cost weights for 65 year olds

Demographic model

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Medicare as Secondary Payer

Beneficiaries with active employee employer-sponsored insurance

Costs are lower

Multiplier scales cost predictions down

Multiplier is ratio of mean actual to mean predicted expenditures

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Frail Elderly

Diagnosis-based models underpredict expenditures for the functionally impaired

Medicare specialty plans (e.g., PACE) serve functionally-impaired populations

Frailty adjuster to better predict their costs Predicts costs unexplained by CMS-HCC Based on difficulties in ADLs ADLs collected from surveys or assessments

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CMS-HCC Model Refinements

Additional HCCs added to model

100% institutional sample used for institutional model calibration

Changes in diagnostic classification

2002/2003 Medicare FFS data used for calibration of all models

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Availability of Additional HCCs

For Part D risk adjuster, plans required to submit diagnoses for 127 HCCs

Additional 57 HCCs available for CMS-HCC models (127 – 70 = 57)

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Adding HCCs

Benefits Greater accuracy in predicting illness burden Rewards plans who enroll and treat

beneficiaries with these diagnoses E.g., Special Needs Plans (SNPs)

Drawbacks Creates greater opportunities for diagnostic

“upcoding”

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HCCs Added to CMS-HCC Model

Available additional HCCs reviewed by project team to determine which were appropriate for payment model

Number of HCCs increased from 70 to 101

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Examples of HCCs Added to CMS-HCC Model

“Refined” CMS-HCC Model

HCC CommunityInstitutional

Type IDiabetesMellitus $1,557 $1,435

Dementia/CerebralDegeneration $1,576 − −

Hypertension $388 $919

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100% Institutional Sample

CMS-HCC institutional model calibrated on 5% institutional sample (n = 65,593)

To increase statistical accuracy and stability, “refined” CMS-HCC institutional model calibrated on 100% institutional sample (n = 1,238,842)

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Distribution of Annualized Medicare Expenditures, 2003

5% Community 100% Institutional

Sample Size 1,380,978 1,238,842

ExpendituresMean $6,541 $11,252

95th Percentile $31,285 $47,39090th Percentile $17,682 $31,553Median $1,445 $3,02810th Percentile $56 $5385th Percentile $0 $349

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Changes in Diagnostic Classification

Diabetes complications moved to diabetes hierarchy E.g., diabetic neuropathy moved from HCC

71 Polyneuropathy to HCC 16 Diabetes with Neurologic or Other Specified Manifestation

HCC 119 Proliferative Diabetic Retinopathy and Vitreous Hemorrhage deleted and most moved to HCC 18 Diabetes with Ophthalmologic or Unspecified Manifestation

Cerebral Palsy consolidated in HCC 70 Cerebral Palsy and Muscular Distrophy

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Refined CMS-HCC Community and Institutional Models

% of CostVariationExplained # HCCs

CMS-HCCCommunity 9.8% 70Institutional 6.0% 69

“Refined” CMS-HCCCommunity 11.0% 101Institutional 8.9% 90

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Refined CMS-HCC Model Performance – I

Predictive ratios, prior year expenditure quintiles

Age/Sex CMS-HCC

First 2.65 1.20

Second 1.82 1.19

Third 1.31 1.09

Fourth 0.91 0.99

Fifth 0.46 0.90

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Refined CMS-HCC Model Performance – II

Predicted ratios by CMS-HCC predicted expenditure deciles

Age/Sex CMS-HCCFirst 2.84 0.88Second 2.43 0.92Third 2.10 0.94Fourth 1.70 0.97Fifth 1.49 0.97Sixth 1.27 1.00Seventh 1.06 1.01Eighth 0.86 1.04Ninth 0.64 1.04Tenth 0.35 1.00

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Conclusions

Medicare risk adjustment has been evolving Demographic Inpatient All-Encounter (AAPCC) (PIP-DCG) (CMS-HCC)

The “refined” CMS-HCC model represents a more comprehensive all-encounter risk adjustment model Increases payment accuracy for plans

Viability of plans

– Beneficiaries’ access to plans