medicare advantage risk adjustment predictive modeling and ... · by combining related services...

Medicare Advantage Risk Adjustment

Predictive Modeling and Stratification

April 22, 2015Scott C Howell, DO, MPH&TM, CPESenior Executive, Heritage Provider Network

Sir William Osler

“No human being is constituted to know the truth, the whole truth and nothing but the truth; and even the best of men must be content with fragments, with partial glimpses, never the full fruition”

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Versatility of the HCC Model

Risk ModelInputs

Populations

Predictive Results

Prediction Year

Classification

Prescription Drugs

Budgeting

Exchange“Hx-HCCs”

Pregnancy, Infants, Peds, Adult

Medical and Rx

Concurrent

Additive

Not Used in Modeling

Budget Neutral

Medicare“CMS-HCCs”

Medi-Medi, Seniors

Medical: Parts A and B

Prospective

Additive

Separate Rx HCC Model

Non Budget Neutral

3Rs for Commercial Exchange

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Risk Adjustment 101Methodology implemented by CMS

– Mandated by the Balanced Budget Act of 1997• Model collects information this year to establish cost of patient

care for next calendar year

Each patient is assigned a Risk Adjustment Factor (RAF)• RAF is a numeric value assigned by CMS to identify the health status of a

patient.

– RAF score is made up of the following criteria for each patient:1. A demographic RAF based on age & sex2. Additional risk factors are added for Medicaid status & if patient was

eligible for Medicare due to a disability3. A RAF for the total of all chronic conditions and several disease

interactions

Normalization to average Medicare patient 1.00

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Risk Adjustment 101HCC (Hierarchical Condition Category)

– The diagnosis codes are recorded at least once per year.

– Clinically homogeneous groups of codes in the HCC model

– Each code falls into one and only one Diagnosis Group and codes are grouped into Condition Categories (CCs).

– There are a total of 79 different HCCs • Each HCC has different coefficient depending on the

future predicted cost for the next fiscal year.• HCC Coefficients are developed from the Fee-for-

Service population– Each year there is a coding intensity adjustment

at a minimum of 5.61%

Risk Adjustment 101

• ICD-10 transition reducing HCC submissions– ICD-10 codes of Omission– ICD-10 codes of Commission

• Chart review is not equivalent with ICD-10 codes

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Medicare Advantage Benchmarks & BIDS

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Basic 4 STAR Risk Ajustment1.05 Benchmark $945.001.00 Benchmark CMS $900.00 $900.00 $900.00Risk Score Plan Population 0.95 0.95 1.15Risk-Adjusted Benchmark $855.00 $897.75 $1,035.00

1.00 Bid $842.11 $842.11 $695.65Risk-Adjusted Bid Plan Estimate $800.00 $800.00 $800.00

MA Basic Premium $0.00 $0.00 $0.00

Savings $55.00 $97.75 $235.00Rebate % 60% 70% 60%Rebate $33.00 $68.43 $141.00

Member Premium $0.00 $0.00 $0.00

Total Revenue $833.00 $868.43 $941.00

Risk Adjustment 101

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Adopted from Optum

Current PY Relative

HCC Factor

Current PMPY Revenue @

100%018 Diabetes With Chronic Complications 0.3509 $3,158.09019 Diabetes Without Complication 0.1125 $1,012.65

023Oth Significant Endocrine And Metabolic

Disorders 0.2336 $2,102.53

058Major Depressive, Bipolar, And Paranoid

Disorders 0.3147 $2,831.98085 Congestive Heart Failure 0.3509 $3,158.09107 Vascular Disease With Complications 0.3909 $3,518.52108 Vascular Disease 0.2851 $2,565.94111 COPD 0.3299 $2,969.29134 Dialysis Status 0.4539 $4,084.91136 CKD, Stage 5 0.2136 $1,922.31137 CKD, Severe (Stage 4) 0.2136 $1,922.31

Population verse Individual Predictive Risk

There is the ability to predict a population’s future cost with some reasonable certainty given incidence of disease.

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While predicting individual incidence is exceptionally more difficult known for members moving into the high cost tier.

Model Population Approach to HCC – Concept of Total Uncovered RAF

Screening

Enhanced Logic for Additional HCCs

FoundationalRecapture of ALL Chronic Conditions

• Foundation to Re-Capture all Chronic Illnesses Year over Year

• Identifies New and Unique Chronic Disease States through Screening based on Evidenced Based Protocols

• Analytics based on Pharmacy, Laboratory,HCPCs, CPTs for additional non-documented RAF

Local Physician Organization (LPO) Quintile Members Uncovered RAF Total Uncovered RAF

SuspectsConversion Discount

Total

1 5260 9198.997 $ 42,768,442 $ 21,384,221

2 5260 3665.867 $ 13,581,991 $ 6,795,495

3 5260 359.33 $ 5,982,632 $ 2,371,736

4 5260 0 $ - $ -

5 8520 0 $ - $ -

Total 26,400 7203.974 $ 59,433,305 $ 30,551,446

Distribution of HCCs in a Projected Medical Group

“Total Uncovered RAF Opportunity Possible within the Medical Group

“At 50% conversion of rate

Example at Local Physician Organization

Screening HCCs

• Major Depression (All seniors)• COPD (Based on Risk Factors)• Secondary Hyperparathyriodism in Renal

Disease (eGFR < 45 ml/min)• Vascular Disease (Based on Risk Factors)• Diabetic Complications (All diabetics)

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Secondary Hyperparathyroidism

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KDIGO Guidelines recommend Screening at least once with eGFR < 45 ml/min for PTH; results of > 70 picogm/ dl is considered “positive”

Different Risk Adjustment Models Model Feature Adjusted

Clinical Risk Groups (ACG)

Chronic Illness Disability Payment System (CDPS)

Clinical Risk Group (CRGs)

Diagnostic Risk Groups (DCG)

Episodic Risk Groups (ERGs)

Developer Johns Hopkins University of California San Diego

3M Health Information Systems

Verisk Health Optum / Ingenix

Introduction 1992 1996 2000 1996 2001

Diagnosis Single Dx Single Dx Single DxInpatient or twoDx Professional

Single Dx Single Dx from F2F encounter or inpatient admission

Condition Acute and Chronic

Chronic Only Acute and Chronic

Acute and Chronic

Acute and Chronic

Classification Categorical Additive Categorical Additive Additive

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Addition of Pharmacy MRX, DxCG

ETG Level Setting• Episodic Treatment Groups (ETGs): By combining related services into clinically homogenous units that describe complete

episodes of care, ETGs may be utilized to provide the basis of valid comparisons. ETGs create episodes by collecting all inpatient, outpatient, and ancillary services into mutually exclusive and exhaustive categories.

– Each specific ETG has an Anchor Record (Management, Surgery, Facility), Clusters and Non-Anchoring Records (Pharmacy and Ancillary)

– Each ETG include additional information of complications, co-morbidities and active treatment (surgical or active management i.e. chemotherapy)

[XXXXXX] [0/1] [0/1] [0/1]Base ETG Complications Co-Morbidities Treatments

Chronic Bronchitis ETG Example: Anchor, Cluster and Ancillary

Timeline

Pulmonologist Finish Oral AbxAnchor Record Follow-up PCP Clean PeriodDiagnostics•

Finished Biaxin BID x 14 days

Primary Care: cough, fever, sputum production, pleuritic chest pain

Pulmonary referralSpirometry, ABG

Clean PeriodCBC, CHEM-7, CXR (Right Lower Lobe Pneumonia)

Resolution of Chronic Bronchitis,

ETG Level Setting

The Severity Weight of 1.0 means the episode has the same average expected resource cost compared with other episodes of the same BASE ETG

This example demonstrates a 6.8 times increase from BASE ETG

ETG Level Setting

The example shows a 1.60 increase in BASE ETG

Diabetes, Severity Levels (1-4)

Example of distribution of diabetes Mellitus Severity Levels 1-4 and associated observed and expected costs

Increasing observed and expected costs with higher severity levels

Practice Variations with Defined ETGs

Clinically similar episodes of care allows direct comparison between Dr. Jones and Smith

Risk Adjustment Limitations

• The current predictive models have the standard and customary issues of claims / encounters– The models do not consistently include the social

determinants of care that are significant drivers• Use of proxy measures for social determinants, such as

Medi-Medi with an additional coefficient • Models can target the risk associated with disease and

over multiple regressions can determine in general members with additional needs

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