Risk Adjustment DataFor Business Insight

Health Care Service Corporation

September 2012

Agenda

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Risk Adjustment Data – Regulatory Needs

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Definitions

• Risk Scoring The process of evaluating and ranking relative exposure (medical / cost) of a

population

• Risk Adjustment Mitigating exposure through making adjustments to the population or the outcomes

• Prospective versus Concurrent Future time period versus existing / current time period

• Morbidity Profile The occurrence pattern of disease, injury and illness in a population

• Risk Management A formal program which assesses, ranks and takes actions design to reduce / control

exposure

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• Expectations for ACA Risk Adjustment model:• Similar to MA HCC model but calibrated to a commercial population

• No pharmacy data to be used in initial risk scoring model

• Concurrent risk score

Risk Adjustment Models

Model Used In Data Elements Time Period

Hierarchical Condition Category (HCC)

Medicare Advantage

DemographicInpatient and outpatient diagnosis dataMedicaid statusMedicare entitlement

Prospective

Chronic Illness and Disability Payment System (CDPS)

Some state Medicaid programs

DemographicDiagnosis for chronic conditions onlyPharmacy data

Concurrent or Prospective

Wakely Phase 1 of Wakely’s national risk adjustment simulation

DemographicInpatient and outpatient diagnosis dataPharmacy data

Concurrent or Prospective

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Business Need: Members’ Medical Conditions Need to Appear on Claims

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• Crucial that all of a member’s medical conditions (diagnoses) appear on claims each year

• Identify members who are likely to have medical conditions that do not appear on claims

• Intervene to make sure that the diagnoses do appear on claims

ACA Risk Adjustment

Member Scorecard

• Identify members who are likely to have medical conditions that do not appear on claims

The absence of diagnoses can’t be directly observed in claims data -- it must be inferred

One metric is “diagnostic non-persistency”

Estimate, at the individual member level, the sum of risk weights of medical conditions that we think a member has, but do not appear on claims

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Member Scorecard - Example

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Member #26318

Diagnoses for –

Central Nervous System Disorders (medium severity, risk weight = 1.31) and

Cancer (very high severity, risk weight = 5.67)

Did not have these diagnoses (they were non-persistent)

Member scorecard score = 6.98 (= expected risk score increment if the diagnoses appeared on claims)

Member Scorecard – Potential Intervention

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May vary incentive levels for visit by the member scorecard scores (higher scores indicate need for greater incentives)

Member Scorecard – Future Enhancements

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• Incorporate scores from models predicting which members have specific medical conditions

• Focus on predicting which members are likely to have specific Dxs (e.g., musculoskeletal and gastroenterological conditions) based on other available information, such as CPTs and Rxs

Provider Scorecard

• Identify providers with a tendency to have high proportion of patients with medical conditions that do not appear on claims

Similar to the issue at the member level, absence of diagnoses must be inferred

Like the Member Scorecard, primary metric is non-persistency (as well as some lower-weighted metrics such as tendency to use vague/non-specific diagnoses)

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Provider Scorecard - Interventions

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Provider Scorecard – Future Enhancements

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• Provider scorecard scores adjusted for providers’ patient case-mix

• Improve scorecards by using chart review results as an objective “target”

Business Insight – Risk Identification

• Actual Risk Profile• Understand the current and emerging health profile of enrolled members

• Risk score, demographic information, rating information

• Compare Insurer profile to market where feasible

• Self Reported Risk Profile• Use health risk assessment response information

• Establish credibility between health risk assessment and claim record

• Responses can support member and provider interventions

• Risk Profile and Actual Costs• Reimbursement of conditions based on the risk scoring model and how this

compares to the Insurer actual reimbursement rates

• Link results from above items into care management and provider interventions

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Business Insight – Risk Identification

• It’s all about the DATA• Getting, storing and retrieving the data, with no loss to its accuracy and

integrity, is key to all analytics, interventions, forecasts and financial settlements

• Actions taken to improve diagnosis code capture can impact the ACA risk adjustment financial settlement for the incurred calendar year that the intervention is taken in

• ACA risk adjustment settlement is based on an incurred calendar year with 3 months of run-out

• Need to get the data right in this time frame

• Different from MA where interventions impact the next year’s risk adjusted revenue if that member stays enrolled with the MA plan

• Insurer loses the benefit of interventions if member switches plans for the next year

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Business Insight – Benefits

• Better Understanding of the Needs of Your Members

• Opportunities in the areas of Member and Provider

• Claims Coded More Accurately

• Accurate Regulatory Data Delivery

• Increased Opportunity for Medical Management Programs

• More Accurate Claims Processing

• More Accurate Information for Provider Contracting

• Reduced Cost – Medical and Administrative

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Summary

Questions?

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