Risk Adjustment Data
For Business Insight
Health Care Service Corporation
September 2012
Agenda
• Risk Adjustment Data – Regulatory Needs
• Definitions
• Risk Adjustment Models
• Business Needs – Accurate Diagnosis
• Member Scorecard
• Provider Scorecard
• Business Insight
• Summary - Questions
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Risk Adjustment Data – Regulatory Needs
Medicare Advantage – HHS / CMS
Medicaid - State
Medicare Part D
ACA Risk Adjustment
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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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Risk Adjustment Models
Model
Used In
Data Elements
Time Period
Hierarchical
Condition
Category (HCC)
Medicare
Advantage
Demographic
Inpatient and outpatient diagnosis data
Medicaid status
Medicare entitlement
Prospective
Chronic Illness
and Disability
Payment System
(CDPS)
Some state
Medicaid
programs
Demographic
Diagnosis for chronic conditions only
Pharmacy data
Concurrent or
Prospective
Wakely
Phase 1 of
Wakely’s national
risk adjustment
simulation
Demographic
Inpatient and outpatient diagnosis data
Pharmacy data
Concurrent or
Prospective
• 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
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Business Need: Members’ Medical
Conditions Need to Appear on Claims
ACA Risk Adjustment
• 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
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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
Diagnoses for –
 Central Nervous System Disorders (medium
severity, risk weight = 1.31) and
 Cancer (very high severity, risk weight = 5.67)
Member #26318
Did not have these diagnoses (they were nonpersistent)
Member scorecard score = 6.98 (= expected risk score
increment if the diagnoses appeared on claims)
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Member Scorecard – Potential Intervention
May vary incentive levels for visit by the member scorecard scores
(higher scores indicate need for greater incentives)
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Member Scorecard – Future Enhancements
• 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
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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
• Provider scorecard scores adjusted for providers’ patient
case-mix
• Improve scorecards by using chart review results as an
objective “target”
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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
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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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