Blue Cross

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Blue Cross Blue Shield of Rhode Island
New Approaches Focusing on Dynamic Variables
Related to Changes in Members Health Status
Suizhou Xue September 2008
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Background and Objectives

• Predictive Modeling at Blue Cross Blue Shield
  of Rhode Island
• Predictive Modeling for Underwriting Small Group
  and Large Group
• Predictive Modeling for Case Management
• New Approaches on Dynamic Variables for Early
  Identification
• Predictive Modeling for Disease Management
• Blue Health Intelligence for Risk Analysis

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History of Predictive Modelingat Blue Cross
Blue Shield of Rhode Island
Timeline
Rules based predictive models used for Case
Management identification
1990s
2000
Began researching Predictive Modeling/Data Mining
methodologies and software
2001
Developed Predictive Models for Case and Disease
Management using statistical methods
2003
Beta tested Johns Hopkins Predictive Modeling
Software
Incorporated Johns Hopkins Predictive Models
output into Case and Disease Management
Initiatives
2006
Incorporated Predictive Models into Underwriting
process
2007
Health risk appraisal data included in Predictive
Modeling process
Incorporated detailed Pharmacy Data into
Predictive Modeling process
Included Dental data in Predictive Modeling
process when available
2008
Remodeling Process for Disease Segmentation
Introduced dynamic variables to identify changes
in member health status for earlier identification
Implemented BHI Risk Score Benchmarks into
Account Reporting Analysis
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Technology of Predictive Modeling

• Johns Hopkins Predictive Models based on both
  Diagnosis (Dx) and Pharmacy (Rx) Information
• Angoss KnowledgeSTUDIO Predictive Model / Data
  Mining
• Combination of software allows for customization
  and inclusion of claims, PHA, gaps in care,
  biometric and dynamic variables
• Blue Health Intelligence DCG Benchmark and
  Statistics

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Blue Cross Blue Shield of Rhode Island
Distribution of Members by Product Type
Other 6
Individual Programs 3
Medicare 11
Large Account (Size gt 50) 65
Small Account (Size lt 50) 15
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Small Group Underwriting
Many Factors Involved in an Accounts Final Rates
Pools Experience
Trends
Admin. Expense
Final Rates
Medical Risk
Reserve Contribution
State Regulations
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Small Group Underwriting - Testing Mode

• Scored individual members by ACG Predictive
  Modeling Score (PM) and Manual Medical
  Underwriting Points (MU), and summarized to an
  account score
• Compared raw scores and ranking of account PM vs.
  MU
• Correlation coefficient of about .70
• Created a Set of Conversion Parameters between PM
  and MU through regression

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Small Group Underwriting - Implementation

• Implemented for 4th quarter 2006 cycle accounts
• Developed supporting system for ongoing outlier
  review and virtual medical record access
• Outlier criteria includes extreme PM values and
  loss ratios
• Medical Underwriting reviewed 15 of accounts
  based on criteria
• Only modified 3 of those reviewed
• Successfully delivered final score July 2006, and
  replaced manual medical underwriting system

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Small Group Underwriting System Support
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Small Group Underwriting System Support
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Small Group Underwriting - Results

• Reduced cycle timeframe from 6 to 3 months
• Allows for more current claims experience
• Reduced Medical Underwriting Staff
• Improved accuracy of Medical Underwriting
• Improved consistency and justification of results
• Coordinated Corporate Predictive Modeling
  activities

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Small Group Underwriting - Evaluation

• Actual Expense Consistent with Rating
• 2nd Quarter 2007 Results

Related To Median
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Large Group Population

• BCBSRIs Large Group Market
• 385,000 Members
• 550 Accounts

of Members
of Accounts
Account Size
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Large Group (IER) Predictive Modeling

• General Process
• Produce electronic file with Predictive Modeling
  scores for each account in rating cycle
• Relate PM scores to specified comparable
  population
• Two comparable statistics for each account
  provided to underwriters
• Percent difference between accounts overall PM
  score and the community score
• Percent difference of account proportion of high
  risk members compared to communities proportion
  of high risk members

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Underwriting for IER Commercial Renewals
Predictive Modeling Claims Incurred 01/2007
12/2007, Paid 12/2007
Account Information Account Information Account Information Account Information Account Information PM (Relative to Commercial Pool) PM (Relative to Commercial Pool)
Account Number Account Name Self Funded Cycle Total Contract Total Risk Score High Risk
4H07 Tonys Incorporated Y May 431 9.62 64.91
959 Metro Properties N May 57 8.58 -24.56
3943 Leah Cosmetics N June 59 34.89 7.02
5V53 Goldmine Jewels N June 164 -8.27 -14.04
100444 Colonial Groceries N June 84 -11.73 -3.51
3129 Eric Simmons, Inc. N July 231 18.48 46.49
1A126 Michelle Co. N July 1,308 5.18 -10.53
102329 Califano Group N July 1,606 -26.19 -50.00




Total Quarter 16,102 1.23 -0.88
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Case Management

• Objectives
• Identify members who are likely to be high
  risk/high cost in the future
• Drill down to explain the major components that
  contribute to the risk factor
• Intervention
• Members whose health can be improved
• Members who are most likely to incur future cost
  savings
• Collaborate care

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Case Management PM Status

• Predictive Modeling Member
• Demographic Information
• Cost Distribution
• Predictive Modeling Risk Probability
• Hospital Dominant Marker
• Disease and Condition Profile
• Virtual Medical Record
• - By Type of Service
• - Chronological
• Case Management / Disease Management Information
• Quarterly Update

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Case Management - Challenges

• Challenges in Predictive Modeling
• Enhance model for predictive accuracy, and reduce
  false positive members
• Early identification for members whose health
  status could be changed in the future
• How can the predictive modeling program maximize
  its value to the case management program
• Actionability
• Timing and scope of intervention

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Case Management Future Health Status

• Prospective Member Health Status
• Its critical for Case Management to identify the
  members who will change health status in the
  future for possible early intervention
• Medical claims, especially pharmacy data incurred
  6 months or less, instead of 12 months, were
  sometimes used for Case Management. It was
  considered that the recent claims experience was
  strongly associated with future health risks
• Generally speaking, a disease or condition is
  changed within a certain analysis period.
  Prospective expense for the coming year will be
  different depending on the conditions incurred in
  the beginning of the year and end of the year
• Should consider weighing the conditions incurred
  in different analysis periods

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Case Management PM Enhancement Test

• Predictive Modeling Dynamic Variables
• Introduced dynamic variables those variables
  change their values during the period of claim
  experiences, such as medical utilization, visits
  and tests. They can be expressed as their
  values, rankings, or moving ratios by quarter or
  month, for example, quarterly medical expenses
  and their moving ratios (4th qtr expense vs. 3rd
  qtr expense, etc.)
• Combination of ACG Predictive Modeling results,
  utilization, measures, and dynamic variables
  allow us to customize the plan data and build the
  enhanced predictive models Neural Network and
  Decision Trees
• The dynamic variables, featured at the end of the
  claims period are displayed near the top of the
  splits in the Decision Tree Predictive Model.
  Similarly, the dynamic variables also showed the
  strong contribution in the Neural Network
  Predictive Model

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Case Management Predictive Modeling

• Decision Tree

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Case Management A New Approach

• Predictive Modeling A New Approach
• The strong prediction power of the dynamic
  variables implies that the prediction accuracy
  will increase progressively from past to present
  medical experiences the current claims reflect
  more in members future health status
• We tested three models for the latest claims for
  early identification 1) ACG predictive modeling
  with local calibration 2) Customized model by
  neural network and 3) ACG predictive modeling
• Moved from quarterly, monthly, bi-weekly to
  weekly. The members selected for Case Management
  intervention are those with a probability
  difference of 0.7 between current weekly results
  and quarter base file.
• Implemented the weekly predictive modeling
  results into McKesson Disease Monitor System.
  The exception rule of the system makes more
  efficient use of the predictive modeling results

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Case Management System Implementation
Predictive Modeling Disease Monitor File
Claimno LineN Memberid Field break Proc code Field break2 From date Field break3 Service type
200804000032 0032 BCBSRIMEMB0031 PMCMH 20080422 PM DATA
200804000033 0033 BCBSRIMEMB0032 PMCMH 20080422 PM DATA
200804000034 0034 BCBSRIMEMB0033 PMCMH 20080422 PM DATA
200804000035 0035 BCBSRIMEMB0034 PMCMH 20080422 PM DATA
200804000036 0036 BCBSRIMEMB0035 PMCMH 20080422 PM DATA
200804000894 0894 BCBSRIMEMB0893 PMCMM 20080422 PM DATA
200804000899 0899 BCBSRIMEMB0898 PMCMM 20080422 PM DATA
200804000900 0900 BCBSRIMEMB0899 PMCMM 20080422 PM DATA
200804001632 1632 BCBSRIMEMB1631 PMCML 20080422 PM DATA
200804001634 1634 BCBSRIMEMB1633 PMCML 20080422 PM DATA
200804001635 1635 BCBSRIMEMB1634 PMCML 20080422 PM DATA
200804003939 3939 BCBSRIMEMB3938 PMCMA 20080422 PM DATA
200804003944 3944 BCBSRIMEMB3943 PMCMA 20080422 PM DATA
200804003945 3945 BCBSRIMEMB3944 PMCMA 20080422 PM DATA
200804003946 3946 BCBSRIMEMB3945 PMCMA 20080422 PM DATA
200804003947 3947 BCBSRIMEMB3946 PMCMA 20080422 PM DATA
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Case Management

• Results (Challenges) in Predictive Modeling
• Enhance model for predictive accuracy, and reduce
  false positive members Combined ACG predictive
  modeling results and other measures including
  dynamic variables. Decision Tree and Neural
  Network models increase the prediction accuracy
• Early identification for members whose health
  status could be changed in the future Reduce
  time to weekly engagement in Predictive Modeling
• How can predictive modeling program maximize its
  value to case management program Implemented
  the results into McKesson Disease Monitor System
• Timing and scope of intervention Produced
  weekly member list with the highest risk scores,
  and grouped members in different risk tiers for
  weekly intervention

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Disease Management

• Objectives
• Identify members who are likely to be high risk/
• high cost in the future within a disease segment
• Diabetes, Asthma, Heart Disease, Hypertension,
  Cancer, Depression, etc.
• Co-morbidity
• Stratification of risk score for intervention

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Disease Management - Diabetes

• Medical Expense Distribution

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Disease Management

• Predictive Modeling A New Approach
• The difference in expense distribution between
  general commercial population and specific
  population indicates that its necessary to build
  a new model for a disease population rather than
  use the model for commercial population
• The lack of sufficient population size prohibits
  us from calibrating model locally for a specific
  disease
• Combination of ACG predictive modeling results
  and inclusion of utilization, measures, and
  dynamic variables, etc. allows us to build the
  robust predictive model through neural network
  and decision trees

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Disease Management - Results

• Predictive Modeling Results
• The customized model for diabetic members
  increases nearly 20 of predictive accuracy
  compared to the general predictive model for
  commercial population
• Stratification based on the predicted risk score
  and evaluation of co-morbidity
• Produce a member listing for intervention

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Disease Management - Diabetes
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Blue Health Intelligence Risk Analysis

• DCG Risk Scores
• Brings together the claims experience of 79
  million BCBS members nationwide
• Detailed DCG risk score benchmarks by geography,
  industry and company size
• BCBSRI analytical team will be actively
  incorporating BHI DCG risk score benchmarks into
  analysis and reporting

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• Questions?