blue cross blue shield of rhode island n.
Skip this Video
Loading SlideShow in 5 Seconds..
Blue Cross & Blue Shield of Rhode Island PowerPoint Presentation
Download Presentation
Blue Cross & Blue Shield of Rhode Island

Loading in 2 Seconds...

play fullscreen
1 / 31

Blue Cross & Blue Shield of Rhode Island - PowerPoint PPT Presentation

  • Uploaded on

Blue Cross & Blue Shield of Rhode Island. New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status. Suizhou Xue September 2008. Background and Objectives. Predictive Modeling at Blue Cross & Blue Shield of Rhode Island

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Blue Cross & Blue Shield of Rhode Island' - wade-durham

Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
blue cross blue shield of rhode island

Blue Cross & Blue Shield of Rhode Island

New Approaches Focusing on Dynamic Variables Related to Changes in Member’s Health Status

Suizhou Xue

September 2008

background and objectives
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
history of predictive modeling at blue cross blue shield of rhode island
History of Predictive Modelingat Blue Cross & Blue Shield of Rhode Island


Rules based predictive models used for Case Management identification



Began researching Predictive Modeling/Data Mining methodologies and software


Developed Predictive Models for Case and Disease Management using statistical methods


Beta tested Johns Hopkins Predictive Modeling Software

Incorporated Johns Hopkins Predictive Models output into Case and Disease Management Initiatives


Incorporated Predictive Models into Underwriting process


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


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

technology of predictive modeling
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

Blue Cross & Blue Shield of Rhode Island

Distribution of Members by Product Type



Individual Programs




Large Account (Size > 50)


Small Account (Size < 50)


small group underwriting
Small Group Underwriting

Many Factors Involved in an Account’s Final Rates

Pools Experience




Final Rates

Medical Risk

Reserve Contribution

State Regulations

small group underwriting testing mode
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
small group underwriting implementation
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
small group underwriting results
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
small group underwriting evaluation
Small Group Underwriting - Evaluation

Actual Expense Consistent with Rating

2nd Quarter 2007 Results

Related To Median

large group population
Large Group Population

BCBSRI’s Large Group Market

  • 385,000 Members
  • 550 Accounts

% of Members

% of Accounts

Account Size

large group ier predictive modeling
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 account’s overall PM score and the community score
    • Percent difference of account proportion of high risk members compared to communities’ proportion of high risk members
underwriting for ier commercial renewals
Underwriting for IER Commercial Renewals

Predictive Modeling

Claims Incurred 01/2007 – 12/2007, Paid 12/2007

case management
Case Management


  • 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
case management pm status
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
case management challenges
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
case management future health status
Case Management – Future Health Status

Prospective Member Health Status:

  • It’s 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
case management pm enhancement test
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
case management a new approach
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 member’s 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
case management system implementation
Case Management – System Implementation

Predictive Modeling – Disease Monitor File

case management1
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
disease management
Disease Management


  • 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
disease management diabetes
Disease Management - Diabetes

Medical Expense Distribution

disease management1
Disease Management

Predictive Modeling – A New Approach:

  • The difference in expense distribution between general commercial population and specific population indicates that it’s 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
disease management results
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
blue health intelligence risk analysis
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