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

2

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

3

History of Predictive Modelingat Blue Cross & Blue Shield of Rhode Island

1990s

2000

2001

2003

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

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

Beta tested Johns Hopkins Predictive Modeling Software

Timeline

2006 Incorporated Predictive Models into Underwriting process

2008

Incorporated detailed Pharmacy Data into Predictive Modeling process

Included Dental data in Predictive Modeling process when available

2007 Health risk appraisal data included in Predictive Modeling process

Introduced dynamic variables to identify changes in member health status for earlier identification

Remodeling Process for Disease Segmentation

Implemented BHI Risk Score Benchmarks into Account Reporting & Analysis

4

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

5

Blue Cross & Blue Shield of Rhode Island

Distribution of Members by Product Type

Small Account (Size < 50)

15%

Medicare

11%

Individual Programs

3%

Large Account (Size > 50)

65%

Other

6%

6

Small Group Underwriting

Many Factors Involved in an Account’s Final Rates

Final Rates

TrendsPools

Experience

Admin.Expense

Reserve Contribution

State Regulations

Medical Risk

7

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

8

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

9

Small Group Underwriting – System Support

10

Small Group Underwriting – System Support

11

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

12

Small Group Underwriting - Evaluation

Actual Expense Consistent with Rating 2nd Quarter 2007 Results

71%

29%

Ranked Consistent w ith Expense Ranked Inconsistent w ith Expense

Related To Median

13

Large Group Population

71%

22%

7%

<100 100-499 500+

BCBSRI’s Large Group Market

– 385,000 Members– 550 Accounts

12%

16%

72%

<100 100-499 500+

% of Accounts % of Members

Account Size

14

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

15

Underwriting for IER Commercial Renewals

Account Information PM (Relative to Commercial Pool)

Account Number Account Name

Self Funded Cycle

Total Contract

Total Risk Score

%

High Risk

4H07 Tony’s 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%

Predictive Modeling

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

16

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

17

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

18

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

19

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

20

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

21

Case Management – Predictive Modeling

Decision Tree

22

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

23

Case Management – System Implementation

Claimno LineN MemberidFieldbreak

Proccode

Fieldbreak2

Fromdate

Fieldbreak3

Servicetype

200804000032 0032 BCBSRIMEMB0031 ||||||||||||||||| PMCMH ||||| 20080422 |||||||||||||||| PM DATA





200804000894 0894 BCBSRIMEMB0893 ||||||||||||||||| PMCMM ||||| 20080422 |||||||||||||||| PM DATA



200804001632 1632 BCBSRIMEMB1631 ||||||||||||||||| PMCML ||||| 20080422 |||||||||||||||| PM DATA



200804003939 3939 BCBSRIMEMB3938 ||||||||||||||||| PMCMA ||||| 20080422 |||||||||||||||| PM DATA





Predictive Modeling – Disease Monitor File

24

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

25

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

26

Disease Management - Diabetes

Medical Expense Distribution

BC Commercial Population

0.4% 0.2%5.0%

20.2%

12.9%8.2%

52.0%

1.2%0%

10%

20%

30%

40%

50%

60%

0- 999 1,000- 2,499 2,500- 4,999 5,000- 9,999 10,000-24,999 25,000-49,999 50,000-99,999 100,000+

Expense Category ($)

Per

cen

t of

Mem

ber

s

BC Diabetes Members

2.0% 0.9%5.1%8.4%

25.7%23.9%

16.5% 17.6%

0%

10%

20%

30%

40%

50%

60%

0- 999 1,000- 2,499 2,500- 4,999 5,000- 9,999 10,000-24,999 25,000-49,999 50,000-99,999 100,000+

Expense Category ($)

Percen

t o

f M

em

bers

27

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

28

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

29

Disease Management - Diabetes

30

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

31

Questions?

blue cross & blue shield of rhode island

Documents