Mckesson payor conf implementing a pred. model

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Mckesson payor conf implementing a pred. model

  1. 1. CRMS & PREDICTIVE MODELING AT BCBSLA Blue Cross Blue Shield of Louisiana Felix J. Bradbury, RN, MHA, ScD*, CHE Director, Medical Management & Reporting Department September, 2003
  2. 2. AGENDA <ul><li>What is a predictive model? </li></ul><ul><li>Introduction and overview </li></ul><ul><li>BCBSLA health plan facts </li></ul><ul><li>How is Predictive Modeling Used to Support Our Care Management Initiatives? </li></ul><ul><li>Predictive model implementation issues </li></ul><ul><li>Our high-risk algorithm for care management </li></ul><ul><li>Using CRMS Fundamentals to improve reliability and validity </li></ul><ul><li>Using CRMS Fundamentals to enhance the predictive model referral process </li></ul>
  3. 3. What is a Predictive Model? A software application which generates a prospective index of risk and a probability of admission from a combination of medical, pharmacy and laboratory claims data. This data allows us to compare the expected future health status of an individual member, or the aggregate health status of a group, product or LOB.
  4. 4. What is a Predictive Model, Cont’d Two Important Definitions <ul><li>Relative Risk Score: Also called RR Score, it is a standardized score, or index, which enables us to make individual as well as aggregate comparisons between the health of members, groups, and products . In our population, RR Scores range from approximately 0.2-101. </li></ul><ul><ul><li>Example: A RR Score of “1.00” means a person is as healthy as “average”. A RR Score of “1.89”means a person is 89% sicker than average. </li></ul></ul><ul><li>Probability of Admission: Also called POA, this is the probability that a person will experience at least one inpatient hospital admission in the coming year. </li></ul><ul><ul><li>Example: RR Score of 0.90 means a member has a 90% chance of at least one admission. POA scores range from approximately 0.03-0.95 in our population. </li></ul></ul>
  5. 5. Introduction and Overview Forces driving strategic change in the health insurance industry
  6. 6. OVERVIEW: Why Do We have a Predictive Model? <ul><li>To analyze prospective health risk for our entire population, </li></ul><ul><li>To prospectively identify members who may: </li></ul><ul><ul><li>experience catastrophic medical and financial outcomes, or </li></ul></ul><ul><ul><li>be admitted to a hospital in the next year, </li></ul></ul><ul><li>To stratify patients for care and case management, </li></ul><ul><li>To help evaluate the ROI of our Care Management processes, and </li></ul><ul><li>To support the small-group renewal underwriting process. </li></ul>
  7. 7. Why Do we Have Predictive Model, Cont’d Overview of Member Risk Stratification
  8. 9. BCBSLA Healthplan Facts (Enrollment data current through June 2003) | Product LOB | BCBSLA PO BCBSLA PP BCBSLA Tr HMOLA HMO HMOLA POS | Total ----------------------+-------------------------------------------------------+---------- ASO | 33,936 30,229 2,496 2,641 4,886 | 74,188 Association | 15 46,221 125 1,538 3,566 | 51,465 Community | 1,251 54,496 656 7,690 14,499 | 78,592 Cost Plus | 0 2,633 573 2,076 625 | 5,907 FEP | 4 36,336 0 0 0 | 36,340 Individual Ble Value | 0 20,787 4,840 0 0 | 25,627 Individual Blue Max | 1 84,383 11,640 0 0 | 96,024 Individual Conversion | 0 0 130 0 0 | 130 Individual Regular | 0 0 210 18,068 10,645 | 28,923 Individual Value Plus | 0 0 226 0 0 | 226 Merit Rated | 7,096 32,139 11 14,957 6,729 | 60,932 Met | 746 59,044 1,128 5,300 18,706 | 84,924 National Control ASO | 0 5,352 3,708 0 0 | 9,060 National Control Insu | 0 0 39 0 0 | 39 Rate Stabilization | 1,078 7,316 305 0 0 | 8,699 ----------------------+-------------------------------------------------------+---------- Total | 44,127 378,936 26,087 52,270 59,656 | 561,076 <ul><li>Over 1 million total covered lives throughout Louisiana </li></ul><ul><li>Approximately 561,076 managed members as of June, 2003 </li></ul><ul><li>BCBSLA began using predictive modeling in August, 2002 </li></ul>
  9. 10. How is Predictive Modeling Used to Support Our Care Management Initiatives? <ul><li>We use the predictive model at the population level and then drill-down to the high-risk member level. This drill down process includes: </li></ul><ul><ul><li>Analysis of an employer group’s health status </li></ul></ul><ul><ul><ul><li>Peer Comparison, i.e., self-funded vs. fully-funded accounts </li></ul></ul></ul><ul><ul><ul><li>Blue Cross Blue Shield book of business comparison </li></ul></ul></ul><ul><ul><li>Analysis of specific diseases which drive employer’s costs </li></ul></ul><ul><ul><li>Identification of the most prevalent and costly conditions </li></ul></ul><ul><ul><li>Identification of actionable and predictable conditions </li></ul></ul><ul><ul><li>Identification of high-risk members </li></ul></ul><ul><ul><li>Stratification of actionable high-risk members into the most appropriate care management program </li></ul></ul><ul><ul><li>Referral of high-risk members into the appropriate program </li></ul></ul>
  10. 11. Predictive Model Implementation Issues <ul><li>Differences in expectations of clinical and reporting staff </li></ul><ul><ul><li>Mathematical models and clinical relevance </li></ul></ul><ul><ul><li>Referral lists and finding actionable, predictable members </li></ul></ul><ul><li>Determining the optimal predictive model algorithm </li></ul><ul><li>Difficulty determining the ideal volume of members to manage </li></ul><ul><li>Impersonal and unfamiliar technology </li></ul><ul><li>Lack of confidence in predictions </li></ul><ul><li>Impatience with the iterative developmental processes </li></ul><ul><li>Questions about how Care Management ROI will be measured </li></ul>
  11. 12. Implementation Issue 1: Differences in Expectations of Clinical and Technical Staff <ul><li>Mathematical models and clinical relevance </li></ul><ul><li>“ Perfect” referral list consists of members who are: </li></ul><ul><ul><ul><li>Predictable (Trauma vs. Chronic Disease) </li></ul></ul></ul><ul><ul><ul><li>Actionable (ESRD vs. Diabetes </li></ul></ul></ul><ul><ul><ul><li>Compliant with treatment regimen </li></ul></ul></ul><ul><ul><ul><li>Teachable </li></ul></ul></ul><ul><ul><ul><li>Easy to contact </li></ul></ul></ul><ul><ul><ul><li>Represent an ideal caseload for clinical staff </li></ul></ul></ul><ul><ul><ul><li>Likely to yield high return for resources consumed – High ROI </li></ul></ul></ul>
  12. 13. Implementation Issue 2: Difficulty Determining the Ideal Volume of Members to Manage <ul><li>How many members can your care management staff manage? </li></ul><ul><li>Volume of members referred to care management depends on: </li></ul><ul><ul><li>High-Risk selection process </li></ul></ul><ul><ul><li>Specific disease prevalence in your population, e.g., COPD, Diabetes, CHF </li></ul></ul><ul><ul><li>Acuity of referred members </li></ul></ul><ul><ul><li>Available nursing resources </li></ul></ul>
  13. 14. Implementation Issue 3: Impersonal Technology <ul><li>The predictive model may be perceived as: </li></ul><ul><ul><li>A contradiction to the “hands-on” personal approach that clinical staff are used to, </li></ul></ul><ul><ul><li>Impersonal because it may be used to target members based on numbers. </li></ul></ul>
  14. 15. Implementation Issue 4: Lack of Confidence in Predictions <ul><li>If the model accurately predicts high-risk members 30% of the time, what about the other 70%? </li></ul><ul><li>Why use it if it isn’t 100% accurate? </li></ul><ul><li>Age and sex predict only about 2% of cases </li></ul>
  15. 16. Implementation Issue 5: Impatience with the Predictive Modeling Process <ul><li>Staff may expect perfect identification of high-risk members, i.e.., members who are actionable, predictable, and teachable. </li></ul>
  16. 17. Implementation Issue 6: Questions About How Care Management ROI will be Measured <ul><li>Value of Care Management is a significant issue </li></ul><ul><li>Care Management staff may question how ROI from predictive model referrals to case management is calculated </li></ul><ul><li>Impatience with adhering to a referral process that may not yield immediate savings </li></ul><ul><li>Concerns regarding member retention: Why waste resources on members who may move on? </li></ul>
  17. 18. Determining the Optimal Predictive Model Algorithm <ul><li>Consider the following issues: </li></ul><ul><ul><li>Clinical resources – How many Case Management and Disease Management referrals can you handle? </li></ul></ul><ul><ul><li>Business priorities </li></ul></ul><ul><ul><li>What criteria will your nursing staff use to review clinical profiles? </li></ul></ul><ul><ul><li>Employer groups – increasing demand for information from large self-funded accounts </li></ul></ul><ul><ul><li>Political issues </li></ul></ul><ul><li>Referral exclusions for: </li></ul><ul><ul><li>Company, product, or LOB? </li></ul></ul><ul><ul><li>Medicare primary beneficiaries? </li></ul></ul><ul><ul><li>Age group? </li></ul></ul><ul><ul><li>Disease category, e.g., ESRD, HIV, NICU, or Cancer? </li></ul></ul><ul><li>Carve outs - members managed by vendors and PBMs </li></ul>
  18. 19. Determining the Optimal Predictive Model Algorithm, Cont’d <ul><li>Identifying “Actionable” and “Predictable” members </li></ul><ul><li>Five approaches to identifying high-risk members: </li></ul><ul><ul><li>Disease-specific approach </li></ul></ul><ul><ul><li>Relative risk score approach </li></ul></ul><ul><ul><li>Probability of admission (POA) approach </li></ul></ul><ul><ul><li>Low-to-Moderate-to-high cost transition approach </li></ul></ul><ul><ul><li>Combination of any of the above </li></ul></ul><ul><li>There is no “best” approach: </li></ul><ul><ul><li>Begin with the end in mind: How will you measure ROI? </li></ul></ul><ul><ul><li>Ask the question: “Is this member actionable?” </li></ul></ul><ul><ul><li>Focus on a developing a consistent, measurable process </li></ul></ul><ul><ul><li>Use your clinical judgment and training </li></ul></ul><ul><ul><li>Pick an approach that makes sense to you from a business and clinical perspective </li></ul></ul><ul><ul><li>Approach must be defensible to employer groups </li></ul></ul>
  19. 20. Approaches and Examples of Referral Algorithms <ul><li>Disease-specific approach </li></ul><ul><li>Relative risk score approach </li></ul><ul><li>Probability of admission (POA) approach </li></ul><ul><li>Low-to-Moderate-to-high cost transition approach </li></ul><ul><li>The current BCBSLA predictive model referral algorithm </li></ul>
  20. 21. Example of Employer Group Disease-Specific Approach
  21. 26. Example of the Low-to-High and Moderate-to-High Cost Transition Approach This slide is an example of the list of high-risk members referred to case management on a monthly basis .
  22. 27. The BCBSLA Predictive Model Referral Schema, June 2003
  23. 28. Predictive Model Referral Schema, Cont’d
  24. 29. Using CRMS Fundamentals to Improve Reliability and Validity <ul><li>BCBSLA clinical staff use Fundamentals to: </li></ul><ul><ul><li>Review claims-level detail of members </li></ul></ul><ul><ul><li>Determine if members referred via predictive model were appropriately referred </li></ul></ul><ul><ul><li>Develop a log of members who were: </li></ul></ul><ul><ul><ul><li>Identified as actionable high-risk but may not be </li></ul></ul></ul><ul><ul><ul><li>Not identified as actionable high-risk but should have been </li></ul></ul></ul>
  25. 30. Using CRMS to Enhance the Referral Process <ul><li>The current BCBSLA referral methodology identifies specific ETGs for referral to Care Management </li></ul><ul><li>You may need more granularity than is available via ETGs </li></ul><ul><li>CRMS claims-level data may be used to identify members with specific ICD-9 and CPT-4 codes </li></ul><ul><ul><li>Example: Member on Celexa not being treated for depression. ETG says the member is “depressed” while analysis of ICD-9 may say PVD. </li></ul></ul>
  26. 31. Using CRMS to Enhance the Referral Process, Cont’d <ul><li>We use CRMS claims-level data to identify members with the following diseases for a Case Management vendor: </li></ul><ul><ul><li>Depression </li></ul></ul><ul><ul><li>CHF </li></ul></ul><ul><ul><li>Hepatitis-C </li></ul></ul><ul><ul><li>High-risk pregnancy </li></ul></ul><ul><ul><li>Diabetes </li></ul></ul><ul><li>Once high-risk members are identified: </li></ul><ul><ul><li>CRMS member-personal data are used to provide contact information on high-risk members </li></ul></ul><ul><ul><li>CRMS provider data may be used to contact the member’s primary care provider </li></ul></ul>
  27. 32. Question and Answer Session

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