Session 6 Emerging Issues In Medical Malpractice PREDICTIVE MODELING

1. Session 6 Emerging Issues In Medical Malpractice PREDICTIVE MODELING Kevin M. Bingham – Deloitte. kbingham@deloitte.com Casualty Actuarial Society Annual Meeting Tuesday, November 16, 2004 12:30 PM – 2:00 PM Montreal, Canada

2. INTRODUCTION • Florida Medical Malpractice Report • www.fldfs.com/companies/pdf/ Med_Mal_2004_Rpt.pdf • Exciting Trends in Patient Safety • The Actuary’s Opportunity • Goal of Predictive Modeling • Predictive Modeling Basics • Closing Thoughts

3. Exciting Trends in Patient Safety

4. Patient Safety Organizations and Other Sources • National Patient Safety Foundation (www.npsf.org) • JCAHO Environment of Care (www.jcaho.org) • Environment of Care • National Patient Safety Goals (2005 goals now available) • Root Cause Analysis • Medical associations (e.g., American Medical Association - www.ama-assn.org) • The Leapfrog Group (www.leapfroggroup.org) • State patient safety organizations (e.g., Virginia - www.vipcs.org) • Advancements in computerized physician order entry (CPOE) systems • Safety books (e.g., “The Satisfied Patient” – James W. Saxton) • Legislative action

5. Safety and Errors • Patient Safety – The prevention of healthcare errors, and the elimination or mitigation of patient injury caused by healthcare errors. • Healthcare Error – An unintended outcome caused by a defect in the delivery of care to a patient. Healthcare errors may be errors of: • Commission (doing the wrong thing); • Omission (not doing the right thing); or • Execution (doing the right thing incorrectly). Definitions from the National Patient Safety Foundation (www.npsf.org)

6. The Actuary’s Opportunity

7. Medical Malpractice and the Actuary – Current Role • Traditional Roles • Pricing • Reserving • Tort Reform • The Actuarial Profession’s Challenge • Overcoming the negative perception in the media: “Actuaries focus on quantifying the price to charge a physician, or the amount of damages that must ultimately be paid to a victim, instead of focusing our energy on preventing injuries in the first place.”

8. Medical Malpractice and the Actuary – Future Role? • Shift our Focus Towards the Positive Side of the Medical Malpractice Equation • Increase our involvement in patient safety initiatives • Increase our eminence on the positive side of the healthcare equation • Join CPOE efforts • Join PSOs • Submit articles with a heavier focus on patient safety • Use Predictive Modeling in the U/W process in order to price policies in a manner that promotes patient safety and risk management goals Definitions from www.npsf.org

9. Goal of Predictive Modeling

10. Current Perception of Specialty Segmentation Obstetricians Below average 112% Average Above average 93% 140% 85% 135% Chiropractors 125% 115% 78% Overall L.R. 75% 110% 75% 100% 72% 90% 68% 80% 65% 70% 63% 60% Loss Ratio Internal data

11. Current Perception of Specialty Segmentation “All chiropractors are good risks” Low frequency Low severity Low profile (i.e., not making headlines) 80% 65% 70% 63% 60% Loss Ratio - Chiropractors

12. Current Perception of Segmentation 112% • “All OB/GYNs are bad risks” • High frequency • High severity • High profile • Dramatic rate increases • Leaving state • Retiring from practice • Cutting back on services 93% 140% 85% 135% 125% Loss Ratio - Obstetricians

13. Segmentation of the Future 135 Internal / External Data Predicted Loss Ratio Dr. Bob Lesse - Chiropractor 90 Dr. Linda Moore - Chiropractor 82 Overall L.R. 75% 78 PredictedLoss Ratio 74 70 66 62 58 40

14. Segmentation of the Future -The Goal of Predictive Modeling Dr. Bob Lesse 90 “Some chiropractors are good risks, some are bad. Focus U/W dollars on good risks.” “All chiropractors are good risks” 80% 65% 70% Dr. Linda Moore 63% 58 60% Chiropractors

15. Predictive Modeling Basics

16. Predictive Modeling Approach

17. Potential Medical Malpractice Sources

18. Data Evaluation Data Collection/Cleansing Loss Development Manual Premium Development On Leveling Variable Creation Univariate Analysis Data Quality Analysis, Capping, Binning Correlation Analysis Training vs. Testing Data External Data Matching and Related Reports Modeling Approach Loss Ratio Transformations Principal Component Analyses Stepwise Regression, Forward, Backward Generalized Linear Modeling Neural Network Applications CART, MARS Algorithms Comprehensive Actuarial Review & Analysis Lift Disruption Longitudinal Drift Stability Analysis Reason Code Distribution Distribution Analysis (premium, class group, geographic, cross line) Data Preprocessing & Modeling

19. Sample Representation of Model

20. Predictive Modeling Results inImproved Class Segmentation Better Average Poor • There is profitable business in “under performing” classes • There is unprofitable business in “over performing” classes • The models help to identify both situations

21. Reason Codes • Reason Codes identify several traditional / acceptable reasons that will be used for external communications. • Reason codes explain 80% to 90% of the resulting policy actions • Reason codes hopefully drive change in attitude towards risk management and patient safety

22. Closing Thoughts • Physician owned organizations might not be receptive to Predictive Modeling • Comfort level regarding personal data (e.g., credit scoring) • More focused pricing will certainly increase rates significantly for some physicians (lowering rates for others) • Patient safety – “It’s time for actuaries to begin focusing more of our efforts on preventing injuries in the first place.”

23. Closing Thoughts • Kaiser Family Foundation Media Advisory for November 17, 2004 “NEW SURVEY ASSESSES PUBLIC'S VIEWS ON HEALTH CARE QUALITY FIVE YEARS AFTER LANDMARK REPORT ON MEDICAL ERRORS”