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CARe Seminar 2005 Medical Malpractice: Observations, Issues and Methodologies June 7, 2005 Hamilton, Bermuda

CARe Seminar 2005 Medical Malpractice: Observations, Issues and Methodologies June 7, 2005 Hamilton, Bermuda. Panelists :James D. Hurley, ACAS, MAAA Towers Perrin Kirk Bitu, FCAS, MAAA Willis Re Moderator : Anne Petrides, FCAS, MAAA Meetinghouse LLC. Session Format.

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CARe Seminar 2005 Medical Malpractice: Observations, Issues and Methodologies June 7, 2005 Hamilton, Bermuda

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  1. CARe Seminar 2005Medical Malpractice:Observations, Issues and MethodologiesJune 7, 2005Hamilton, Bermuda Panelists:James D. Hurley, ACAS, MAAA Towers Perrin Kirk Bitu, FCAS, MAAA Willis Re Moderator: Anne Petrides, FCAS, MAAA Meetinghouse LLC

  2. Session Format • Introductions • Overview and Observations of the current Medical Malpractice Environment including current issues on Tort Reform • Reinsurance Modeling techniques with specific nuances to Medical Malpractice • Discussion with Question and Answer Opportunity

  3. Casualty Actuaries in Reinsurance Observations on Medical Malpractice This document is incomplete without the accompanying discussion; it is confidential and intended solely for the information and benefit of the immediate recipient hereof.

  4. Observations on Medical Malpractice • Financial results • Tort reform • Insurance reform

  5. Observations on Medical Malpractice Source: Compilation of Best Data

  6. Observations on Medical Malpractice Source: Compilation of Best Data

  7. Observations on Medical Malpractice Source: Compilation of Best Data

  8. Observations on Medical Malpractice Source: Compilation of Best Data

  9. Observations on Medical Malpractice Source: Compilation of Best Data

  10. Observations on Medical Malpractice Source: Compilation of Best Data

  11. Observations on Medical Malpractice Source: Compilation of Best Data

  12. Observations on Medical Malpractice Source: Compilation of Best Data

  13. Observations on Medical Malpractice Source: Compilation of Best Data

  14. Observations on Medical Malpractice Source: Compilation of Best Data

  15. Observations on Medical Malpractice Source: Compilation of Best Data

  16. Observations on Medical Malpractice Source: Compilation of Best Data

  17. Observations on Medical Malpractice • Financial results impacted by... • 1990’s • modest loss trends • favorable reserve development • relatively high investment returns • expansion • slippage in pricing • 2000’s • loss trends pick up • unfavorable reserve development • investment returns turn • rates adjusted

  18. Observations on Medical Malpractice Source: A.M, Best’s Aggregates and Averages

  19. Observations on Medical Malpractice Frequently discussed tort reforms • Caps on non-economic loss • Collateral source offsets • Limitations on joint-and-several liability • Punitive damage restrictions • Periodic payments • Frivolous suit penalties • Limitations on attorneys’ fees • Immunity statutes Continued...

  20. Observations on Medical Malpractice Frequently discussed tort reforms (cont’d) • Changes in pre-judgment interest • Establishment of pre-trial hearing panels • Establishment of state-operated funds to handle certain claims • Changes to the statute of limitation or statue of repose • Mandatory mediation

  21. Observations on Medical Malpractice • Tort reform • Background – Federal • HR5/HR4280 • MICRA-like • Background – State • many have discussed • several have passed • likely impacts • e.g., Texas, Pennsylvania, Florida Continued...

  22. Observations on Medical Malpractice • Tort reform (cont’d) • Issues/risks • limited data to evaluate • prospective credit? • interpreted as expected • upheld Continued...

  23. Observations on Medical Malpractice • Tort reform • Issues/risks (cont’d) • specifics • non-economic limit: per defendant or per occurrence • collateral source: jury disclosure or after award • panels: admissible or not • PCF: who defends? • no-fault

  24. Observations on Medical Malpractice • Insurance reform • For example, Rhode Island proposal • use ‘lowest factor’ of any state • ‘rate formula’ • rate of return formula • number of years for development, trend, ILF’s • specific weights to each experience year • class plan range limited to 500% • use of RBC • surplus exceeds RBC • ‘unreasonably large’ • no rate increase/distribute ‘fairly allocable surplus’

  25. Observations on Medical Malpractice • Are we having fun yet?

  26. W Modeling: ApproachCARE Presentation

  27. Modeling Technique Typical Modeling exercise for Medical Malpractice Find appropriate option for Med Mal book: • 300,000 Per Occurrence Retention • 500,000 Per Occurrence Retention • 1,000,000 Per Occurrence Retention • ALAE treated pro-rata Reasonable step process: • Actuarial analyses: estimate ceded loss by retention • Fit loss curve(s) • Build model / Simulate loss • Report results Specific clash contracts and examples to follow

  28. Modeling Technique Step 1 – Actuarial Analysis: Expected Loss By Layer • Selected loss for each layer • Experience Analysis • - trend analysis, loss development analysis • - Premium / exposures to current level • Exposure Analysis • Actuarial Judgment

  29. Modeling Technique Step 2 – Curve Fitting: Preliminaries Common methods of fitting: • Maximum likelihood estimators • Method of moments • Percentile matching • Sum squared error Inputs needed: • Data points (ultimate loss of the individual claims) Medical Malpractice considerations: • Long-tailed - not all claims in data set are closed • Tort reform / changing social factors create non-homogeneous losses • Clash Losses • Pro-rata ALAE

  30. Modeling Technique Step 2 – Curve Fitting: Addressing Med Mal Considerations Long-tail business: Years with claims not closed? • Common Feedback: • Do not use the ‘green’ years’ fit your curve Tort Reform / Changing Social Factors:Older years have less predictive value • Common Feedback: • Exclude older years in curve fitting / modeling If we use this feedback, we are left with little data to fit to!

  31. Modeling Technique Step 2 – Curve Fitting: Addressing Med Mal Considerations Clash Losses:Modeling clash events • Common Feedback: • Fit a distribution for number of claims per event • Fit a distribution for individual claim severity correlated to claims in event • Find distribution for claimibased on other claims in the event Pro Rata ALAE:Addressing ALAE? • Common Feedback: • Use regression analysis, correlation or fit a copula While the feedback is sound in theory, is it practical or even necessary?

  32. Modeling Technique Step 2 – Curve Fitting: Addressing Med Mal Considerations Statistical complications: • Adjustments for non-homogenous losses, open claims, etc. • Variance-covariance matrix for the clash, in several dimensions • ALAE: copula, regression analysis, correlation • Difficult / impractical to implement • Parameter and model error unavoidable But these issues are implicitly addressed in the actuarial analysis!

  33. Modeling Technique Step 2 – Curve Fitting: Alternative Approach – Using Actuarial Analyses Fit directly to the output of our actuarial analyses Selected loss reflects terms of contract: • In this example: Selected Loss and Fitted Loss include ALAE pro rata *In any curve fitting exercise, the analyst will also make necessary statistical considerations. Examples: Non-parametric versus parametric estimators, # of parameter to use, spliced vs. mixed curves vs. incorporating industry curves between nodes, accounting for clusters of points around limits, etc.

  34. Modeling Technique Step 2 – Curve Fitting: Alternative Approach Fitting to data points requires analysis of raw data twice: Alternative approach works from actuarial work already performed: Efficient!

  35. Modeling Technique Step 3 – Simulation: Preparing Model Use parameters found in curve fitting to simulate losses Based on varying retentions, model impact on financial statements items • Summary of company underwriting assumptions:

  36. Modeling Technique Step 3 – Simulation: Options Summary of retention options to model

  37. Modeling Technique Step 4 – Report Model Results : Variability in Underwriting Result

  38. Modeling Technique Step 4 – Report Model Results: Variability in Underwriting Result Probability Results 99.6% 75% 50% 25% .4% Avg ♦

  39. Modeling Technique Clash: Coverage examples Example 1: Type: Per Occurrence retention Modeling: Per occurrence Example 2: Type: Per Occurrence, Per Risk Cover (same retention) Modeling: Per Occurrence Allocate simulated occurrence losses to per-risk and clash components Example 3: Type: Clash Retention greater than Per Risk Cover Modeling: Per-occurrence model for each per-risk retention Simulate using two curves: Excess Per Risk, Clash Requires correlation–two curves instead of many

  40. Modeling Technique Handling Clash – Allocation of Losses Use actuarial analysis to find per-risk loss in layer AND per-occurrence loss in layer Clash Loss = Per-occurrence minus per-risk OR Model clash separately

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