Modeling Medical Professional Liability Damage Caps An Illinois Case Study
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1 Modeling Medical Professional Liability Damage Caps An Illinois Case Study Prepared for: Casualty Actuarial Society Ratemaking and Product Management Seminar Chicago, IL Prepared by: Susan J. Forray, FCAS, MAAA Consulting Actuary March 17, 2010
2 Overview of Presentation Background Scope of Analysis Overview of Model Summary of Results Other Considerations 2
3 3 Topic #1: Background
4 Background Illinois Medical Professional Liability Statutes Tort reform enacted in 2005 (Public Act , aka Reform Act) Five reform provisions: Limit on non-economic damages Hospitals - $1,000,000 limit Physicians - $500,000 limit Periodic payment provisions Revised standards for expert witnesses Public identification of physician signing affidavit of merit Encouragement for health care professionals to acknowledge medical errors 4
5 Background Recent Developments Cap on non-economic damages was ruled unconstitutional by a Circuit Court Judge for Cook County, Illinois in late 2007 in the case of Abigaile Lebron, etc. vs. Gottlieb Memorial Hospital, et.al. Illinois Supreme Court ruled February 4, 2010, upholding the Circuit Court s decision 5
6 6 Topic #2: Scope of Analysis
7 Scope of Analysis Scope of analysis was to evaluate the impact on physicians MPL claim costs of the overturning of the cap on non-economic damages Magnitude of impact on rates less clear Reform appears to have been only partially reflected in rates to date Could have seen rate decreases if Reform Act were upheld Impact on frequency also unclear Could be significant based on experience of other states 7
8 8 Topic #3: Overview of Model
9 Overview of Model General Approach Understand components of Illinois PPL claim costs Loss ALAE CWI vs CWE claims Develop distributions around each of these components Including allocation of loss to economic and non-economic damages Simulate loss and ALAE costs under two scenarios With cap on damages Without cap on damages 9
10 Overview of Model Illinois Industry Data ISMIE Rate Filing Loss severity (per CWI Claim) ALAE severity (per CWI Claim and per CWE Claim) Portion of claims CWI / CWE / CNP 10
11 Overview of Model External Industry Data States of Florida and Texas closed claim databases Shape of distributions for claim costs by category Economic Non-Economic Correlation of economic/non-economic loss State of Texas closed claim database only Allocation of damages between economic/non-economic Portion of claims with loss that is Economic Non-Economic Both Correlation between overall ALAE and loss 11
12 Overview of Model Simulated Outcome For each scenario we estimated the impact on the following components for Illinois physicians Loss Severity Economic Non-Economic ALAE Severity 12
13 13 Topic #4:
14 Number of Claims per Occurrence Using industry data, we assumed the following: Expected number of claims per occurrence of 1.30 Distributional form is Zero-Truncated Poisson These assumptions imply the following probabilities for the number of claims per occurrence: Probability of 1 claim / occurrence = 74.1% Probability of 2 claims / occurrence = 22.2% Probability of 3 claims / occurrence = 3.3% Probability of 4+ claims / occurrence = 0.3% Weighted average claims / occurrence =
15 Claim Disposition Based on ISMIE Mutual Insurance Company s July 1, 2006 PPL rate filing, we assumed the following claim disposition ratios: CWI to total closed: 17% CWE to total closed: 78% CNP to total closed: 5% For CWI claims we then decomposed by category of loss based on the Texas closed claim database 15
16 Probability of CWI Claims by Category of Loss Selected Portion of Closed Claims Loss Type by Loss Type Economic Only 1.5% Non-Economic Only 20.5% Both Types 78.0% Total Claims 100.0% Source: Texas Closed Claim Database 16
17 Claim Severity Distribution by Category of Loss Fit a distribution to data for each category of loss Lognormal Exponential Weibull Gamma Pareto Logistic etc. Measured correlation between claim severities for each category of loss 17
18 Severity of Claims - Economic 100% Cumulative Distribution (based on Florida database) 90% 80% 70% 60% 50% 40% 30% Empirical Frequency Fitted Frequency 20% 10% 0% $0 $200K $400K $600K $800K $1.0M $1.2M $1.4M $1.6M $1.8M $2.0M 18
19 Severity of Claims - Economic 25% Incremental Distribution (based on Florida database) 20% Empirical Frequency Fitted Frequency 15% 10% 5% 0% $0 $200K $400K $600K $800K $1.0M $1.2M $1.4M $1.6M $1.8M $2.0M 19
20 Severity of Claims - Economic Comparison of Empirical and Fitted Distribution (based on Florida database) Empirical Cumulative Distribution Of Exponential Lognormal Distribution of Loss Under a Coefficient of Variation of Threshold Non-Zero Claims Distribution , % 0.13% 0.57% 0.61% 0.65% 0.69% 0.72% 0.76% 0.80% 2, % 0.27% 1.48% 1.56% 1.63% 1.70% 1.77% 1.84% 1.91% 3, % 0.40% 2.46% 2.56% 2.66% 2.75% 2.85% 2.94% 3.04% 4, % 0.54% 3.43% 3.56% 3.67% 3.79% 3.90% 4.01% 4.13% 5, % 0.67% 4.39% 4.53% 4.67% 4.80% 4.93% 5.05% 5.19% 7, % 1.01% 6.68% 6.85% 7.01% 7.17% 7.32% 7.47% 7.64% 10, % 1.34% 8.80% 8.99% 9.17% 9.34% 9.51% 9.67% 9.85% 12, % 1.67% 10.76% 10.96% 11.15% 11.33% 11.50% 11.67% 11.87% 15, % 2.01% 12.58% 12.79% 12.98% 13.17% 13.34% 13.51% 13.72% 20, % 2.67% 15.88% 16.08% 16.28% 16.46% 16.64% 16.81% 17.03% 25, % 3.32% 18.79% 18.99% 19.18% 19.36% 19.53% 19.70% 19.91% 35, % 4.62% 23.76% 23.94% 24.11% 24.28% 24.43% 24.58% 24.79% 45, % 5.90% 27.90% 28.06% 28.21% 28.35% 28.48% 28.61% 28.80% 55, % 7.16% 31.44% 31.57% 31.70% 31.82% 31.93% 32.04% 32.22% 65, % 8.41% 34.53% 34.64% 34.74% 34.84% 34.93% 35.02% 35.18% 75, % 9.63% 37.26% 37.34% 37.42% 37.50% 37.58% 37.65% 37.79% 100, % 12.64% 42.93% 42.96% 43.00% 43.03% 43.06% 43.10% 43.21% 125, % 15.54% 47.43% 47.43% 47.42% 47.42% 47.42% 47.42% 47.50% 150, % 18.34% 51.14% 51.10% 51.07% 51.04% 51.01% 50.98% 51.03% 175, % 21.05% 54.27% 54.20% 54.14% 54.08% 54.03% 53.98% 54.02% 200, % 23.67% 56.96% 56.87% 56.79% 56.71% 56.64% 56.57% 56.58% 250, % 28.66% 61.38% 61.25% 61.13% 61.02% 60.92% 60.82% 60.80% 350, % 37.67% 67.76% 67.58% 67.42% 67.26% 67.11% 66.98% 66.92% 450, % 45.55% 72.21% 72.01% 71.82% 71.63% 71.46% 71.30% 71.22% 550, % 52.43% 75.54% 75.32% 75.11% 74.91% 74.73% 74.55% 74.45% 650, % 58.44% 78.14% 77.91% 77.69% 77.48% 77.29% 77.10% 76.99% 750, % 63.69% 80.23% 79.99% 79.77% 79.56% 79.36% 79.17% 79.05% 1,000, % 74.10% 84.05% 83.81% 83.59% 83.37% 83.17% 82.97% 82.84% Chi-Squared Statistic 1,
21 Severity of Claims - Economic Cumulative Distribution (based on Texas database) 100% 90% 80% 70% 60% 50% 40% 30% Empirical Frequency Fitted Frequency 20% 10% 0% $0 $200K $400K $600K $800K $1.0M $1.2M $1.4M $1.6M $1.8M $2.0M 21
22 Severity of Claims - Economic Incremental Distribution (based on Texas database) 40% 35% 30% Empirical Frequency Fitted Frequency 25% 20% 15% 10% 5% 0% $0 $200K $400K $600K $800K $1.0M $1.2M $1.4M $1.6M $1.8M $2.0M 22
23 Severity of Claims - Economic Comparison of Empirical and Fitted Distribution (based on Texas database) Empirical Cumulative Distribution Of Exponential Lognormal Distribution of Loss Under a Coefficient of Variation of Threshold Non-Zero Claims Distribution , % 0.44% 0.40% 0.51% 0.63% 0.75% 0.87% 0.99% 1.12% 2, % 0.88% 1.41% 1.68% 1.95% 2.21% 2.47% 2.71% 2.95% 3, % 1.32% 2.71% 3.11% 3.50% 3.87% 4.23% 4.56% 4.88% 4, % 1.75% 4.13% 4.64% 5.13% 5.58% 6.01% 6.41% 6.78% 5, % 2.19% 5.60% 6.20% 6.76% 7.28% 7.76% 8.21% 8.62% 7, % 3.26% 9.31% 10.05% 10.72% 11.33% 11.89% 12.40% 12.87% 10, % 4.33% 12.87% 13.67% 14.39% 15.04% 15.63% 16.17% 16.65% 12, % 5.38% 16.22% 17.04% 17.78% 18.43% 19.02% 19.55% 20.04% 15, % 6.42% 19.34% 20.16% 20.88% 21.52% 22.10% 22.62% 23.08% 20, % 8.47% 24.96% 25.72% 26.38% 26.97% 27.49% 27.96% 28.38% 25, % 10.47% 29.85% 30.52% 31.11% 31.63% 32.08% 32.49% 32.86% 35, % 14.34% 37.95% 38.42% 38.84% 39.21% 39.53% 39.82% 40.07% 45, % 18.05% 44.38% 44.68% 44.94% 45.17% 45.38% 45.56% 45.71% 55, % 21.60% 49.64% 49.78% 49.91% 50.02% 50.12% 50.21% 50.28% 65, % 24.99% 54.02% 54.04% 54.05% 54.06% 54.07% 54.08% 54.09% 75, % 28.24% 57.74% 57.64% 57.56% 57.49% 57.43% 57.37% 57.32% 100, % 35.75% 64.99% 64.68% 64.42% 64.19% 63.99% 63.81% 63.65% 125, % 42.48% 70.26% 69.82% 69.44% 69.11% 68.82% 68.56% 68.32% 150, % 48.50% 74.28% 73.76% 73.30% 72.90% 72.55% 72.23% 71.94% 175, % 53.89% 77.45% 76.86% 76.36% 75.91% 75.51% 75.16% 74.83% 200, % 58.72% 80.00% 79.38% 78.84% 78.36% 77.94% 77.55% 77.20% 250, % 66.91% 83.86% 83.21% 82.63% 82.12% 81.66% 81.25% 80.87% 350, % 78.74% 88.70% 88.05% 87.47% 86.96% 86.49% 86.06% 85.67% 450, % 86.34% 91.56% 90.96% 90.41% 89.92% 89.47% 89.06% 88.68% 550, % 91.22% 93.43% 92.87% 92.37% 91.91% 91.48% 91.09% 90.73% 650, % 94.36% 94.72% 94.21% 93.75% 93.32% 92.93% 92.56% 92.22% 750, % 96.38% 95.66% 95.20% 94.77% 94.38% 94.01% 93.66% 93.34% 1,000, % 98.80% 97.14% 96.77% 96.42% 96.09% 95.78% 95.49% 95.22% Chi-Squared Statistic
24 Severity of Claims Non-Economic Cumulative Distribution (based on Texas database) 100% 90% 80% 70% 60% 50% 40% 30% Empirical Frequency Fitted Frequency 20% 10% 0% $0 $200K $400K $600K $800K $1.0M $1.2M $1.4M $1.6M $1.8M $2.0M 24
25 Severity of Claims Non-Economic Incremental Distribution (based on Texas database) 18% 16% 14% Empirical Frequency Fitted Frequency 12% 10% 8% 6% 4% 2% 0% $0 $200K $400K $600K $800K $1.0M $1.2M $1.4M $1.6M $1.8M $2.0M 25
26 Severity of Claims Non-Economic Comparison of Empirical and Fitted Distribution (based on Texas database) Empirical Cumulative Distribution Of Exponential Lognormal Distribution of Loss Under a Coefficient of Variation of Threshold Non-Zero Claims Distribution , % 2.46% 0.67% 1.15% 1.68% 2.22% 2.76% 3.27% 3.75% 10, % 3.26% 1.36% 2.11% 2.89% 3.63% 4.33% 4.98% 5.59% 12, % 4.06% 2.25% 3.27% 4.25% 5.17% 6.00% 6.76% 7.44% 15, % 4.86% 3.32% 4.57% 5.73% 6.77% 7.70% 8.54% 9.29% 20, % 6.42% 5.81% 7.42% 8.83% 10.05% 11.11% 12.03% 12.84% 25, % 7.96% 8.61% 10.44% 11.99% 13.29% 14.41% 15.36% 16.19% 35, % 10.97% 14.56% 16.54% 18.13% 19.44% 20.52% 21.43% 22.21% 45, % 13.87% 20.50% 22.35% 23.82% 24.99% 25.96% 26.76% 27.45% 55, % 16.69% 26.14% 27.73% 28.98% 29.97% 30.78% 31.45% 32.02% 65, % 19.41% 31.38% 32.64% 33.64% 34.43% 35.07% 35.60% 36.05% 75, % 22.04% 36.19% 37.11% 37.84% 38.43% 38.91% 39.30% 39.64% 100, % 28.25% 46.48% 46.58% 46.70% 46.81% 46.92% 47.01% 47.08% 125, % 33.96% 54.66% 54.08% 53.71% 53.44% 53.24% 53.08% 52.95% 150, % 39.22% 61.22% 60.12% 59.36% 58.79% 58.35% 57.99% 57.69% 175, % 44.06% 66.53% 65.05% 63.99% 63.19% 62.56% 62.05% 61.62% 200, % 48.51% 70.89% 69.13% 67.85% 66.87% 66.10% 65.47% 64.94% 250, % 56.39% 77.50% 75.43% 73.87% 72.66% 71.69% 70.89% 70.22% 350, % 68.71% 85.67% 83.45% 81.72% 80.32% 79.18% 78.22% 77.41% 450, % 77.55% 90.27% 88.19% 86.49% 85.09% 83.92% 82.93% 82.07% 550, % 83.89% 93.08% 91.21% 89.63% 88.30% 87.16% 86.18% 85.32% 650, % 88.44% 94.90% 93.25% 91.81% 90.56% 89.48% 88.54% 87.71% 750, % 91.70% 96.14% 94.69% 93.39% 92.23% 91.22% 90.32% 89.52% 1,000, % 96.38% 97.89% 96.85% 95.84% 94.90% 94.05% 93.27% 92.57% Chi-Squared Statistic
27 Severity of Claims Loss Correlation Relationship Between Economic Loss and Non-Economic Loss (based on Florida database) $6,000 $5,000 R-Squared = 0.07 Correlation Coefficient = 0.26 $4,000 Non-Economic Loss ($000) $3,000 $2,000 $1,000 $0 $0 $5,000 $10,000 $15,000 $20,000 $25,000 Economic Loss ($000) Note: Data includes only claims with non-zero values for both economic loss and non-economic loss 27
28 Severity of Claims Ln(Loss) Correlation Relationship Between Economic Loss and Non-Economic Loss (based on Florida database) Ln of Non-Economic Loss R-Squared = 0.21 Correlation Coefficient = Ln of Economic Loss Note: Data includes only claims with non-zero values for both economic loss and non-economic loss 28
29 Severity of Claims Loss Correlation Relationship Between Economic Loss and Non-Economic Loss (based on Texas database) $8,000 $7,000 $6,000 R-Squared = 0.25 Correlation Coefficient = 0.50 Non-Economic Loss ($000) $5,000 $4,000 $3,000 $2,000 $1,000 $0 $0 $2,000 $4,000 $6,000 $8,000 $10,000 Economic Loss ($000) Note: Data includes only claims with non-zero values for both economic loss and non-economic loss 29
30 Severity of Claims Ln(Loss) Correlation Relationship Between Economic Loss and Non-Economic Loss (based on Texas database) Ln of Non-Economic Loss R-Squared = 0.35 Correlation Coefficient = Ln of Economic Loss Note: Data includes only claims with non-zero values for both economic loss and non-economic loss 30
31 Severity of Claims Loss Correlation Relationship Between Economic Loss and Non-Economic Loss Selected Indicated Correlation Coefficient Relationship / Spearman's Correlation Database Assumption R Squared Pearson's R Rank Order Coefficient Linear Relationship Florida Log-Linear Relationship Log-Linear Texas Linear Relationship Log-Linear Relationship Note: Relationship derived from non-zero values of economic and non-economic losses 31
32 Severity of Claims ALAE on CWI In modeling ALAE severities we differentiated between CWI and CWE claims Based on ISMIE s rate filing and a 4% per annum ALAE trend, we assumed the following: ALAE per CWI claim = $90,890 ALAE per CWE claim = $50,656 ALAE per CWE claim remains fixed throughout the model ALAE severity per CWI claim varies with the loss severity in a log-linear fashion with a slope of
33 Severity of Claims ALAE on CWI Correlation Relationship Between Loss and ALAE on CWI $2,000 $1,600 (based on Texas database) R-Squared = 0.18 Correlation Coefficient = 0.43 ALAE on CWI ($000) $1,200 $800 $400 $0 $0 $2,000 $4,000 $6,000 $8,000 $10,000 Loss ($000) Note: Data includes only claims with non-zero values for both loss and ALAE on CWI 33
34 Severity of Claims Ln(ALAE on CWI) Correlation 16.0 Relationship Between Loss and ALAE on CWI (based on Texas database) Ln of ALAE on CWI R-Squared = 0.25 Correlation Coefficient = Ln of Loss Note: Data includes only claims with non-zero values for both loss and ALAE on CWI 34
35 35 Topic #5: Summary of Results
36 Summary of Results Indicated Increase in Severity Due to Reform Repeal (Assuming $1,000,000 Policy Limits) Cap on Damages Indicated Estimated Per Occurrence With Without Increase Mean Indemnity 117, ,000 23% Mean ALAE 67,200 73,600 10% Mean Indemnity & ALAE 184, ,600 18% Note: Measured increases are per reported claim. 36
37 Observations Large estimated impact due to Illinois MPL severities Among highest countrywide Impact on calendar year payments less clear Mix of accident dates within calendar year Delay in settlements Delay in claim filings Impact on rates will likely be smaller Few insurers had reduced rates for tort reform May see some rate increases among insurers who had taken rate decreases Had Supreme Court stayed the reforms, we might have seen rate decreases 37
38 38 Topic #6: Other Considerations
39 Other Considerations Accompanying Oral Discussion This document is not complete without the accompanying oral discussion and explanation of the underlying information and concepts as well as any interpretational limitations. Limited Distribution This document should not be distributed, disclosed or otherwise furnished, in whole or in part, without the express written consent of Milliman. Data Reliance We have relied upon data and other background information from the Florida Office of Insurance Regulation and the Texas Department of Insurance, as well as rate filings made by ISMIE, without audit or independent verification. We have performed a limited review of the data for reasonableness and consistency and have not found material defects in the data. If there are material defects in the data, it is possible that they would be uncovered by a detailed, systematic review and comparison of the data to search for data values that are questionable or relationships that are materially inconsistent. Such a review was beyond the scope of our assignment. 39
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