Medical Malpractice
After the Bubble ……
Medical Malpractice Crisis
Winding Down
• New Capacity for Primary Hospital business
– Rates flattening
– Terms and conditions eroding
• New Capacity for Excess Hospital business
– Rates Falling
• New Treaty Reinsurance Capacity
– Reinsurance terms weakening
How can Reinsurers still
maintain Profitability in this line
• New Capacity must do the appropriate due
diligence when entering new lines of business
• Medical Malpractice is a prime target for
adverse selection
– Practitioners typically shop for the cheapest
insurance regardless of company ratings.
– Primary insurers typically shop for the cheapest
reinsurance regardless of company ratings.
• Examples: New Physician Carrier has ended up with a
book of business made up predominantly of OB/GYNs
• Reinsurer has ended up as the carrier of choice for
Cook County Teaching hospitals
How can Reinsurers do the
appropriate Due Diligence?
• Difficulties:
– Lack of good publicly available data
• ISO does not have complete data because so many
companies that write MedMal do not belong to ISO
• Most company rates were based on copying St Paul
Rate Filings
– Huge Variety of Risks Involved:
• Physicians, Surgeons, Allied Professionals, Hospitals,
Managed Care, Healthcare D & O, Aviation
(helicopters), Auto (ambulances), GL Mold problems
– Highly Jurisdictional Line of Business.
• State differentials
• Differences within states
GAO Report on Factors
Contributing to Increased
MedMal Insurance Rates
• Conclusion of this Report:
– Encourage NAIC and State Regulators to
“identify and collect additional, mutually
beneficial data necessary for evaluating the
Medical Malpractice insurance market.”
National Practitioner Databank
Public Use File
• Free Download of data is available at:
– http://www.npdb-hipdb.com/publicdata.html
• Updated Quarterly
• Formatted as either ASCII file or SPSS file
– SPSS is a statistical package program similar to SAS
– Free demo download of SPSS available at;
• http://www.spss.com Just register for free and
download the software for one month
– Using SPSS, you can select the data you would like
and create an Excel spreadsheet
What data is Available
• Medical Malpractice payments made on
behalf of individual practitioner
– Physicians and Surgeons
– Dentists
– Nurses
– Various Allied health Professional
What data is Available
• Cause of Loss
– Obstetrics Related
– Anesthesia Related
– Failure to Diagnose
– Surgery
– Medication
– IV and Blood
– Treatment Related
Other Useful Data Fields
• Accident Year
• Year reported to Databank (payers are
required to report within 30 days of
payment)
• Fund Payments
• Age group of practitioner
Calendar Severity Trends
•Example – CT in Crisis
CT Phys/Surg
• Ability to compare Severity Trend
Calendar Year 670,000
Severity Trends by 570,000
470,000
– State 370,000
– Type of Practitioner
270,000
170,000
– Cause of Loss 70,000
0
1
2
3
4
5
6
7
8
9
0
1
2
3
99
99
99
99
99
99
99
99
99
99
00
00
00
00
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
2,
2,
2,
2,
Payment Year
Average Payment Exponential Trend
•Trend = 8.5%
•Avg Sev = $550,000
•Michigan – Showing •California –
Problem Signs Currently Okay
•Trend since 1996 = 5% •Trend = 4.7%
MP y u
I h s/S rg A h s/S rg
C Py u
C S erityT d
Y ev ren Y ev ren
C S erityT d
10 0
4 ,0 0 9 ,0 0
10 0
10 0
3 ,0 0 7 ,0 0
10 0
2 ,0 0
10 0 5 ,0 0
10 0
1 ,0 0
10 0
3 ,0 0
10 0
0 ,0 0
10 0
1 ,0 0
10 0
00
9 ,0 0
8 ,0 0
00 00
9 ,0 0
7 ,0 0
00 00
7 ,0 0
90
91
92
93
94
95
96
97
98
99
00
01
02
03
90
91
92
93
94
95
96
97
98
99
00
01
02
03
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
2,0
2,0
2,0
2,0
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
2,0
2,0
2,0
2,0
P y en Y r
a m t ea a m t ea
P y en Y r
Ae g P ym n
v ra e a e t E o e tia T n
xp n n l re d v ra e a e t
Ae g P ymn xp n n l re d
E o e tia T n
•Avg Sev = $130,000 •Avg Sev = $175,000
CO – Currently OK (?)
O hys/S
C P urg
C S
Y everityTrend
O Y a m ts er id t
C C P y en p res en
320,000 .0 0 0
0 60
270,000 .0 5 0
0 50
.0 0 0
0 50
220,000
.0 5 0
0 40
170,000
.0 0 0
0 40
120,000
.0 5 0
0 30
70,000
.0 0 0
0 30
90
91
92
93
94
95
96
97
98
99
00
01
02
03
.0 5 0
0 20
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
2,0
2,0
2,0
2,0
Payment Year
91
92
93
94
95
96
97
98
99
00
01
02
03
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
2,0
2,0
2,0
2,0
Average Payment Exponential Trend requ cy opu
F en perP lation Fitted
•Trend = 9.8% •33% jump in 2002
What Happened in CO in 2002?
• If you do a little research, you will find:
– In 2001, Preston v. Dupont held that damages for
physical impairment and disfigurement are not
subject to the $250,000 damages cap.
1991 3.25
1992 5.43 # of Payments greater
1993 4.43 than $250,000 by CY
1994 4.83
1995 8.36
1996 6.38
1997 6.47
1998 10.93
1999 6.63
2000 6.93
2001 7.45
2002 10.66
2003 10.71
Calendar Year Frequency
Trends (Indemnity only)
I Y aid d n er esid ts
M C P In em ityp 1MR en
• Big decrease in CY
0.11000 frequency in 1996
0.10000
– Due to tort reform which
0.09000
enacted a cap on non-
0.08000 economic damages in
0.07000 MI
0.06000
• Difficult to find data on
0.05000
historical doctor counts
91
92
93
94
95
96
97
98
99
00
01
02
03
so use Population
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
1,9
2,0
2,0
2,0
2,0
en opu
Frequ cyper P lation Fitted
CY Paid Indemnity Frequency
•CA CY Paid Count per 1M Residents
•0.06000
•0.05500
•0.05000
•0.04500
•0.04000
•0.03500
•0.03000
•Frequency per Population •Fitted
•Trend = -4.3% Unusual
Check AY Reporting Patterns
A Y 1 2 3 4 5 6 7 8
1 9 9 0 7 1 1 8 3 9 8 8 6 0 1 1 5 8 1 3 0 4 1 3 8 6 1 4 2 6
1 9 9 1 2 0 1 7 5 5 2 4 9 8 4 1 2 1 8 1 3 6 0 1 4 1 9 1 4 4 2
1 9 9 2 1 7 1 6 0 5 4 6 9 2 5 1 1 8 0 1 2 8 8 1 3 3 6 1 3 6 6
1 9 9 3 2 4 2 0 5 5 9 4 1 0 3 3 1 2 9 5 1 3 9 2 1 4 2 9 1 4 5 7
1 9 9 4 1 8 1 6 5 6 3 0 1 2 7 5 1 4 8 6 1 5 6 6 1 6 1 0 1 6 3 2
1 9 9 5 1 6 1 6 6 6 1 7 1 0 3 5 1 2 3 1 1 3 2 6 1 3 7 7 1 4 0 5
1 9 9 6 2 7 1 9 4 6 4 5 1 1 1 0 1 2 7 1 1 3 5 7 1 4 0 1 1 4 2 3
1 9 9 7 1 3 1 6 9 6 0 4 1 0 0 5 1 1 9 1 1 2 5 9 1 2 8 8
1 9 9 8 6 1 4 8 5 8 5 9 9 8 1 1 4 6 1 2 0 6
1 9 9 9 2 1 1 6 6 6 5 1 1 0 2 6 1 2 1 1
2 0 0 0 6 1 2 9 5 8 8 9 8 0
2 0 0 1 1 4 1 4 7 5 8 3
2 0 0 2 2 9 1 5 7
2 0 0 3 1 9
1 9 9 0 16 .8 5 7 3 .3 7 3 2 .1 6 1 1 .3 4 7 1 .1 2 6 1 .0 6 3 1 .0 2 9
1 9 9 1 8 .7 5 0 2 .9 9 4 1 .8 7 8 1 .2 3 8 1 .1 1 7 1 .0 4 3 1 .0 1 6
1 9 9 2 9 .4 1 2 3 .4 1 3 1 .6 9 4 1 .2 7 6 1 .0 9 2 1 .0 3 7 1 .0 2 2
1 9 9 3 8 .5 4 2 2 .8 9 8 1 .7 3 9 1 .2 5 4 1 .0 7 5 1 .0 2 7 1 .0 2 0
1 9 9 4 9 .1 6 7 3 .8 1 8 2 .0 2 4 1 .1 6 5 1 .0 5 4 1 .0 2 8 1 .0 1 4
1 9 9 5 10 .3 7 5 3 .7 1 7 1 .6 7 7 1 .1 8 9 1 .0 7 7 1 .0 3 8 1 .0 2 0
1 9 9 6 7 .1 8 5 3 .3 2 5 1 .7 2 1 1 .1 4 5 1 .0 6 8 1 .0 3 2 1 .0 1 6
1 9 9 7 13 .0 0 0 3 .5 7 4 1 .6 6 4 1 .1 8 5 1 .0 5 7 1 .0 2 3
1 9 9 8 24 .6 6 7 3 .9 5 3 1 .7 0 6 1 .1 4 8 1 .0 5 2
1 9 9 9 7 .9 0 5 3 .9 2 2 1 .5 7 6 1 .1 8 0
2 0 0 0 21 .5 0 0 4 .5 5 8 1 .6 6 7
2 0 0 1 10 .5 0 0 3 .9 6 6
2 0 0 2 5 .4 1 4
•Obvious Slow Down in Payments
Payout Lag
• Although databank only has CY
payments since 1990, payments are
being shown on all prior accident years
– So can compare length of payout pattern
and tail for different states
• New Jersey Extremely long
Sum of PAYMENT
Accident Year 10 11 12 13 14 15 16 17 18 19 20 21
1972 501250 492500
1973 1067500 592500 155000
1974 440000 299500 97500 295000
1975 145000 3500 82500 750000
1976 885000 730000 460000 298750
1977 1285000 110000 990000 977500 597000 840000
1978 97500 217500 175000 992500 770000
1979 240000 1320000 1577500 1097500 3345000 1200000 32500
1980 1173250 2127500 1732500 652500 1005000 42500 575000
1981 622500 692500 1905000 1380000 1765000 145000 222500 42500 295000 845000
1982 1900000 1003750 1700000 927500 199500 2697500 97500 375000 645000 2565000 1060000
1983 4742000 5807500 1887500 17500 315000 1567500 387500 245000 545000 1070000
1984 3015750 4005000 52000 1297500 485000 540000 970000 2850000 707500 97500
1985 1906250 7636250 602500 2267500 2512500 145000 1022500 1152000 1435000
1986 4885000 3340000 1615000 2001250 762500 4060000 1132500 395000 495000
1987 2940000 2037500 2885000 1195000 3290000 2593750 1632500 935000
1988 2105000 2121250 1422500 1231250 2697500 533750 590000
1989 5870050 2790000 4202500 12664500 3105000 520000
1990 5250000 3263750 10116250 3217500 2742500
1991 2617500 10080000 9617500 1341250
1992 25306000 4560000 1947000
1993 6744500 3444750
1994 3257500
Trend by Cause of Loss
CT Avg Severity by Cause of Loss
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
90
91
92
93
94
95
96
97
98
99
00
01
02
03
19
19
19
19
19
19
19
19
19
19
20
20
20
20
Diagnosis OB Surgery Treatment
Comparative Size of Loss Distributions
CT Im plied Indem nity ILF curve
8.000
7.000
6.000
5.000
4.000
3.000
2.000
1.000
0.000
0
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
10
20
30
40
50
60
70
80
90
10
OB Treatment Diagnosis
Implied OB/GYN ILFs
8.000
7.000
6.000
5.000
4.000
3.000
2.000
1.000
0.000
0
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
10
20
30
40
50
60
70
80
90
10
CA CT ID
All Physician/Surgeons Implied ILFs
6.000
5.000
4.000
3.000
2.000
1.000
0.000
0
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
00
10
20
30
40
50
60
70
80
90
10
CA CT ID
Comparative Frequency By State
State MA CA CO
Projected # 264 1,404 165
Payments
Population 6,433,422 35,116,003 4,574,579
Freq per 1000 .4% .4% .36%
Doctor Count 28,851 88,553 9,999
Freq per doc .9% 1.6% 1.65%
Doctor per 1000 4.485 2.522 2.185
Resident
Rules for Reporting to NPDB
• Entities such as insurance companies must report
practitioners on whose behalf medical malpractice payments
are made.
• Medical Malpractice payments must be reported to NPDB
within 30 days of the date of the initial payment.
• Civil penalties can be assessed for non-reporting and for
unauthorized use of NPDB information.
• Entities failing to report medical malpractice payments can be
assessed up to $11,000 for each unreported payment.
Compliance Issues
• The GAO did a study of the reporting to NPDB in 2000.
• Agency officials believe that some insurers and self-insured
organizations such as HMOs and other health plans should
report to NPDB but do not.
• In 2000, the agency identified 41 insurers that reported
payments to NAIC but not to NPDB
– 17 of the 41 companies have adequately explained the
discrepancies
– Of the remaining 24, 18 companies recognized their
omissions and agreed to file the delinquent reports
• About 25% (331) of the 1,300 malpractice reports
received in the test month (Sept 1999), were not
submitted to NPDB within 30 days of the initial
payment, as required. On average, these reports
were about 85 days late.
• More than 30 percent of the Sept reports, noted
delays between the date the report was submitted
to NPDB and the date that the information was
incorporated into the data bank. The median
processing delay was about 13 days.
• Agency officials believe that some
insurers may be using a technicality in
NPDB’s reporting requirements to
avoid reporting some practitioners.
– Corporate shield. Only practitioners who
are named in a settlement need to be
reported upon. So corporate shield
occurs when individuals filing malpractice
claims remove the practitioner’s name
from the claim leaving only the hospital or
another corporate entity as the
responsible party.
Other Issues regarding the
Data
• Companies in receivership may not be
reporting to the NPDB
– Example: New Jersey shows a huge decrease in the
volume of reports. Most likely the affect of MIIX and
PHICO
• States with Patient Compensation Funds –Data
needs special handling to appropriately match
fund payments with underlying payments.
Conclusion
• Insurers and Reinsurers can get into deep
trouble by not doing the appropriate due
diligence before writing MedMal insurance.
– Examples: Florida XPL, Claims made step factors
for Excess Losses
• Reinsurers can use the NPDB data to get a
better idea of what might be happening with
medical malpractice losses
• The use of the NPDB data requires actuarial
analysis to appropriately recognize problems
inherent in the data source.