MEDICARE PHYSICIAN
PROFILING
Statistical Informatics
BOB HOUCHENS, PhD
June 27, 2010
FULL REPORT
• The Use of an Episode Grouper for Physician
Profiling in Medicare: A Preliminary Investigation.
MedPAC, June 2009. (www.MedPAC.gov).
• Coauthors (Thomson Reuters):
• William Marder
Marder, PhD
• Scott McCracken, MBA
• Robert Kelley, MS
• Financial support from MedPAC; views expressed
are those of the authors.
2
OUTLINE
• OBJECTIVE
• DATA
• METHODS
• RESULTS
3
OBJECTIVE
• Identify “outlier”
outlier physicians with higher-thanhigher than
expected Medicare episode costs (standardized
p y
payments)
)
4
DATA
• All 2002 and 2003 Medicare claims for patients in
six MSAs:
– Boston, MA
– Greenville, SC
– Miami, FL
– Minneapolis, MN
– Orange County, CA
– Phoenix, AZ
• About 75 million claims p
per yyear
• 8 to 9 million episodes per year
5
METHODS
• Apply Thomson Reuter’s
Reuter s Medical Episode Grouper
(MEG) to Medicare claims data
• Attribute
Att ib t each
h episode
i d tto a physician
h i i
• Develop benchmarks for expected cost per episode
• Identify physician outliers
6
SINGLE ATTRIBUTION
• Episodes attributed to 37,000
37 000 physicians
• Each episode attributed to MD with highest
percentage
t
off Evaluation
E l ti and
d Management
M
t (E&M)
payments (> 35 %).
• Analyzed 25,000 physicians who had at least 20
episodes.
7
COST BENCHMARKS
• Standardized payments for services
• Expected payment = mean standardized payment for each
episode
p
classified by
y the combination:
– MEG (> 550 disease categories)
– Stage of MEG (integer stage 1, 2, 3)
– Diagnostic Cost Group relative risk score (RRS, 5 categories)
• Each episode assigned
– Observed
Ob
d standardized
t d di d paymentt
– Expected standardized payment
8
METHODS
• Flag MDs with risk
risk-adjusted
adjusted mean episode costs at
least 25 % above the mean for an average MD at
the .0001 significance
g
level
– Not interested in identifying MDs simply above the mean
– Low significance level helps address multiple
comparisons
– Separate analyses for each MSA and MD specialty
• Multilevel regression models
• Monte Carlo randomization tests
9
MULTILEVEL REGRESSION
For each MSA and specialty
For episode i within physician j:
ln Oij    0 j  1 ln( Eij )    k Dk  eij
K
k 1
0 j  0  u j
   Score for MD j
~ N 0,  [ln( E )] 
u j ~ N 0, 
2
u
eij
2
e

ij
10
MULTILEVEL REGRESSIONS
Ph
hysician Varian
nce
Total Variance
Episode Variance
Episode-Level
Residual
MD-Level Residual
MD Mean
Avg.
MD
MULTILEVEL REGRESSION RESULTS
12
CARDIOLOGISTS
13
BETWEEN-YEAR SCORE CORRELATIONS
Correlation between 2002 and 2003 MD scores
MSA
All MDs
Boston
0.90
Greenville
0.91
Miami
0 88
0.88
Minneapolis
0.86
g County
y
0.89
Orange
Phoenix
0.90
Total
0.89
Urologists
0.89
0.95
0 93
0.93
0.85
0.88
0.86
0.89
Cardiologists
0.88
0.88
0 88
0.88
0.79
0.87
0.87
0.87
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PERSISTENT OUTLIERS
15
MONTE CARLO RANDOMIZATION TEST
Episode #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
MEG
180
180
180
180
180
181
184
184
184
192
192
192
193
331
331
331
331
331
331
336
336
339
Stage
RRS
G
Group
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
3
1
1
1
Mean
MD
S
Sample
l
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
$ 114
334
96
151
55
141
3,475
623
5,680
625
527
1,188
110
96
78
171
264
68
3,995
625
14,900
151
$ 812
1,003
4,256
2,135
4,521
600
851
3 562
3,562
154
168
2,037
577
84
55
236
111
4 664
4,664
92
3,030
218
339
51
$ 55
1,221
80
44
55
120
528
15 141
15,141
154
58
81
168
51
416
177
66
157
80
5,690
41
5,384
487
$ 301
66
3,140
704
52
235
5,499
3 363
3,363
8,605
51
876
454
89
263
387
243
83
171
4,060
169
51
139
$ 655
51
41
51
139
87
2,822
1 123
1,123
7,218
336
51
279
55
641
125
456
1 538
1,538
69
584
172
212
907
$ 197
84
55
1,544
99
55
106
681
4,119
2,129
1,073
183
73
799
192
70
158
225
390
195
195
120
$ 1,521
$ 1,343
$ 1,375
$ 1,318
$ 801
$ 579
16
MONTE CARLO RANDOMIZATION RESULT
17
MONTE CARLO RANDOMIZATION RESULT
Distribution shifted right by ln(1.25) = .223
18
AN OUTLIER PHYSICIAN
19
AN OUTLIER PHYSICIAN
20
COMPARE MULTILEVEL TO MONTE CARLO
Corr = 93 %
21
COMPARE MULTILEVEL TO MONTE
CARLO, 2003
Randomization
Test Result
Multilevel Test Results
Number of MDs (% of total)
Not outlier
Outlier
Total
Not outlier
22,088
(94.6 %)
466
(2.0 %)
22,554
(96.6 %)
Outlier
127
(0 5 %)
(0.5
679
(2 9 %)
(2.9
806
(3 5 %)
(3.5
Total
22,215
(95.1 %)
1,145
(4.9 %)
23,360
(100.0 %)
22
TAKE-AWAYS
• Physician payment outliers can be identified
• Outliers are fairly persistent over time
• Substantial overlap in outliers between methods
• Method does not have to be a “black
black box
box”
• Next step: measure reliability of physician scores
23