Transcript T-SGS-0067_Power_Point_CMS_SAS_Day

Using SAS to Identify
Aberrant Provider Practice
Patterns
Mark Ireland, M.A.
DME Statistician, SGS
CMS SAS Day, October 31, 2007
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Introduction
• The SAS programming language provides an
excellent vehicle to create data analytic
techniques.
• SAS enables the user to have complete control
over data manipulations and does not confine the
analyst into sets of canned routines.
• SAS offers many modules that can be used to
perform a vast array of sophisticated statistical
models.
• The approaches presented here rely only upon the
Base SAS module, illustrating its programming
power.
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Some Simple Yet Effective Applications
• Outlier models – identifying provider over-utilization.
• Static models – utilization that does not change over
time by Beneficiary:
Services
Code combinations
Modifier usage
Higher level procedure codes/HCPCS
• Shifts in procedure code/HCPCS policy group utilization
– a technique to identify a significant increase in the
use of a procedure code or set of procedure codes.
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Outlier Models
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Outlier Metrics
• In the DME world all of the providers that supply a given
service are considered the “Peer Group.”
• “Average” utilization practice patterns can be measured
using the mean or the median utilization over all providers
in a similar peer grouping.
• Thresholds that identify aberrancies can be computed
using measures of variability such as the standard
deviation and the inter-quartile range (IQR).
• At the provider and procedure code/HCPCS level the
number of allowed services per beneficiary is chosen as
the metric to identify aberrancy.
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Using Quartiles to Measure Over-Utilization
One such model computes a threshold for services per bene
by adding 1.5 times the IQR to the third quartile:
(…)
PROC SUMMARY DATA=UNIVERSE;
CLASS HCPCS;
VAR SERV_PER_BENE;
OUTPUT OUT=SUMSTATS(RENAME(_FREQ_=NBENES)) P75(SERV_PER_BENE) =
P75_SERV_PER_BENE QRANGE(SERV_PER_BENE)= IQR_SERV_PER_BENE;
(…)
DATA COMBINE;
MERGE UNIVERSE(IN=A) SUMSTATS(IN=B);
BY HCPCS;
IF (A=1) AND (B=1) THEN OUTPUT;
(…)
DATA COMBINE;
SET COMBINE;
IF SERV_PER_BENE > P75_SERV_PER_BENE + 1.5* IQR_SERV_PER_BENE THEN
OUTPUT;
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A Surgical Dressing Example Using Quartiles to Construct a
Threshold
Histogram
45
40
Threshold = 21.4
Number of Suppliers
35
30
25
Frequency
Outlying Suppliers
20
15
10
5
0
2.27
6.14
10.01
13.89
17.76
21.63
25.51
29.38
33.25
37.13
More
Services per Bene
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Same Example Using the Mean and Two Standard Deviations to
Construct a Threshold
Histogram
45
40
Threshold = 28.7
Number of Suppliers
35
30
25
Frequency
Outlying Suppliers
20
15
10
5
0
2.27
6.14
10.01
13.89
17.76
21.63
25.51
29.38
33.25
37.13
More
Services per Bene
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Discussion
• Two standard methods of computing a statistical
threshold can be used.
• Many analysts commonly use the mean plus two
standard deviations to compute a threshold or
limit.
• The third quartile plus 1.5 times the IQR is also a
valid methodology.
• The threshold using quartiles is less sensitive to
extreme values and in some cases can identify
more suppliers for drilldown analysis.
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Static Utilization
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Identifying Static Practice Patterns Using SAS
• We can safely assume that not all patients are going to
need the exact same and amount of medical service or
supplies.
• Surprisingly enough there are suppliers that give their
beneficiaries:
the exact same number of services,
the same combinations of HCPCS,
use the same modifier for every service,
use one referring physician almost exclusively,
attach the same diagnosis for every beneficiary.
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Static Practice Patterns
• The task is to develop SAS code that can identify
these static patterns.
• One example looks at the Surgical Dressing policy
group to determine if suppliers are providing the same
number of dressing for each beneficiary for each date
of service.
• A large universe of data is used by SAS to extract
suppliers that provide the same number services to
their beneficiaries.
• The raw data is at the line level. We can use
percentiles to identify static use.
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Static Use of Surgical Dressings
The 95th and 5th percentiles of the number of allowed services are obtained
by supplier and HCPCS. If these two statistics are equal then we know
that 90% of the line items all have the same value:
PROC SUMMARY DATA=UNIVERSE;
CLASS PROVID HCPCS;
VAR SERV_PER_LINE;
OUTPUT OUT=SUMSTATS(RENAME(_FREQ_=NLINES)) P5(SERV_PER_BENE) =
P5_SERV_PER_BENE P95(SERV_PER_BENE)= P95_SERV_PER_BENE;
(…)
DATA COMBINE;
MERGE SUMSTATS(IN=A) UNIVERSE(IN=B);
BY PROVID HCPCS;
IF (A=1) AND (B=1) THEN OUTPUT;
DATA STATIC;
SET COMBINE;
IF P5_SERV_PER_BENE = P95_SERV_PER_BENE THEN OUTPUT;
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Static Bi-lateral Orthotic Utilization and Static HCPCS
Combinations
• Another variant of static utilization looks at HCPCS
“cocktails.”
• There are some suppliers that exclusively provide the same
set of orthotics for every beneficiary, and also supply bilaterally for all patients.
• In the case of bi-lateral orthotics we can compute the
average number of services per beneficiary for each
orthotic HCPCS. If this average is very nearly equal to 2.0
we know that the majority of patients received bi-lateral
services.
• HCPCS cocktails can be identified by ranking distinct code
combinations and then using percentiles of these ranks to
identify static behavior.
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Static Bi-lateral Orthotic Utilization
Each Orthotic could be billed on separate line items, or even separate claims.
To be on the safe side we need to aggregate items by supplier, HCPCS, and HICN:
PROC SUMMARY DATA=UNIVERSE;
CLASS PROVID HCPCS HICN;
VAR SERV_PER_LINE;
OUTPUT OUT=SUMSTATS(RENAME(_FREQ_=NLINES)) SUM(SERV_PER_BENE) =
SUM_SERV_PER_BENE;
PROC SUMMARY DATA=UNIVERSE;
CLASS PROVID HCPCS;
VAR SUM_SERV_PER_LINE;
OUTPUT OUT=NEWSUM(RENAME(_FREQ_=NLINES)) MEAN(SUM_SERV_PER_BENE)
= MEAN_SERV_PER_BENE;
(…)
DATA STATIC;
SET COMBINE;
IF (MEAN_SERV_PER_BENE > 1.95) AND (MEAN_SERV_PER_BENE < 2.05) THEN
OUTPUT;
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Static Code Combinations
• One can also use SAS to identify static HCPCS
combinations.
• A relatively common orthotic over-utilization
problem occurs when a supplier unnecessarily
provides orthotics for multiple parts of the body.
• For example, a beneficiary might only need an
ankle-foot orthotic (AFO) for the left ankle, but
is also provided with an AFO for the right ankle
as well as two knee orthotics as well as two
elbow orthotics and hand orthotics.
• Another example that has appeared involves the
simultaneous utilization of E0277 – Air Mattress
and E0260 – Hospital Bed with Mattress.
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Static HCPCS
• We need to build an arrays of HCPCS and then assign unique
ranks to each unique HCPCS array.
• One way to form such arrays is to process the data by provider
and HICN and build a text string for each HICN that consists of a
concatenation of each unique HCPCS.
• We can build these text strings using the FIRST DOT
methodology.
• Then we can assign unique numeric ranks to each unique text
string.
• Use the NODUPKEY option to remove duplicates and obtain
uniqueness.
• The methodology remains the same: if a provider has at least
90% of HICNs with the same rank, that corresponding HCPCS
string has been used for at least 90% of the HICNs.
• We use the same process that we used for static utilization of
surgical dressings: take the 95th and 5th percentiles of these
ranks and then check for equality.
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Static HCPCS Methodology
(…)
DATA SUMPOP;
LENGTH CODESTRG $50.;
SET POP;
BY PROVID HICN;
RETAIN CODESTRG;
IF FIRST.HICN THEN DO;
CODESTRG = HCPCS; /* Initialization */
END;
ELSE IF ~LAST.HICN THEN DO;
CODESTRG = HCPCS||CODESTRG; /* Concatenation */
END;
ELSE IF LAST.HICN AND ~FIRST.HICN THEN DO;
CODESTRG = HCPCS||CODESTRG; /* Concatenation */
OUTPUT; /* Output one record per HICN */
END;
(…)
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Static HCPCS Methodology
• This methodology will identify static patterns of HCPCS
that “make sense” to provide together, such as A4253 –
Glucose Test Strips and A4259 – Lancets.
• It will also identify providers that routinely bill HCPCS
that contradict each other as well, which makes for an
even more interesting case. This was illustrated on slide
16 with the example of E0277 and E0260.
• Sometimes a provider will have a static “core set” of
HCPCS and also bill for additional miscellaneous HCPCS.
• The method above will not capture this scenario since
these miscellaneous HCPCS technically define separate
text strings.
• One work around would be to use the SUBSTR function to
extract portions of the text string. Alternatively arrays
might be employed.
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A Static Orthotic HCPCS Combination Example
Below is an example of static orthotic usage:
HICN
AAA
BBB
CCC
DDD
EEE
FFF
GGG
HHH
III
HCPCS
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
DESCRIPTION
SERVICES
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
HICN
JJJ
KKK
LLL
MMM
NNN
OOO
PPP
HCPCS
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
L1843
L3760
L3919
DESCRIPTION
SERVICES
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
KO single upright custom fit
2
AFO w/ankle joint, prefabricated
2
HO w/o joints CF
2
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HCPCS Shifts
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Significant Shifts in HCPCS Utilization
• A “shift” in HCPCS utilization can be described as a
provider’s trend from one category of HCPCS to
another.
• While we can easily measure a provider’s relative
proportion of utilization of one set of HCPCS to the
remainder of HCPCS, it is more valuable to be able
to detect statistically significant shifts.
• As a way to illustrate this, suppose we want to
measure the degree of increase of provider
utilization of power wheelchairs.
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Significant Shifts in HCPCS Utilization
• We create two buckets of provider specific
utilization, one containing the power wheelchairs,
the other containing all other utilization.
• Obtain this division of provider utilization over two
distinct time periods. For other examples these time
periods might be divided according to an overall
spike in utilization or the creation of new HCPCS.
• SAS can accomplish these partitions using the DATA
step and creating two variables,
CODETYPE(ABUSED,NONABUSED) and
PERIOD(TIME1,TIME2), following reading in the
service dates and HCPCS.
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Significant Shifts in HCPCS Utilization
• First we SORT the data by CODETYPE, PERIOD, and
HICN, using the NODUP option to obtain unique
HICNs for each category.
• Then we use PROC SUMMARY again using CLASS
variables CODETYPE and PERIOD to obtain counts of
unique HICNs by CODETYP and PERIOD.
• Using the DATA step we can obtain the relative
proportion of HICNs in the ABUSED category by
provider for PERIOD=TIME1.
• This relative proportion can be thought of as a prior
probability that a HICN will fall into the ABUSED
category.
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Significant Shifts in HCPCS Utilization
• Now let’s assume that the relative proportion for
PERIOD=TIME2 will be “close enough” to that of
PERIOD=TIME1.
• We can use the Binomial probability distribution and
the prior probability computed using PERIOD=TIME1
to compute the probability that we would in fact see
the number of HICNs in the ABUSED category that
were obtained in PERIOD=TIME2.
• If this probability is small, say less than 0.05, then
we have a reason to believe that it is statistically
unlikely that we would see that many HICNs in
PERIOD=TIME2.
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Significant Shifts in HCPCS Utilization
• This probability can be thought of as a p-value.
• This approach would enable us to analyze large
universes of claims data and only output those
providers whose p-value is quite small.
• SAS can obtain this p-value using the CDF function
(Cumulative Distribution Functions) which can
compute cumulative Binomial probabilities.
• The syntax is PROB=CDF(‘BINOM’,m,p,n), where m
can be the number of HICNs in PERIOD=TIME2, p is
the prior probability computed from PERIOD=TIME1,
and n is the total number of HICNs in
PERIOD=TIME2.
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Significant Shifts in HCPCS Utilization
• The cumulative Binomial probability is the
probability that we would see less than or equal to
the number of HICNs in the ABUSED category for
PERIOD=TIME2 given the prior probability in
PERIOD=TIME1.
• The p-value must be computed as the probability
that we would see greater than or equal to the
number of HICNs observed in PERIOD=TIME2.
• 1-CDF(‘BINOM’,m,p,n) would give us the greater
than portion of the p-value, so we also need to
compute the equal to portion.
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Significant Shifts in HCPCS Utilization
• The equal to portion of the p-value can be
computed using the SAS function PDF(‘BINOM’,m,p,n).
• So our p-value can be computed as
1-(CDF(‘BINOM’,m,p,n)+PDF(‘BINOM’,m,p,n))
• For example, suppose provider A has the following
HICN categories where the abused category are
power wheelchair codes:
Time 1
Time 2
Abused Non-Abused Prior Probability
13
51
0.203
23
60
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Significant Shifts in HCPCS Utilization
Time 1
Time 2
Abused Non-Abused Prior Probability
13
51
0.203
23
60
• Just looking at the raw numbers in each category
from TIME1 and TIME2, it’s hard to tell if the
increase is significant.
• This providers ABUSED category showed an
increase, but there was an overall increase as well.
• Our p-value is computed as
1-[CDF(‘BINOM’,23,0.203,83)+PDF(‘BINOM,23,0.203,83)] =
1-[0.9612 + 0.0265] = 0.0123.
• Since this value is quite small (i.e. less than 0.05),
we conclude that it is a significant shift and we
should conduct drilldown analysis on this provider.
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Wrap Up
• It must be emphasized that these tools are mechanisms
to identify providers that might have utilization issues.
• A provider identified using any of these methods might
have legitimate reasons for a practice pattern and drill
down analysis can help determine the source of the
aberrancy.
• SAS techniques illustrated here included:
PROC SUMMARY to obtain summary statistics,
MERGE – merging summary statistics to raw data,
BY group processing using FIRST and LAST DOT,
Specialized SAS functions, CDF and PDF.
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QUESTIONS?
Mark Ireland, M.A., Statistician
SafeGuard Services, LLC
9795 Crosspoint Blvd, Ste 100A
Indianapolis, IN 46256
www.eds.com
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