Download Remove Voided Claims for Insurance Data

NESUG 2012
Finance and Insurance
Remove Voided Claims for Insurance Data
Qiling Shi
ABSTRACT
The purpose of this study is to remove voided claims for insurance claim data using SAS. Suppose that
for these voided claims, we don’t have a flag to identify them at first. All we know is that they have the
same absolute values with positive and negative amounts for the same insurance ID. A SAS macro with
procedures such as PROC SQL and DATA STEPS is employed to do the data analysis.
INTRODUCTION
An insurance Claim is filed by a policyholder stating that an insured event has occurred and that the
insurance company should provide coverage. An insurance adjuster could be set up with access to
portions of the claim to just the right payment amount. Insurance adjustment may be very complicated.
For example, claims that be rejected as duplicates may be adjusted, cancelled or resubmitted. Claims
that rejected for eligibility or other billing errors may be adjusted when the eligibility or billing issue is
resolved. At different times, billing transactions that have previously processed may be paid or repaid with
a duplicate payment.
The Division of Medicaid and the fiscal agent allow adjusting and voiding of claims. If a paid claim is being
adjusted, the Provider Identification Number and the Recipient cannot be changed. The voided amount
originally paid will appear as a negative amount and that amount will be deducted from payments until the
overpayment is recovered.
Usually we have a void claim identifier which can be used to as an edit in the system to exclude the
voided claims. Sometimes since some database or data warehouse is not real time and has different
updating time, the voided claims identifier will not be effective or even exist in the system. For the same
insurance ID, we have positive or negative amount charged which can be offset eventually. For these
claims, we will remove them as voided claims before calculating any overpayments.
SAS CODES
****************************************************************************;
*
Remove Void Insurance Claims
;
****************************************************************************;
%macro remove_void(indata, outdata);
*clear data before deleting voids;
data temp;
set &indata.;
if verify(id, '8') ~= ' ' and amount_charged not in (.,0);
run;
*create partial void claim IDs;
data temp;
set temp;
length voidid $200;
amc1=floor(abs(amount_charged));
amc=put(amc1, 20.);
insert='INT';
voidid=cats(id, insert, amc);
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Finance and Insurance
drop amc1 amc insert;
run;
*Sort dataset by void ID and amount_charged ascendingly;
proc sort data=temp;
by voidid amount_charged;
run;
data temp;
set temp;
by voidid amount_charged;
retain s1 0;
if first.amount_charged then s1=0;
if amount_charged >0 then s1=s1+1;
run;
*Sort dataset by void ID and amount_charged descendingly;
proc sort data=temp;
by voidid descending amount_charged;
run;
data temp;
set temp;
by voidid descending amount_charged;
retain s2 0;
if first.amount_charged then s2=0;
if amount_charged < 0 then s2=s2+1;
run;
*Get the void claim flags;
proc sql;
create table flag_temp as
select *, max(s1) as max_s1, max(s2) as max_s2
from temp
group by voidid;
quit;
data flag_temp;
set flag_temp;
if max_s1=max_s2 then void_flag='Y';
else if max_s1 < max_s2 then do;
if s2<=max_s1 then void_flag ='Y'; else void_flag='N';
end;
else do;
if s1<=max_s2 then void_flag ='Y'; else void_flag='N';
end;
if max_s1=0 or max_s2 =0 then void_flag='N';
run;
*Remove the void claims;
data voids;
set flag_temp;
if void_flag='Y';
run;
data novoids;
set flag_temp;
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Finance and Insurance
if void_flag='N';
run;
data &outdata.;
set novoids;
drop voidid s1 s2 max_s1 max_s2 void_flag;
run;
%mend remove_void;
*Read in the insurance data;
data insurance;
infile "C:\Documents and Settings\shiq\Desktop\New Folder\WUSS\void.txt";
input id $6. amount_charged tpl adjustment_indicator;
run;
*use the macro to Remove the void claims;
options symbolgen mprint mlogic;
%remove_void(insurance, insurance_novoids);
RESULTS
The demonstrated data set “INSURANCE” has 22 observations and 4 variables. One variable is called
“ID” which represents the claim identifications. “AMOUNT_CHARGED” means the provider charged on
the insurance agents or companies. “TPL” represents the other third party payment to this claim.
“ADJUSTMENT_INDICATOR” provides codes for different adjustment activities. Here the code “0”
represents the original claims. The other codes mean claims with different partial adjustments.
Table 1: The Dataset “INSURANCE” used as an example.
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For the same claim ID, there are different records with different adjustments and third party payments. We
want to find out the voided claims remove them from the above table.
The following is the intermediate table derived from “INSURANCE” containing the check results for voided
claims for the same claim IDs.
Table 2: Dataset “TEMP” used to check voided claims for the same claim IDs.
The field “VOIDID” is a concatenation of the insurance claim ID and the amount charged. Between “ID”
and “AMOUNT_CHARGED”, there are three characters “INT” to differentiate these two values in the field
of “VOIDID”.
The field “S1” is created to count the number of positive charged amounts for the same “VOIDID”. First
we sort the dataset by “VOIDID” and ascending “AMOUNT_CHARGED”. Then for the first value of
“VOIDID” in the sorting of “AMOUNT_CHARGED”, initialize S1=0. For positive amount charged,
S1=S1+1. Retain the value of S1 for the same “VOIDID”. Repeat these steps until we get all the S1
values.
The field “S2” is created to count the number of negative charged amounts for the same “VOIDID”. First
we sort the dataset by “VOIDID” and descending “AMOUNT_CHARGED”. Then for the first value of
“VOIDID” in the sorting of “AMOUNT_CHARGED”, initialize S2=0. For negative amount charged,
S2=S2+1. Retain the value of S2 for the same “VOIDID”. Repeat these steps until we get all the S2
values.
Table 3: Dataset “FLAG_TEMP”.
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From table 3, we can get the voided claim flags for all the records. The field “VOID_FLAG” is created. If
VOID_FLAG = ‘Y’ then this claim record is voided. If VOID_FLAG = ‘N’ then this claim record is not
voided. The “MAX_S1” is the total number of positive charged amounts for the insurance claim ID. The
“MAX_S2” is the total number of negative charged amounts for the insurance claim ID.
If MAX_S1 = MAX_S2, then all the records related to this claim ID will be identified as voided. If MAX_S1
< MAX_S2, then for all the records with S2 <= MAX_S1 assign VOID_FLAG = ‘Y’, otherwise VOID_FLAG
= ‘N’. If MAX_S1 > MAX_S2, then for all the records with S1 <= MAX_S2 assign VOID_FLAG = ‘Y’,
otherwise VOID_FLAG = ‘N’. If the insurance ID only have positive or negative charged amounts which
means MAX_S1 =0 or MAX_S2 =0, then let VOID_FLAG = ‘N’ for all the records of this insurance ID.
Table 4: Dataset “VOIDS”.
From table 4, we know that there are 12 voided claims altogether with VOID_FLAG = ‘Y’.
Table 5: Dataset “NOVOIDS”.
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From table 5, we know that there are 10 valid claims with VOID_FLAG = ‘N’. To get the final output
dataset, we can simply drop the fields like “VOIDID”, “S1”, “S2”, “MAX_S1”, “MAX_S2” and
“VOID_FLAG”.
REFERENCES
1. “Detecting Medicaid Data Anomalies Using Data Mining Techniques”, Southeast SAS Users
Group Conference, 2010.
2. “Find Potential Fraud Leads Using Data Mining Techniques”, Southeast SAS Users Group
Conference, 2011.
3.
“Assign Overpayment to Insurance Data with Adjustments”, Southeast SAS Users Group
Conference, 2011.
CONTACT INFORMATION
Your comments and questions are valued and encouraged. Contact the author at:
Qiling Shi, Mathematics PhD, Certified Fraud Examiner
Email: [email protected]
SAS® and all other SAS® Institute Inc. product or service names are registered trademarks or
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