1 1 
1:. 
Department of Health and Human Servces 
OFFICE OF 
INSPECTOR GENERAL 
NATIONAL DRG VALIDATION STUDY

UPDATE: SUMMARY REPORT

AUGUST 199
 . J

OFFCE OF INSPEcrR GENERA

The mission of the Office of Inspector General (OIG), as mandated by Public Law 95
452, as amended, is to protect the integrity of the U.S. Department of Health and 
Human Servces (HHS) programs as well as the health and welfare of beneficiaries 
served by those programs. This statutory mission is carred out through a nationwide 
network of audits, investigations, and inspections conducted by three OIG operating 
components: Office of Audit Servces (OAS), Office of Investigations (01), and Office 
of Evaluations and Inspections (OEI). The OIG also informs the Secretary of HHS 
program and management problems and recommends actions to correct them. 
OFFCE OF AUDIT SERVICE 
The OAS provides all auditing servces for HHS, either with its own resources or by 
overseeing work done by others. Audits examine the performance of HHS programs 
and/or its grantees and contractors in carrng out their respective responsibilties. 
Audits provide independent assessments of HHS programs and operations in order to 
reduce waste, abuse, and mismanagement; and to promote economy and effciency 
throughout the Department. 
OFFCE OF INTIGATIONS 
The 01 conducts criminal, civil, and administrative investigations of alleged 
wrongdoing in HHS programs or to HHS beneficiaries, and of unjust enrichment by 
/ providers. The investigative efforts of 01 lead to criminal convictions, administrative 
, or civil money penalties. The 01 also oversees State Medicaid fraud control 
. sanctions
units which investigate and prosecute fraud and patient abuse in the Medicaid 
program. 
AN 
OFFCE OF EVALUATION  INSPECTONS 
The OEI conducts short-term management and program evaluations (called 
inspections) that focus on issues of concern to the Department, Congress, and public. 
The findings and recommendations contained in these inspection reports generate 
rapid, accurate, and up-to-date information on the efficiency, vulnerabilty, and 
effectiveness of departmental programs. 
The information for this report was collected under contracts with Baxer-Health Data 
Institute and BOTEC Analysis Corporation. A list of project participants appears in 
Appendix A.  Department of Health and Human Servces

OFFICE OF

INSPECTOR GENERAL

NATIONAL DRG VALIDATION STUDY

UPDATE: SUMMARY REPORT

AUGUST 199 OEI-12-8190
 EXECUTIVE  SUMMAR 

PUROSE 
Ths  , 9th Edition 
inspection reabstracted the International Classification of Diseases
Clinical Modification (ICD- CM) codes from a sample of Medicare discharges on a 
blinded basis. It compared the resulting diagnosis-related group (DRG) to the 
hospital's DRG to determine over-reimbursement or under-reimbursement. The 
sample was nationally representative and covered all of calendar year 1988, the most 
recent data available. 
Ths study updates a previous inspection from the Office of Inspector General (OIG). 
It found that 20.8 percent of 1985 Part A bils contained coding errors that changed 
the DRG and that 61.7 percent of these errors over-reimbursed the hospitals. This 
improper DRG "creep" increased total prospective-payment system (PPS) 
disbursements by 1.9 percent or $308 milion. 
FIINGS 
DRG coding error reduced: 14.7 percent of 1988 discharges had DRG coding errors. 
Ths proportion of coding errors was statistically significantly lower than the 20. 
percent reported for 1985. 
DRG creep eliminated: 50.7 percent of DRG errors over-reimbursed the hospital. 
This proportion differed signifcantly from the 61.7 percent over-reimbursed reported 
: for 1985. 
DRG coding errors. overall. no longer over-reimburse hospitals: Taken together, 1988 
DRG errors had the net financial effect of under-reimbursing all hospitals a non
significant $69.8 milion, or 0.1 percent of the $52 bilion in 1988 PPS disbursements. 
Projected nationally, over-reimbursements totaled $2 657.8 milion and under-
reimbursements totaled $2 588.0 milion. 
Mis-specification errors under-reimbursed the hospitals: Of the 361 DRG errors in the 
sample, 63.2 percent occurred because the attending physician mis-specified the 
narrative diagnoses. Of these 227 mis-specification errors, only 43.2 percent over-
reimbursed the hospitals. The statutorily-required attestation probably reminded 
physicians of their obligation to select accurate narrative diagnoses. 
Resequencing errors over-reimbursed the hospitals: Of the 361 DRG errors in the 
sample, another 26.6 percent occurred because the hospital substituted a secondary 
diagnosis for the (correct, narrative) principal diagnosis. Of these 96 resequencing 
errors, 66.7 percent over-reimbursed the hospitals. The sentinel effect of the Peer 
Review Organizations (PRO) surveilance apparently did not fully prevent over-
reimbursement due to resequencing.  RECOMMATIONS 
The Peer Revew Orgations should contiue their sureilance of hospita
cog 
for DRG reimburment acccy. 
The attestation requirement appears to have deterred 
over-reimbursement due to 
mis specifcation by attending physicians, causing their hospitals to be under-
reimbursed. However, the sentinel effect did not fully prevent over-reimbursement 
due to resequencing by hospitals. Although these two trends approxiately offset 
each other, this equilbrium may not continue in the future. 
The Agency for Health Care Policy Research plans to reabstract the ICD-
codes from a large sample of Medicare charts to determe 
their diagnostic 
accuracy for health servces research purposes. Using 
1991 data, grouping these
ICD- CM codes to DRGs, and selecting reasons for any diferences would provide 
a third time point with which to track trends in reimbursement accuracy. The OIG 
supports this effort. 
COMM 
AGENCY 
In its May 29, 1992 comments to the draft of this inspection, the Health Care 
Financing Agency (HCFA) concurred with the OIG recommendation. The HCFA 
noted that the improvement in DRG coding accuracy may be attributable to increased 
hospital experience with PPS, PRO review, and HCF A educational efforts. 
/ The HCF A also made a number of technical comments. Based on these comments 
the OIG made several changes to this report. The full text of the HCFA comments 
appear as an appendix.  .....................................................................  
. . . . . . . . . ..  ..  ..  ..  ..  ..  ..  ..  ..  ....    ... .  . .. . .  .. .. . . . ..  .  . ..... .    .. ..... .   . .  ... ....   .    ... ...   . . . ... ..  . .   .  .. ..... .  .     . .. ......  ..      ..  ...... .  .      ..' ......  .      .........        .........      .  ........         .........         .........         ........ .       . ...... ..     .   .... ....  '  .   ..........         ... ......        . .........         . ..........          .. .......  .  .  .  .... .  ...      .  .... ......  .     . ......... . ... ......         
. . . . . . . . . ...   ...   ....    ....   ....    ....    ....    ....    ....    ....   ....    ....    ...   ...   ...   ...   ...   ...   ...  ..  ..  ..  ..  ...   ...   ...   ...   ...   ...  . .  ...   ...   ...   ...   ...   ..  ..  . .. 
TABLE OF CONTENTS

SUMY. 
EXECUTIVE 
TABLE OF CONTNTS 
mTODUCfION 
ORIGIN OF TH PREENT STUY 
METHODOLOGY 
SAMLE REPRESENTATIVNESS

FININGS. . 
DRG CODING ERROR REDUCED 
DRG CREEP ELIMINATED

MIS-SPECIFICATION UNER-REIMURSES, WfILE
REEQUENCING OVER-REIMURSES 
CERTAI DRGS AR MOST SUSCEPTffLE TO ERROR 
RECOMMNDATIONS 
ENDNOTE 
APPENDIX A: PROJECf PARTICIPANS 
APPENDIX B: HCFA COMMNTS  INTRODUCTION

Since the inception of the Medicare program, health care expenditures have grown 
faster than the rest of the economy, and the Medicare program has grown even faster 
than general health care expenditures. The need for innovative reimbursement 
policies and their evaluation has become critical to medical communities, beneficiaries 
and payers. 
TH PREEN STUY 
ORIGIN OF 
, Medicare has provided hospital and medical insurance to the nation 
Since 1965
elderly and disabled.  However, rapidly escalating health care costs coupled with a 
rise in the proportion of the population eligible for Medicare have burdened its 
hospital insurance trust fund (Part A). Health care costs accounted for 7.4 percent of 
the gross national product in 1970, 9.1 percent in 1980, and 10.9 percent in 1986. In 
1970, Medicare paid 8 percent of all health care costs, but by 1986 Medicare 
proportion increased to 17 percent.

In 1983 Congress changed Medicare inpatient reimbursement from a retrospective 
3 Under the PPS 
reasonable-cost basis to a prospective-payment system (PPS).
hospital payments depended upon the patient s diseases and procedures as defined by 
the International Classification of Diseases, 9th Edition, Clinical Modification (lCD-
4 The PPS "
CM). grouped" various combinations of the approximately 10 000 ICD
CM codes into 476 diagnosis-related groups (DRGs).5 By reimbursing the average 
cost of care for each DRG, the PPS provided incentives for effcient delivery of health 
' care. The Veterans Administration Medical System Resource Allocation Method 
, emulated this change. 
A hospital files a claim for Medicare payment at the time of patient discharge. The 
attending physician writes the narrative diagnoses and procedures on the face sheet; 
and attests to the principal diagnosis, secondary diagnoses, and any procedures. The 
hospital's medical records department then assigns numeric, ICD- CM codes to the 
narrative diagnoses and procedures, using the rules of the Uniform Hospital Discharge 
Data Set (UHDS), "a minimum data set used by HHS programs to collect data on 
individual hospital discharges on a continuing basis.'t6 The hospital's biling 
department enters up to five diagnoses and three procedure codes on the Medicare 
claim form. A Fiscal Intermediary (FI) for each State receives the hospital bils. 

runs GROUPER software to select the correct DRG by assessing diagnosis and 
procedure codes. It then runs PRICER software that adjusts for geographic location 
teaching costs, and other factors to calculate the payment due to the hospital. 
Since 1976 the Office of the Inspector General (OIG) has had responsibilty for

protecting the integrity of the programs and program beneficiaries of the U.

7 Following this statutory

Department of Health and Human Servces (HHS).
mandate, the OIG commissioned the 1985 National DRG Validation Study (1985

Study), which examined how the processes that assigned diagnosis and procedure
 codes affected the accuracy of PPS reimbursement for Medicare discharges.8 The 
1985 Study analyzed a representative, national' sample of medical records to obtain 
inormation on the accuracy of the diagnosis and procedure coding, the impact of
codig errors on DRG assignent, and the potential net fiancial impact of 
coding
errors. It also assessed the appropriateness and quality of the servces provided to 
Medicare patients. 
The 1985 Study found an overall error proportion of 20.8 percent in assigng DRGs. 
In 61.7 percent of the errors, the hospitals over-reimbursed themselves. Physician mis
bilg 
specifcation of narrative diagnoses and hospital 
department resequencing of 
diagnoses caused most errors. These errors caused $308 mion in overpayments to
hospitals, 1.9 percent of PPS disbursements. 
The Health Care Financing Admistration (HCF A), as the payer of the Medicare
reimbursements, made the followig points in commenting on the 1985 Study. 
The PPS started in 1983. The 1985 Study data covered FY 1985. Physicians
hospital coders, and Peer Review Organiztions (PROs) needed a longer
learng period to adjust to the new payment and qualty monitorig system. 
The 1985 Study appeared in 1987, so that its coc1usions deried in part from 
inormation and coding conventions that had undergone updating by that time. 
The HCF A and hospital associations recently had begun to conduct traing
semiars for hospital personnel. The HCF A therefore expected-1hat coding
errors would decrease over time. 
To examie these trends, the OIG intiated a follow-up project, implemented through
contracts with the Health Data Institute (HDI) of Lexigton
, Massachusetts; American 
Medical Records Association (AM) of Chicago, Ilois; and BOTEC Analysis
Corporation (BOTEC) of Cambridge
, Massachusetts using 1988 data. The purpose of
this inspection, the DRG Validation Study Update (1988 Study), was to determe 
how the PPS' s coding accuracy had changed over time. The OIG designed this
inspection to meet the followig objectives. 
Replicate the 1985 work on coding accuracy, using 1988 data. 
Compare the findings from 
1985 and 1988. 
Identif patterns of coding errors that PPS changes could modify. 
As in the previous study, this inspection assessed how 
coding errors affected hospital
reimbursement and identifed the 
coding problems most subject to error. It evaluated 
hospital and beneficiary characteristics for their relative contnbution to 
DRG errors.  METHODOLOY 
The OIG randomly selected 2 680 discharges from 1 744 acute care hospitals. The 
study population consisted of the 10.8 milion Medicare funded discharges for calendar 
year (CY) 1988 from the 6 715 acute care, short-stay hospitals in the United States. 
The design excluded discharges from specialty institutions such as children s hospitals 
tuberculosis units, and psychiatric facilties. It also excluded discharges in Maryland 
and New Jersey, which the PPS stil exempted in 1988. It excluded bils for pediatric 
obstetric, and psychiatric DRGs (pricipally drug and alcohol rehabiltation performed
by a general hospital). Unlike its FY 1985 predecessor, it included hospitals 
established since the advent of the PPS in 
1983. 
The OIG requested that the hospitals' medical records departments send complete 
copies of the selected medical records to the OIG' s contractor, the HDI. With follow-
up, the OIG ultimately obtained 2 451 medical records, 91.4 percent of those selected. 
The OIG compelled the cooperation of four hospitals by administrative subpoenas. 
AM 
The  reabstracted the charts by selecting ICD- CM codes supported by the 
record, selected the principal diagnosis, and grouped to select the DRG. To ensure 
that the original ICD- CM codes and DRG codes did not affect the AM' 
AM 
reabstraction, the  coders conducted this reabstraction without knowledge of 
the original ICD- CM codes and DRG codes. The coders had instructions not to 
treat marginal problems or honest differences in judgment about appropriate coding 
as DRG errors. This standard should have produced a conservative estimate of the 
proportion of discharges having DRG errors. 
The AMRA also identifed the reasons why a hospital's bil differed from the correct 
codes. Where multiple reasons applied, the coders had instructions to select the first 
chronological reason (i.e., mis-specification -+ miscoding -+ resequencing -+ other). 
AM 
Finally,  entered its reabstracted codes into a personal computer database 
provided by the OIG. A series of reliabilty checks verified the reproducibilty and 
AM 
accuracy of the  coding. 
SAMLE REPREENATINES 
The sample accurately represented the characteristics of the underlyig population.
Distributed by hospital demography, it did not differ from the population in bed size 
teaching status, location, or control. (Figure 1). 
The sample also accurately represented the underlying population by patient 
age and
sex. However, the OIG made remedial efforts to classify unknowns by race. This 
match to other government fies reduced the proportion of sample unkowns in 
comparison to the underlying population.ll The volume of cases precluded a similar 
reclassification of unkowns for the entire population. Without the OIG' 
reclassification, the sample would also have conformed to racial distribution of the 
underlying population. (Figure 2).  .-....-. 
-- 
Bed size  Teaching status 
TII  Tea1n
10029 
NoIn 
Saple  Poplation  Saple  Population 
Lotion 
Control 
r= MeII  r: 
MeII 
No 
Pr Nopl 
Gome 
Nome  Gome
Saple  Population  Saple  Population 
Figue 1: Sample representativeness by hospital demography, 
1988 
Age  Sex 
6574  Mal 
6574  Mal 
75
75-
Fem 
Sample  Population  Sample  Population 
Race 
D Wh 
. Ot 
I8 
Sample  Population 
Figue 2: Sample representativeness by patient demography, 1988 
Because of its random design, the sample should also accurately represent the 
distribution of DRGs.  FINDINGS

REUCE 
DRG CODING ERROR 
Of the sample discharges, 14.7 percent 
Percnt 
proved to have coding errors that 
changed their DRGs. This proportion 
was statistically significantly lower than 
the 20.8 percent for 1985 (18.4 percent

after reweighting to make the 1985 
sample comparable). The standard 
198 
7 indicated this point estimate 
error of 0.
, a secondary effect of 
to be quite precise 198 
the sample size. (Figure 3). 
No hospital demographic characteristics 
had a statistically significant effect upon 
the proportion of DRG coding errors.  Figue 3: Proportion of discharges with 
DRG coding errors, 1985 and 1988 
Smaller hospitals had higher proportions 
of miscodes, but this apparent difference 
did not attain statistical signifcance. 
Similarly, no patient demographic characteristics had a statistically significant effect

upon the proportion of DRG coding errors. Discharges of younger patients had

" higher error rates, However, this difference also failed to attain statistical significance. 
CR 
ELIATED 
DRG 
The 1988 DRG errors divided evenly  Percnt 
Hospitals 
between errors that had over-reimbursed 
over.relmburs 
the hospital (50.7 percent) and under-
under-reimbursd 
reimbursed the hospital (49.3 percent). 
This non-directionality differed 
significantly from the 61.7 percent of 
1985 coding errors that over-reimbursed 
the hospital. (Figure 4). 
The equal division between coding errors 
that over-reimbursed and under-
reimbursed the hospital occurred across 
1985  198 
all hospital characteristics. For-profit 
Figue 4: Direction of DRG coding errors 
, hospitals over-reimbursed themselves 
more than did other  1985 and 1988

tyes of hospitals
but these differences did not attain  statistical signifcance. The direction of coding error also exhbited no statistically

significant trend by patient characteristics.

The OIG calculated the 
financial effect

of coding errors using the case-mix index 
Cae-mix Index

(CMI), which quantifies the complexity 
Beore
. and resource intensity of Medicare 
DAfer
reimbursed discharges. Hospitals with 
higher mean relative weights serve

Medicare patients with more complex

conditions and therefore consume more

resources. Ths 
inspection calculated

CMI as (1) submitted to the Fls for

reimbursement, (2) reabstracted by the

AM 
on a blinded basis, and (3) the  198  198

difference or mean weight change due to

coding inaccuracy. After AMRA Figue 5: Case-mix index before and after 
recoding, the overall CMI increased DRG reabstraction, 1985 and 1988 
0023, a statistically nonsignificant 
difference. Exrapolating this financial change to all 10.8 milion Medicare discharges 
DRG coding errors under-reimbursed hospitals by $69.8 milion. This difference 
amounted to only 0.1 percent of the $52 bilion in 1988 PPS expendituresP The 
average hospital biled for  less reimbursement for itself than it should have 
slightly 
received. (Figure 5). 
WH 
/ MIPECIFCATION UNER-REIMURSES  REEQUENCING
. OVE-REIMURSES 
Each stage of the reimbursement 
Reason

process introduced coding errors for a

Mis-specification

different reason. The OIG identified 
three tyes of errors: mis-specification  Miscoing

miscoding, and resequencing. Mis- Resequencing

specification describes physician errors in  . Oter

attesting to the narrative diagnoses.

Miscoding refers to the medical records

department selecting the wrong ICD
CM code for a correct narrative 
diagnosis. Resequencing involves the 
1985  1988
biling department improperly 
substituting a secondary diagnosis for the  Figue 6: Reasons for DRG coding errors 
correct principal diagnosis. (Figure 6).  1985 and 1988 
Narrative changes accounted for 63.2 percent of the 361 DRG errors. Examples of 
mis-specification include (1) the physician selecting the wrong principal diagnosis and 
(2) including or excluding a complication, comorbidity, or operating room procedure.  Resequencing cause 26.6 percent of DRG errors. In all of these cases, the 
review disagreed with the providers' listing sequence. Usually, a secondary ,diagnosis
was listed as the principal diagnosis. In other cases, the diagnosis needed greater 
specificity. ICD- CM ruling changes accounted for the remaining sequence changes. 
Miscoding comprised 9. 1 percent of DRG errors. The incorrect numeric code was 
, usually for the -principal diagnosis, rather, than for complications, comorbidities, or

operating room procedures. A few changes represented inaccurate discharge

destinations or apparent tyographical errors.

Mis-specification significantly under-

reimbursed hospitals; while resequencing 
160

Hos

significantly over-reimbursed them. 
o1v-8rbu-rrs 

Thus, the attending physicians selected  100
their patients' diagnoses and procedures

with caution, earning their hospitals less

reimbursement than they should have

received.

The biling departments behaved more 
aggessively, obtaining greater

MIwMIs 
reimbursement than their employers  -100 Resue OC 
should have received. Possibly the 
hospitals had greater influence over  Figue 7: Reasons for DRG changes by 
employees, or the employees identified  direction of reimbursement change, 1988 
.' closely with the economic interests of the 
, hospital. The sentinel effect of the Peer Review 
Organizations surveilance did not 
fully prevent over-reimbursement due to resequencing. (Figure 7). 
These two trends, under-reimbursement due to physician mis-specification and over-
reimbursement due to resequencing, approximately offset each other; so that overall 
hospitals received correct reimbursement. Whether this equilbrium 
will continue is 
unknown. 
CETAI  AR 
DRGS  MOST SUSCEPTLE TO ERROR 
In this random sample of discharges, vascular disorders such as heart failure, angina
stroke, and arrhythmias cause Medicare hospitalizations most frequently. Overall
, 28
of the 476 DRGs accounted for half of the total bils, and 10 DRGs accounted for 28. 
percent of all bils. Most coding errors fell into these DRGs, but not in direct ratio to 
the DRGs' volume. (Table 1). 
This report identified DRGs with high proportions of coding errors by dividing the 
number of errors by the frequency of bils for specific DRGs. Many of these DRGs 
covered vague or nonspecific diagnoses such as atherosclerosis (DRG 132), other
circulatory system procedures (DRG 120), respiratory signs and symptoms (DRG 99),  pectoris 
DRG description 
n (%) 
Heart failure and shock 
127 
133 (5.4) 
11440  ASSnpgienica imfic cperelberov apscunlare disuordmers eoxcnepti TaIA  89 (3.
89  75 (3.
74 (3. 
182 Esophajgiotis, gaisntrointetst inapl, anrd moiscelclaneeous ddigestuive rdiseorder s 66 (2.

96 Bronchitis and asthma with complications 
63 (2.

209 Major 
15 TOrantsiehnt eiscrhem ic 1att
acks 55 (2.
47 (1.9)
138 Cardiac arrhythmia and conduction disorders 
45 (1.
296 Nutritional and miscellaneous metabolic disorders with complications 44 (1.
760 (71.8) 
Total 
451 (100. 
Table 1: DRGs biled most 
frequently, 1988 
and other nervous system disorders (DRG 34). This 
indeterminateness suggests 
inherent ambiguities in medical taxonomy. For example, DRG 99 includes apnea 
dyspnea, hemoptysis, hypercapnia, pleurodynia, stridor, and ventilatory failure. (Table 
2). 
DRG description  Miscoded 
Number (%) 
132  Atherosclerosis 
3 (75.

413  Myeloproliferative 
4 (66.

185  Dental except extractions 
2 (66.

120  Other circulatory system procedures 
3 (66.

Respiratory signs and symptoms 
5 (62.

403  Lymphoma and nonacute leukemia 
3 (60.

Other nervous system disorders 
3 (60.

Other  420  341 (14.

Total  451  361 (14. 
Table 2: DRGs with high proportions of coding errors, 1988 
Over-reimbursement concentrated in selected DRGs and certain 
tyes of hospitals.
This report identified DRGs with maxmum savigs potential by multiplying the error
frequency by reimbursement change for each DRG. Although this inspection found 
no overall over-reimbursement to hospitals, certain DRGs stil significantly over-
reimbursed the hospitals. In particular, 13 DRGs each had over $20 milion in 
projected overpayments. This group consisted primarily of DRGs with operating room 
procedures, probably because of their high relative 
weights. (Table 3).  . .
DRG description	 Over-reimbursement 
$ per  $ milion 
discharge  total 
104  Cardiac valve procedure with pump & cath  4915  81.1 
468  Unrelated operating room procedures 
1128  78. 
475  Respiratory system diagnosis with ventilator  791  49. 
110  Major reconstructive vascular procedures  682  47. 
191  Major pancreas, liver, & shunt procedures  3313  43. 
154  Stomach, esophageal, & duodenal procedures  1114  40. 
Pulmonary edema & respiratory failure 
571  37. 
Respiratory system operating room procedures 
764  25. 
Respiratory neoplasms 
306  24. 
121  Myocardial infarction discharged alive  189  23. 
415  Operating room procedure for infection  858  22. 
148  Major large & small bowel procedures  202  22. 
217  Wound debridement & skin graft 
333  22. 
Other	 235  263 -587. 
Total	
451  69. 
Table 3: DRGs with maxmum savings potential, 1988 
Additionally, small and for-profit hospitals made more errors that over-reimbursed 
themselves than did other tyes of hospitals. This net over-reimbursement totaled 
2 milion. 
/ $842. RECOMMENDATIONS

Orgations 
~ The Peer Review  should contiue their sureilance of hospita

cog 
for DRG reimburement accurcy.

The attestation requirement appears to-have deterred over..reimbursement due to 
mis-specification by attending physicians, causing their hospitals to be under-
reimbursed. However, the sentinel effect did not fully prevent over-reimbursement 
due to resequencing by hospitals. Although these two trends approximately offset 
each other, this equilbrium may not continue in the future. 
The Agency for Health Care Policy Research plans to reabstract the ICD-
codes from a large sample of Medicare charts to determne their diagnostic 
accuracy for health servces research purposes. Using 
1991 data, grouping these
ICD- CM codes to DRGs, and selecting reasons for any differences would provide 
a third time point with which to track trends in reimbursement accuracy. The OIG 
supports this effort. 
COMM 
AGENCY 
In its May 29, 1992 comments to the draft of this inspection, the HCFA concurred 
with the OIG recommendation. The HCFA noted that the improvement in DRG 
coding accuracy may be attributable to increased hospital experience with PPS, PRO 
review, and HCF A educational efforts. 
/ The HCF A also made a number of technical comments. Based on these comments 
the OIG made several changes to this inspection. The full text of the HCF 
comments appear as an appendix.  ENDNOTE

1. Schweiker R S. Report to Congress: hospital prospective payment for medicare. 
Washington, DC: U.S. Department of Health and Human Servces, 1982. 
2. Social Security Amendments of 1965. Pub. L. No. 89- , 79 Stat 286 (1965). 
3. Social Security Amendments of 1983. Pub. L. No. 98- , 97 Stat 65 (1983). 
4. Commission on Professional and Hospital Activities. International 
classification of 
diseases, 9th edition, clincal modifications. Ann Arbor, MI: Edward Brothers
, 1980. 
5. Fetter R B, Shin Y, Averill R F, & Freedman A T. The new ICD- CM diagnostic 
related groups classification scheme user s manual: final report. New Haven, Cf: Yale 
School of Organization and Management, Health Systems Management Group, 1981. 
6. Fed Reg, July 31, 1985; 50 (147): 31038. 
7. Shriners' Hospital for Crippled Children -- Colorado Corporation -- Conveyance. Pub. 
L. No. 94-505; October 15, 1976: Title II  201-07. 
8. Delaney A M & Hsia D C. 1985 national DRG validation study. Lexington, MA: 
Health Data Institute, 1987. Report prepared for the Office of Inspector General. U. 
Department of Health and Human Servces; contract HHS-
100-87-0015. 
, 9. Hsia D C, Krshat W M, Fagan A B, Tebbutt J A, & 
Kusserow R P. Accuracy 
diagnostic coding for medicare patients under the prospective-payment system. N Engl 
J Med, 1988; 318: 352-55. 
10. Division of Information Analysis, Office of Statistics and Data Management, Bureau 
of Data Management and Strategy, Health Care Financing Administration
, U.
Department of Health & Human Servces. 1989 HCF A Statistics. Baltimore, MD:
Health Care Financing Administration, 1989: 16 & 34. 
11. Ruther M, Reily T W, Silverman H A, & Abbott D B. Medicare and Medicaid data 
book, 1990. Baltimore, MD: Office of Research and Demonstrations, Health Care 
Financing Administration, U.S. Department of Health & Human Servces
, 1991: 15. 
HCFA pub. no. 03314. 
12. Division of Information Analysis, Office of Statistics and Data Management, Bureau 
of Data Management and Strategy, Health Care Financing Administration
, U.
Department of Health and Human 
Servces. 1989 HCF A statistics. Baltimore, MD:
Health Care Financing Administration, 1989: 25. HCFA publication 
no. 03294.  . .
APPENDIX A

PROJECf PARTICIPANTS

OIG

Cathaleen A. Ahern, B.

Evan J. Buckingham, B.

David C. Hsia, J.  , M.  , M.

Thomas F. Komaniecki, M.

W. Mark Krshat, M. 
Unda M. Moscoe, B. 
Brian P. Ritchie, B.A. 
Barr L. Steeley 
John M. Traczyk, B. 
HCFA

Timothy F. Greene, M.  , M.

Stephen F. Jencks, M.

Michael R. McMullan, M.

Harr L. Savitt, Ph.

Jeanette M. Smith, M.  , M.

Malcolm A. Sneen, B.S.

RA 
Corporation 
Haya P. Rubin, M.  , Ph. 
Baxer-Health Data Institute

Patricia J. Baxer, R.

Patricia Cassidy-Tsnosas, R.

Annette M. Delaney, R.  , M.

Ellen B. Inghileri, R.

Janet Mathews, A.

Laurie H. Moore, R.

Claire Shannon, AR.

Michele A. Wiese, B.

Margret K. Amatayakul, M.B.A., R. 
Mary Converse, R. 
1. Institutions appear in chronological order of involvement. 
2. Now at Health Audit Servces, Ellcott City, MD. 
3. Now at the Journal of the American Medical Association, Chicago, IL. 
4. Now at Johns Hopkins Medical Institutions. 
5. Ceased operations February 16
, 1990.  Nicholas J. Cotsonas, M.

Linda Ertl, R.

Rita M. Finnegan, R.

Desla Mancila, A.

Barbara Manny, R.

Sonia Martiuk, R. 
, AR. 
Toula Nicholas
Charlotte Razor, R. 
LouAn Schraffenberger, R. 
Lynn, Smetko, R. 
Dawn Smith, AR. 
Joan Zacharias, A.R. T. 
BOTEC Analysis COI:poration 
Geraldine M. Berenholz, R. 
Andrew H. Chalsma, B. 
David P. Cavanagh, M.  , Ph. 
Janet W. Knight, R.  , Ph. 
Amy L. Lockwood, B. 
6. Outside contractor.  APPENDIX 
HETH CAR FIANCIG ADMITION COMMNT  . ,

('  '' 
""Ie 
Health Care 
DEPARTMENT OF HEALTH & HUMAN SERVICES 
lt:  Financing Administration 
Memorandum 
"OWIG 
MAY 2 9 

Date 
From  Wilam Toby; Jr. 
Actig Administrator 
Subject 
OIG Draft Report: "National DRG Valdation Study Update: Summary

Report" (OEI-12-89-O190) and "National DRG Valdation Study Update:

Technical Report" (OEI-12-89-O191)

Inspector Genera 
Offce of the Secretary 
dr 
We have reviewed the above-referenced 
report which are 
updates to a previous OIG study which re-abstrctd the International 
Cliica Modcation coes from a 
Clasifcation of Diseaes, 9th Edtion, 
saple of 
1985 Medicare discharges. These report update the previous study 
cog 
work on  data comparg 
by: replicating the 1985  accuracy using 1988 
identig  cog 
the findings from 1985 and 1988, and  pattrns of  errors that 
mod. 
the Prospectie Payment System (PPS) changes could  The saple 
was nationaly representative and covered al of caenda yea 1988, the most 
recnt year for which data were available. 
OIG found a signifcat improvement in hosita cog of PPS cas 
between 1985 and 1988. In the 1985 study, OIG found that 20.8 percent of 
cog 
1985 Par A bil contaed  errors that changed the dignosc related 
group (DRG) and that 61.7 percent of thes errors over-reimbursd the 
cog 
diurments 
hospitals. This improper DRG  incread tota PPS 
9 percent or $308 m.on. Durig the 1988 study, OIG found that 
cog 
14.7 percent of the diharges hadDRG  errors 51 percnt of DRG
errors over-reimbursed the hospita and 49 percnt under-reimburd the 
hospita. Taken together, the 1988 DRG errrs rested in no net 
overpayment of hospita. 
We believe thes report are a useful addition to the contiuing 
cae mi 
research and evaluation of  change among hospita paid under PPS. 
We agree with OIG' s recommendation that Peer Review Organtions 
contiue their surveilance of hospita cong of DRG reimbursment 
accuracy. Our detailed comments on these two report are attched for your 
consideration.  " .
Page 2 - Inspector General 
Thank you for the opportnity to review and comment on these draft 
report. Please advise us if you agree with our position on the report' 
recommendation at your earliest convenience. 
Attachment  J ;-
Comments of the Health Care Financing Administration (HCEA
on OIG's Draft Reports: "National DRG Validation Study Update: 
Summary Report" OEI-12-89-00190 and "National DRG Validation 
Study Update: Technical Report" OEI-12-89-00191 
DIG Recommendation 
The Peer Review Organizations (PROs) should continue their surveilance 
hospital coding for DRG reimbursement accuracy. 
HCF A Response

We agree. The PROs wil continue their surveilance of hospital coding of 
diagnostic related group (DRG) reimbursement accuracy. 
We are pleased to note the decrease in DRG coding 
errors identifed by the 
1988 study, as compared to the 1985 study. We believe this improvement may 
to a combination of factors including increased hospital 
be  attributed 
experience under the prospective payment system (PPS), the impact of PRO 
review and the effect of HCFA educational efforts to enhance coding accuracy. 
General Comments 
Summary Report. OEI-12-89-00190 
This report includes a brief but excellent description of the payment process 
Three tyes of errors are defined at each stage of 
including the role of  coding. 
the payment process. However, no statement is made as to whether or not the 
errors are mutually exclusive, and if the 361 records with errors contained only 
1 of each tye. We believe this information should be included in the final 
report. 
Although the representativeness of the sample is thoroughly addressed
, there is 
no discussion as to how adequately each DRG is represented. 
We would like 
to know how the distribution by DRG of the sample relates to 
the universe of 
DRGs, and if any correlation exists between high volume DRGs and errors in 
the sample. Also, we note that most hospitals contributed only one discharge
to the sample. We question whether it is statistically valid for 
1 record in 1 hospital to represent 3 300 other records in the total population 
of records.

It is not made clear in the report how payment projections are calculated. The 
amount used to estimate payment is not stated, although mention is made  , '
.I 
Page 2

of using a rate of $3 118 for metropolitan hospitals and $2 637 for 
non metropolitan hospitals (page 29, Technical Report). It is stated that these 
are "current dollars " but there is no discussion as to the actual figures used to 
ptoject payment amounts. While the calculation for case-mix index (CMI) is 
clearly defined, as is the DRG difference before and after coding, it is not 
, clear how the payment amount is determined. The final report should include 
a more complete descrption of payment calculation. 
Executive Summary, page ii - "DRG coding errors, overall, no longer over-
reimburse hospitals." The figure for under-reimbursements is stated to be 
588.0 bilion." The correct figure is $2,588 millon (also Executive

Summary, page ii, Technical Report).

Findings  pages 5, 9 - While it is unwise to ignore magnitudes and only pay 
attention to statistical significance, we do not believe effects that are not 
statisticalIy significant should be highlighted. For example, the report notes 
that for-profit hospitals "over-reimburse themselves" but that "these results did 
not attain statistical signifcance" (also CMI discussion, pages 13, 14, Technical 
Report). 
Findings, page 6 - The narrative incorrectly states that the overall CMI 
decreased after the study s recoding. For hospitals to have underpaid 
themselves, the CMI would have to increase after recoding, as is correctly 
stated on page 13 of the Technical Report. 
Findings, page 6, Figure 6 - The 1988 pie chart does not match the numbers 
the text immediately below the chart. The resequenced and miscoded pieces 
of the pie do not represent the 27 percent and 9 percent respective figures and 
may have been reversed in the pie chart. 
Technical Report 
Introduction, page 1, paragraph 3 - The original number of DRGs in 1983 was 
470, not 476. 
Introduction, page 1, paragraph 4 - The narrative implies that there is a fiscal 
intermediary for each State. The sentence should be rewrtten to state: " 
fiscal intermediary receives the hospital bils for each State."  ! ' . . .
.. 
Page 3

Page 2, paragraph 3, under the heading "The HCF A . . . made the following 
points in commenting on the 1985 Study" - It was HCF A' s intent to point out 
that the 1985 study was published in November 1987, and conclusions set forth 
in that report were based in part on 
information and coding conventions which 
had been updated by the time the report ,was released. The phrasing " 
needed a longer learning period to adjust to the new payment and quality 
monitoring system" does not encompass the concept that changes have been 
made to the whole International Classification of Diseases, 9th Edition, Clinical 
ModificationlProspective Payment SystemlDRG system since its conception. 
Such changes and improvements continue to be made. 
Page 4 - We do not believe the data support some of the assertions and 
language which the report uses. For example, the report describes the findings 
of the earlier OIG report as showing "intentional ' gaming ' or manipulating (of 
the) coding process" by hospitals. The note cites unrelated sources while the 
earlier report used much more restrained language. 
The report consistently characterizes coding disagreements as provider "errors. 
A recent Rand study used charts re-abstracted at SuperPRO to examine the 
1987-88 increase in the Medicare CM!. Rand estimated that changes in coding 
common to SuperPRO and hospitals accounted for almost one-third of the 
1987-88 CMI increase. Since SuperPRO coders have no incentive to upcode 
this probably reflects changes in explicit and implicit coding rules between 
original coding (1987) and recoding (1988). While the CMI may increase 
payments to hospitals will not. However, disagreements which may reflect the 
evolution of coding practice over time are not strictly provider "errors." Rand 
and OIG studied slightly different problems, and the Rand finding may be ' 
unique to 1988. However, since American Medical Records Association staff 
should be able to provide insight on this matter, we recommend that OIG 
explore and report on the question of whether the increase in the CMI is 
attributable to provider errors or the evolution of coding practices. 
Page 4, paragraph 3 - "Each year, the relative weights change to reflect 
alterations in resource consumption, DRG title, coding and. . . ." The change 
in relative weights is not related to the title of the DRG.  .r. r

Page 4

Page 10 - The report does not find net overpayment due to coding error. 
Inclusion of a table of DRGs with "maxmum savings potential (to Medicare)" 
due to upcoding, without a paraJIel table of DRGs with maxmum potential 
payment increase, gives an impression of lack of balance. Financial impact 
(savings) estimates could be presented in the context of the argument that 
while ". . . trends to over-pay and under-pay approximately offset each other 
this equilbrium may not continue in the "future." 
Page 12 - OIG reports that coding errors (incidence of coding error capable 
influencing DRG assignment) declined significantly between 1985 and 1988. 
While true, the report overstates the change. The 1985 report used a two-
stage sampling design and reported error rates for the nation  8 percent 
of 20.
(hospital-weighted) and 18.6 percent (case-weighted). The update report finds 
7 percent but compares it to the 1985 hospital-weighted 
a 1988 error rate of 14.
statistic. The  , based on a simple random sample of cases
1988 value , is 
statistic and should not be compared with a 
equivalent to a case-weighted 
hospital-weighted value. 
Page 15 - Some PPS details in the draft report are obscured. For example, the 
report discusses "base payment amounts" when it means standardized amounts. 
Discussion of reimbursement effects states that financial impacts "paraJIeled 
CMI changes" when they are simply calculated from and are 10gicaJIy 
equivalent to such changes.

Appendix 5 - There appears to be an error for hospitals with 300 or more 
beds. The response rate of 66.8 percent should be changed to 48.9 percent 
199 responses of 2 451). 
There is some disagreement between the narrative and the appendices. Table 
9 ilustrates the number of records per DRG and the number miscoded. 
Appendix 21 lists each DRG with the number of records selected and the 
error frequency and the proportion of errors. While the number of records 
selected is consistent between the table and the appendix the number of 
errors is not. There is no indication why these numbers differ, the source of 
this data, or which is correct. However, the errors reported in appendix 21 do 
total 361, the correct total for the study.