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NATIONAL DRG VALIDATION STUDY

SPECIAL REPORT ON CODING ACCURACY

Si.V'(".r. 
OFFICE OF INS.PECTOR GENERAL

OFFICE OF ANAL VSIS AND INSPECTIONS 
FEBRUARY 1988  The Office of Inspector General

The mission of the Office of Inspector General (OIG) is to

promote the efficiency, effectiveness and integrity of programs

in the United states Department of Health and Human Services

(HHS). It does this by developing methods to detect and prevent
fraud, waste and abuse. 
Created by statute in 1976, the
Inspector General keeps both the Secretary and the Congress fully
and currently informed about program or management problems and 
action. -
recommends corrective  The  OIG performs "its mission
conducting audits 
, investigations and inspections with 
approximately 1;200 staff strategically located around the
country. 
Office of Ana1ysis and Inspections 

This special report is produced by the Office of Analysis and
Inspections (OAI), one of the three major offices wi 
thin the OIG. 
The other two are the Office of Audit and the Office of 
Investigations. The OAI conducts inspections which are typically 
short-term studies designed to determine program effectiveness 
efficiency and vulnerability to fraud and abuse. 
This Report

Enti t1ed "National DRG Validation Study - Special Report on
Coding Accuracy, " this study was conducted to determine the 
extent of coding errors across a random sample of all hospitals
System. 
paid under the Prospective PaYment  The report was
prepared by the fOllowing staff of the Office of Analysis and
Inspections: 
Headquarters  Region I 
Catha1een A. Ahern  Joyce M. Greenleaf, M.
David C. Hsia, J.  Cindy A. Pi1 tch 
, M. 
, M. P. H. 
Wm. Mark Krushat, M.

Brian M. Rawdon  Region V
Barry L. Steeley 
Jane C. Tebbutt, M. S.  Natalie A. Coen 
Nicole Bass  Suzanne G. Johnson 
Patti Snyder  Margaret Shell 
Region IX 
Kathy L. Admire

Leonard Czajka

Kaye D. Kidwell, M.
 NATIONAL DRG VALIDATION STUDY

SPECIAL REPORT ON CODING ACCURACY

RICHARD P. KUSSEROW

INSPECTOR GENERAL

OAI-12-88-0l0l0 
FEBRUARY 1988  EXECUTIVE SUMRY 
The Office of Inspector General (OIG) studied the accuracy

wi th which hospitals reported the diagnoses and procedures

performed on Medicare beneficiaries under the prospective

paYment system (PPS). A two-stage cluster design sampled

size. 
050 medical records from 239 hospitals, stratified by 
Medical records specialists reabstracted the information in 
the medical record for each case to arrive at the correct 
diagnosis-related group (DRG), on which paYment is based. 
The wrong DRG was originally assigned, based on hospital

8 percent of the cases reviewed. 
coding, in 20. Smaller

hospi tals made significantly more errors than mid-size and

large hospitals. A significant percentage of the errors

(over 61 percent) favored the hospitals, that is, the

- hospitals 
were paid more for the hospital stay than they 
would have been if the correct codes had been submitted. 
Previous studies had found the direction of errors to be 
random , overpaying and underpaying the hospitals about
equally. 
Most errors (48 percent) derived from physician designations

of diagnoses or procedures, which, although incorrect or

insupportable 
, were not corrected before a claim for payment 
was submitted. Other causes of these errors include

incorrect sequencing of diagnoses or codes (27 percent) and

percent). 
incorrect coding (12 
These errors caused an overall 1. 3 percent overpaYment in the

sample, which, if projected to all PPS hospitals, would

represent $308 million in excess paYments in Fiscal Year

1985.  ..  ..
TABLE OF CONTENTS

. i

EXECUTIVE SUMRY. . 
INTRODUCTION 
. 1

Origin of the Prospective PaYment System 
. 1

DRG Implementation 
. 2

Background 
. 5

Methodology. . 
. 5

FINDINGS 
. 8

CONCLUSIONS. .

APPENDIX
 g . 
INTRODUCTION 
From the inception of Medicare in 1965 until 1983, hospitals had 
been paid on a reasonable cost basis for caring for Medicare 
beneficiaries. They were reimbursed for their expenditures, 
based on cost reports submitted annually. The more services a 
hospital rendered, and the more those services cost, the more the 
hospital received in paYment from Medicare. Third-party payers 
including Medicare, tended to isolate both the patient and the 
provider of care from the effects of steadily increasing hospital 
expendi tures. Many of these increases were due to new techniques 
and procedures, with life-saving and life-enhancing consequences. 
Others were due, however, to a system of paYment which rewarded 
inefficiency. As medical expenditures outpaced general inflation
rates 
, various limits were put on the amounts that would be 
reimbursed, but the cost-based methodology remained the .same. 
ORIGIN OF THE PROSPECTIVE PAYMENT SYSTEM

Looking for ways to reverse the trend toward higher health care 
expendi tures, the Health Care Financing Administration (HCFA) 
funded various studies, one of which resulted in the diagnosis-

related group (DRG) system of classification. Robert B. Fetter

John D. Thompson and their colleagues at Yale University s Center

for Health Studies developed DRGs as a framework to monitor 
settings.
quali ty of care and utilization review in institutional 
Their original goal was to define a "case " in terms of resource
consumption. 
These DRG categories would have to represent 
clinically coherent groups of patients with similar patterns of 
resource use. Since treatment, as a rule, is based on diagnosis 
point. 
this became the logical starting  More than a third of a 
million hospital records were abstracted to analyze the patients 
condi tion, age, the presence or absence of complicating
illnesses 
, the length of stay and the treatment rendered. The 
information was analyzed by statistical means, with length of 
stay used as a surrogate for resource consumption, but decisions 
were made by physicians as to the clinical relationships 
identified. The resulting DRG categories, which were refined 
several times during development and field testing, represent 
groups of patients who can be statistically shown to require, on 
, similar levels of care. 
the average Not every instance of a 
given DRG would require the same expenditure of hospital 
resources as the next of that DRG, but on the average, DRGs can
be ranked based on the "
weight" given to the intensity of care 
required by patients in that DRG. In 1983, there were 467 DRGs
based on diagnosis 
, and 1 DRG which represented instances in 
which the surgical procedure performed was not related to the 
, a patient admitted for respiratory 
principal diagnosis  (e. 
problems who slipped and broke a bone, which was then surgically
repaired) .  On October 1, 1983, HCFA' implemented a wholly new approach to 
paying hospitals for the inpatient care of Medicare

beneficiaries. The Congress had mandated a prospective payment

system 
(PPS) based on DRGs, with rates for each DRG determined
prospectively. 
The PPS is responsible for reimbursement for Medicare patients at 
nationwide. 
more than 5, 400  hospi tals  Another 2, 000 hospitals or 
PPS. 
special ty wings of hospitals are exempt from  These include 
those specializing in the treatment of psychiatric disorders

physical rehabilitation and, until recently, drug and alcohol

treatment. 
Wi th the change to PPS, hospitals receive a pre-established

paYment for each discharge based upon its DRG. As described
above , the DRG paYment represents an average cost for patients 
h(ai.v ing similar diagnoses. Some discharges consume morlee ssesr.v ices

e., cost more) than the paYment, while others use  The 
hospi tal retains any surplus from discharges costing less than

their DRG paYments and absorbs losses on those consuming more

services than the paYment. 
An assumption underlying PPS is that 
a fixed paYment per discharge would induce hospitals to implement 
economies and reduce the unnecessary services previously 
associated with the retrospective reimbursement system. At the
same time , the total payments to the hospital should provide
, on
the average, the same resources for its patients as the cost-
based system. 
The PPS also simplified reimbursement administration and program
control. 
For example, the case-mix index (CMI) describes, in a
single measure, the complexity of a hospital' s discharges

because it represents a weighted average of all the DRGs treated

at that hospital. Hospitals that attract a sicker patient
population
, and, therefore, need to provide a greater degree of 
care 
, have higher CMIs. The PPS adjusts reimbursement to 
compensate for the more complicated patient mix. The CMI also

permi ts administrators to monitor health care delivery, at least

to the Medicare population, via previously unavailable

comparisons both between hospitals and of a single institution

over time. 
DRG IMPLEMENTATION

Implementation of PPS depended upon accurate coding using the

International Classification of Diseases, Ninth Revision 
Clinical Modification (ICD-9-CM). This three-volume reference 
uses a five-digit code to classify approximately 10 000 diseases 
by anatomic and pathologic taxonomy, and a four-digit code to 
describe about 1 080 types of diagnostic and therapeutic 
procedures. The National Center for Health Statistics originally  sponsored the development of ICD-9-CM to automate the collection

of morbidity and mortality statistics for epidemiological

research. 
The PPS radically changed ICD-9-CM' s use from research to claims 
processing. Two areas of confusion were created with this new 
application. The first is that ICD-9-CM, which was created for 
research purposes, is used in the construction of DRGs. The 
second is that DRGs were initially created for reviews of quality 
and utilization. This has inevitably led to difficulties when 
used for paYment purposes. Formerly, the selection of one 
diagnosis or code over another would have little effect on the 
mass of data co11ected for epidemiological or other -research 
purposes. Now these , choices have an impact that can affect not 
only individual hospitals but the yearly Medicare inpatient 
expendi ture of $27 billion. The Office of Inspector General
( OIG) has issued a number of individual reports focusing on
coding problems wi thin specific disease entities. This study 
examines coding errors across a random sample of all hospitals 
paid under PPS. 
The HCFA calculated "relative weights" for 468 DRGs from 
data. 
historical Medicare cost and charge  The relative weights 
came from a 20 percent sample of 1981 Medicare cases from each 
reports. 
participating hospital and matching cost  Thus Medicare 
weights represent the actual care rendered to Medicare patients.
Weights could be different for other p,o peutlcat.i ons because of 
their varying age, sex, health status At the inception of
PPS , the relative weights theoretically averaged 1. 0000 (after
adj ustments for DRG frequency). Those DRGs consuming more 
resources have higher relative weights. For instance , a cardiac
val ve procedure with 
pump and cardiac catheterization (DRG 104)
bore a relative weight of 6. 
8527 , whereas malignancy aftercare 
1983. In this 
(DRG 465) carried a relative weight of 0. 2071 in 
report 
, we discuss our findings in terms of the weights in use 
, the period under review. 
during Fiscal Year (FY) 1985 The 
relative weights have changed each year in response to changes in
hospitals ' practices. 
For example, if most simple cases of a 
given procedure are now performed in an outpatient setting, the
relative weight for the remaining (more complex) inpatient cases
will increase. 
Conversely, a very complex procedure may have 
become less time- or resource-intensive due to advancing
technology, and now have a lower re1 ati ve weight. 
relative 
A hospital' s paYment depends on the product of the DRG' s 
weight times the hospital' s blended paYment rate, which takes 
costs. 
into account the hospital' s specific historical  The 
blended rate in effect in FY 85 provided for a hospital' 
historic costs to represent 50 percent of its rate, while the 
federal portion was based 75 percent on regional rates  (recognizing varying market-rbaatsekest.  costs, area wage levels, etc.

and 25 percent on national  The blended paYment rate will 
be phased out as PPS is fully implementedra, taend.  most hospitals

will be paid based on the same national  Adjustments are
made to the blended paYment rate for area wage levels
, urban

versus rural locations, indirect medical education and other

factors. 
Addi tional paYments can be made for discharges which are
statistical "outliers, 
" requiring much longer than avercaoge sts. 
lengths of stay or incurring much higher than average 
Some types of hospitals, such as sole community providers, rural

creferoral csentetrs sand. th ose which treat a disproportionate share

of Medicare and Medicaid patients 
, receive supplemental paYments 
of various sorts. Yet other costs are "pass-throughs 
" at least
temporarily. 
These include direct medical education and capital
The HCFA calculates standardized amounts, which represent the
cost of an average Medicare discharge (i. e. , the paYment for a
case having a relative weight of 1. 0000). In 1985, the national
standardized amount averaged $2 972 in urban areas and $2 358 in
rural areas. 
Congress periodically modifies the standardized 
amount to reflect changes in the costs of delivery of health care 
or for other reasons it judges appropriate. 
Processing a Medicare claim for paYment commences with the
patient' 
s discharge from the" hospital , so cases under PPS are
frequently referred to as " discharges, 
" but also include 
patients who die, are transferred or leave the hospital against
medical advice. At the time of discharge, the attending
physician (1) lists the principal diagnosis, secondary diagnoses
and any inpatient procedures on the front of the chart; and (2)
signs an attestation certifying the correctness of these 
statements. The hospital then assigns ICD-9-CM codes to all 
diagnoses and procedures for each discharge, using the rules of 
the Uniform Hospital Discharge Data Set (UHDDS) and the coding
conventions known to Accredited Record Technicians (ARTs) and
Registered Record Administrators (RRAs), the professional 
personnel trained in management of medical records and use of
coding systems. These codes are shown on the " 
face sheet" of the 
medical record and on the claim for paYment from Medicare. 
Local fiscal intermediaries (FIs) contract with HCFA to process

these codes as part of the hospital claim. The FI applies the
Medicare Code Editor, a program which checks for valid codes 
, and coverage by Medicare. 
coherent data Claims that fail these 
edi ts are returned to the hospital for correction. Using the 
hospi tal-generated codes, the FI processes each record through a

computer program, named GROUPER and mandated by HCFA, that
 DRG. In addition 
returns the appropriate  to codes, the grouping
process also considers the patient' 
s age, sex, complicating
condi tions and status at discharge. A related computer program 
named PRICER adjusts for the area wage level, hospital-specific 
historic costs, etc. , and assigns a paYment amount to the DRG. 
Background 
Wi th the change from retrospective reimbursement to prospective
paYment , the OIG began a number of studies aimed at 
provider
understanding PPS and its effects on utilization and
behavior.  provider
Among our concerns was that inappropriate 
behaviors , intended to maximize reimbursement, could place 
Medicare beneficiaries at unnecessary risk and cause 
inappropriate charges to HCFA. 
The OIG anticipated the possibility that hospitals seeking 
maximum allowable paYments might engage in impermissible coding
practices. 
This "gaming " could take the form of coding diseases 
that the patient did not have or that the attending physician had
not attested to 
, changing the order of the ICD-9-CM codes 
choosing a principal diagnosis other than the condition 
responsible for admission to the hospital, or picking a non
specific diagnosis. 
A hospital that influences the coding 
process in an improper manner to increase its revenue engages in 
a practice known as "DRG creep. 
In contrast, choosing among viable, alternate principal diagnoses

does not violate ICD-9-CM priority rules. However, this

optimization , has the 
" still potential to increase reimbursement 
and contribute to overall increased costs to the government. 
Various attempts have been made to measure the components of the 
increase in CMI among all PPS hospitals. The weighted average CMI 
for FY 84 PPS bills nationally was 1. 1389; by FY 85 , it was
1739 , and preliminary FY 86 data showed an increase to 1.2066. 
As mentioned earlier, part of this increase is due to the shift 
of some services to an outpatient setting, and to more attention 
coding. 
being paid to precise and thorough  The CMI increases may 
also be due to the advent of new, more intense treatment regimens 
elderly. 
for diseases affecting the  This inspection was designed 
to measure the increase due to inaccurate or improper coding. 
Methodology 
Based on the National DRG Validation study sample, the OIG 
compared the codes paid by Medicare with the codes supported by 
record. 
the underlying medical  Where inconsistencies occurred 
the study investigated their causes, effect on DRG assignment 
resul ting influence on reimbursement, and changes in the CMI. 
The study also measured whether systematic coding changes were 
biased in favor of either the providers or Medicare.  This study used a stratified, two-stage, cluster sampling design

based on hospitals. The first stage used simple random sampling

wi thout replacement to select 80 hospitals from each of three 
. groups based on bed size: small (fewer than 100 beds); mid-size
(100 to 299 beds); and .large (300 or more beds). The three 
cri teria that defined the hospitals in the sampling frame were: 
they were acute care, short stay facilities; they were not

located in a PPS waiver state (Massachusetts, Maryland, New

Jersey, or New York); and, they had participated in the Medicare

program since at least 1981 (and therefore had contributed to the

establishment of the DRG weights).
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quali ty control process revealed no significant discrepancies in

the reviewers ' code assignments.

The results of this recoding process, analyzed for statistical 
Statistical 
significance, form the basis of this report. 
methods are discussed in the Appendix.  Addi tional review by 
nurses and physicians identified concerns with the quality of 
PPS. 
care rendered under  Subsequent OIG reports will discuss 
these concerns in detail. 
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FINDINGS 
ERROR RATE OF 20. 8 PERCENT

The DRG assigned by the FI to the codes provided by the hospital 
was found to be incorrect in 1 374 discharges , or 20. 8 percent of
the discharges reviewed when weighted by 'the proportion of 
hospi tals in each bed size stratum. The error rate was 16. 
percent in large facilities, 18.4 percent in mid-size facilities
and 23. 6 percent in small facilities. The small hospitals had  913. 
The number of hospitals available for study was 4  Of the 
initial sample of 240 hospitals, one facility terminated its 
Medicare eligibility before the actual collection of medical 
records. Therefore, the first stage sample included 239 (4. 
percent) randomly selected hospitals meeting the three criteria 
mentioned above. The second stage of the design employed 
systematic random sampling to select 30 Medicare discharges from 
each of the 239 hospitals for the period October 1, 1984 to March
, 1985. If the hospital discharged fewer than 30 Medicare 
patients during that time, all Medicare discharges for the period 
were selected for review. 
In mid-1986, the Office of Inspector General requested copies of

the medical records for the sampled discharges. Administrative 
subpoenas compelled the cooperation of a few institutions. Of

the 222 396 records0 7av6a.i lable from the hospital sample, the

design selected 7  The reviewers ultimately received and 
reviewed 7 050 (99. 6 percent) medical records. Payment records
from the FI' s 
files documented the DRG as billed and the amount 
of payment.

The OIG then contracted with the Health Data Institute of

Lexington 
, MA, to perform DRG validation on the sample records. 
Clerks taped over the hospital ICD-9-CM codes on the face sheet

and physician attestation on all records. An ART reviewed each

chart and translated the supportable diagnoses and procedures

into ICD-9-CM codes. An RRA supervised the coding team

providing guidance on the application of coding rules and
resol ving  arose. 
coding issues as they  The official GROUPER 
program processed the codes to determine the correct 
DRG. 
Coders had instructions to ignore marginal problems or honest

differences in judgment about appropriate coding. If the newly

abstracted DRG nevertheless differed from the DRG paid by HCFA
record.  , a
member of a panel of physicians evaluated the  The 
review. If 
physician did not know the assigned codes during the 
the physician felt that the discharge record in question 
presented complex clas ification problems, a physician corni ttee
reso1 ved the issue. The physicians reviewing coding 
discrepancies had Board certification in appropriate special ties 
experience with ICD-9-CM classification, and recent patient care
responsibility. The physician s involvement was not to clarify 
coding, per se, but to identify principal diagnoses and provide 
clarification of vague or complex language used by the attesting
physician. 
The physicians were further involved in reviewing 
instances of poor quality care and unnecessary admissions
, which
reports. 
will be addressed in separate 
Five percent of the sample underwent blind re-coding by a
process. 
different ART to verify the accuracy of the  This  .....  
..". .., .. ..........  .....,.................,."..,. ... ..  . ... ... ... .... ..",. , . "':''  , p( .  , -- , p(. 
significantly more DRG assignment errors than large and mid-size 
hospitals (Wald Chi-square 39.  , 2 degrees of freedom  OOOl). 
Hospi tals in the same bed size stratum had similar rates of 
errors regardless of location (urban osrt artuurasl.) , teaching status 
for- Weighting by the 
or proprietary versus not- profi t 
proj ecting these 
proportion of discharges in each stratum and 
error rates to the 8.3 million cases paid under PPS in FY 85 
suggests 1 500, 000 discharges were paid incorrectly. 
SIGNIFICANTLY MORE ERRORS IN HOSPITAL' S FAVOR

In 61.7 percent of the discharges with DRG errors, the DRG 
paid.
assigned on recoding has a lower weight than originally 
Thus the hospital' 
s coding gave it a larger payment than it 
discharge. This differs 
should have received for the 
significantly from the 50 percent rate expected if errors were
made at random (Wald Chi-square 68. 7, 2 d. f.  0001). Previous 
studies of coding accuracy, performed prior to PPS but using DRG

systems of classification, have reported higher rates of error 
but found the direction of coding error to be random or to 
underpay the hospital at nonsignificant rates. Bed size had no 
errors. 
effect on the direction of 
DRG CHANGES BY HOSPITAL SIZE

. u..  AMPLE
RCENT 
, TOTAL , DISCHAGE HOSPITAL 
:'1ot31  137  20. 
. on .. 
:Urban '  788  20.4 
:Rural '  :586  21.3 
::Teachig . 318  19. 
Nonteacbig  :::1056  20. 
.',... n..

. Profit  ::140  21. 
Nonprofit"  :1234  20.  .. 
.. 
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--iBYHOSPITAL:SIZE . 
''PERCEN  OF SAMPLE 
:"'OTAL  DISCHAGE HOSPIT 
::NUMBEROF '  : H'
HAR 
Total  ::193. 
::::1  7.4

n'H' :: 
':R:Urub ral'  47 .  :128  ' 7.4

146 " :" 'H' .50 '  :7.
Teaching " "  :38 ,
-4 H

Nonteacbing '  189 ,. .  140 '

Nonprofit . 
Profit
 35 
143 7.4  ..... 
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PROJECTED COST SAVINGS OF $308 MILLION

The effect of these errors was to raise the case-mix index in the 
hospitals under review from 1.0720 (as corrected) to 1.0878 (as 
Consequently, the sample hospitals were overpaid by
paid) 
$294  000 (out of $22 million total payments for the sample).
This represents a potential cost savings of $308 million (1. 
percent of FY 85' s $27 billion in PPS payments) if projected to 
discharges. 
the population of PPS  These figures do not represent
costs associated with unnecessary admissions
, which will be

discussed in another OIG report.

..u.. 
::::: NET":F.lNANCIAL"EFFECT,Of:::CODINGCHAN 
HOSPITALSIZE 
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iil;iil:  308,000,000 
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PHYSICIAN' S CHOICE OF DIAGNOSIS OR PROCEDURE CAUSED 48 PERCENT OF 
ERRORS 
The physician s choice of diagnosis or procedure
, especially the

principal diagnosis, was the single most frequent reason for

incorrect payment seen in this study. In 
errors  39 percent of the
, the principal diagnosis designated by the physician was 
record. 
not supported by the medical 
The attending physician
described a patient who did not have the disease he later
attested to 
, or who had that disease but was admitted for 
treatment of another condition. 
For example, a patient might be
 , ,

discharged with a principal diagnosis of acute pancreatitis, but 
a subsequent review may not support that diagnosis. If the
record did not describe the patient' 
s complaining of pain, had 
laboratory results showing normal amylase and had no orders for 
pain medications, the reviewing physician would question whether 
the reason for admission was actually some other disease in a 
, a past history of pancreatitis. 
patient with, perhaps The 
reviewing physician would also be alerted to the possibility that 
the attending physician was not adept in the diagnosis and 
treatment of pancreatitis, and would have subj ected the chart to 
a review of the quality of care delivered as well as to a review 
diagnosis. 
of the principal 
Some errors can be particularly advantageous for the hospital 
such as a chronic condition shown as the principal diagnosis 
when in reality a patient with that chronic condition was 
condition. 
admi tted for treatment of an acute  Chronic 
condi tions such as cirrhosis, emphysema and arteriosclerosis are 
weighted relatively highly (1. 1841 , 1. 0304 and 0. 9392) when 
compared to those conditions secondary to a principal diagnosis 
for example, acute gastroenteritis (0. 6121 and 0. 5593, 
depending on whether the chronic disorder is classified as a 
complicating condition). This represents a difference in 
payment of $1 000-$1  300. 
The UHDDS definition of principal diagnosis is "the condition 
established after study to be chiefly responsible for 
occasioning the admission of the patient to the hospital for 
it. 
care. " Errors result when physicians are not cognizant of this
rule 
, or are inconsistent in applying  In such instances, it 
is the responsibility of the medical record professional, after 
an analysis of the entire chart, to inform the physician that the 
principal diagnosis he or she selected does not conform to UHDDS 
guidelines. Thus, although the original error is the
physician 
, it is compounded by a coder who is poorly trained 
and unaware of the coder s responsibility, aware of it but afraid 
or unwilling to confront the physician, or under such pressure to 
complete coding so that a bill may be issued that the decision is 
procedure. In 
made not to follow proper  some hospitals, coding 
chart. 
is done from the face sheet only, not from the entire 
This can ensure that errors in the identification of principal 
diagnoses will result in erroneous claims for payment, since no 
record. 
independent review is given to the underlying medical 
The addition or deletion of a secondary diagnosis can affect a 
hospi tal' s reimbursement if the secondary diagnosis is a 
complicating condition. These conditions were determined during 
the development of DRGs to have a strong likelihood of increasing
the patient' s length of stay by at least one day. Thus, an 
costs. 
increase in payment acknowledges an increase in hospital  (In a few DRGs, a particular secondary code can lead to lower
reimbursement.  Similarly, the addition or deletion of an 
operative procedure can cause the DRG to change. As a rule 
surgical DRGs are weighted more highly than medical DRGs to 
reflect the additional resources involved in performing surgery. 
INCORRECT SEQUENCING OF CODES CAUSED 27 PERCENT OF ERRORS

More than a quarter of the errors were due to showing the disease 
enti ty that was actually responsible for the admission in the 
list of narrative diagnoses, but not as the principal diagnosis. 
In other instances, the codes, which correctly described correct 
narratives, were renumbered to create an incorrect claim. 
To illustrate these errors of both coding and sequencing, assume 
a patient with chronic lungt idsis.e ase entered the hospital for 
treatment of acute bronchi  This would be correctly shown as: 
tis 
Acute bronchi  466. 
COPD  496 
and would group to DRG 96, with a weight of 0. 7996. 
Incorrect sequencing by the physician would show the acute
condi tion which actually necessitated the admission as a
secondary diagnosis: 
COPD  496 
tis 
Acute bronchi  466. 
which would group to DRG 88, with a weight of 1. 0412. 
Leaving. the narrative diagnoses in correct order, but submitting 
codes in the wrong order would also result in incorrect payment 
of DRG 88: 
tis 
Acute bronchi  466. 
COPD  (f 496 
Coding acute bronchitis as chronic would also have the effect of 
causing this example to group to DRG 88: 
tis 
Acute bronchi 
491. 2 
COPD  496 
IN l2 PERCENT OF ERRORS, AN INCORRECT CODE WAS SELECTED

The narrative diagnoses and procedures written or dictated by

the physician are coded using ICD-9-CM, the three-volume work

described earlier. The coder should use Volume 2, the diseases
 index 
, as the first step in assigning a code, and Volume 1, the
tabular list, as the second step. Frequently, however, a coder

feels so familiar with the coding system that he or she assigns

codes from memory or by turning to what is assumed to be the

correct section of Volume 1, which contains over 1 000 pages of
order. 
codes in numerical  This can lead the coder to assign an 
incorrect code, either through failing to properly identify the

disease or the stage of the disease referred to by the physician

or by assuming that the familiar code is the most descriptive in

a given situation. Sometimes a physician may use language which

is unfamiliar to the coder, who assumes an incorrect code

describes the disease. Because of the intricacies of medical

nomenclature and the nature of handwritten notes, errors such as

reading 
" emphysema " for " empyema " can occur. 
Some of these errors were made because coding rules or

followed. 
conventions were not  These are technical procedures 
which are shown in ICD-9-CM as "excludes " notes or as specific
instructions 
, such as "do not code when ' x ' is present " or 
rules of coding such as not coding symptoms when the disease

responsible for them has been diagnosed, coding of diseases that

have or have not been " 
ruled out " etc. Al though some issues of
classification will emerge from time to time, for the most part

these coding conventions are well known. 
Coding rules exist as well to govern when a cancelled procedure

may be coded as performed. For example, if the abdomen was 
opened to perform gallbladder surgery, but the patient'
heartbeat became so irregular that the wound was closed without

the procedure s being done, the code for exploratory laparotomy

is appropriately used. If, however, the irregularity caused the

surgery to be cancelled while the patient was still in the

holding area, no procedure may be coded.

OTHER ERRORS RESPONSIBLE FOR 13 PERCENT OF CHANGES

Among other causes of errors was physicians ' use of vague or non
language. 
specific diagnostic  This may also represent an 
inadequate diagnostic workup, where not enough is determined
about the patient' 
s condition to allow a more precise diagnosis. 
A few of the erroneous discharges represent non-documentation of
the patient' 
s condition and treatment while in the hospital. 
That is, if the physician had adequately described the patient' 
status and treatment, the reviewing physician might have agreed 
wi th the principal diagnosis.

Transcription and transposition errors, including key entry
errors 
, also caused errors in DRG assignment, as did coding
records with multiple attestations, coding from incomplete  , "

records (e. 
, those lacking definitive laboratory results
recei ved after the patient was discharged), and showing an 
incorrect patient age or discharge destination.

CONCLUSIONS 
Under PPS, assignment of diagnoses and codes assumes new
importance. 
Previously, payment had very little to do with any 
it. 
factor but the patient' s length of stay and/or the costs
associated with 
Hospi tals may have responded to these new 
reimbursement factors by devoting greater attention to the 
accuracy of coding, which would explain the lower overall rate of 
studies. 
error found in this study compared to earlier 
The direction of the error, however, may confirm that some coding 
choices are being made with the intent to increase reimbursement. 
above. It 
The opportunities to do this are many, as discussed 
was not wi thin the scope of review of this study to determine a 
hospi tal' s "motive " in coding a higher-weighted DRG than 
this 
supported by the medical record. However, the resu1 ts of 
study appear to confirm that hospitals are coding records in a 
way that results in a higher payment than is justified by the 
medical record. This up-coding resulted in a 1. 3 percent 
increase in the CMI in this study. 
The finding that small hospitals code in error 23. 6 percent of 
their discharges may indicate the relative lack of resources at 
these hospitals. These resources would include experienced 
well-trained coders, consulting physicians who could provide more 
specific diagnoses and sophisticated software which would allow 
moni toring of coding accuracy and adequacy of record
documentation. This 
suggests that further review and educational 
efforts should be directed towards these hospitals. 
The pressures on hospitals under PPS should provide an incentive 
care. At 
to provide cost-effective  the same time, remaining 
competi tive in the marketplace requires a reputation for 
providing high quality care. Thus a balance should be maintained 
in which quality of care would not be sacrificed for decreased
costs. If 
this is true, hospitals would look to other means for 
revenues. 
increasing operating  Among the measures hospitals 
could take would be more streamlined scheduling of admissions and 
surgery; cost-benefit analyses of new purchases, particularly of 
state-of-the-art" equipment; and tracking of the costs incurred
by individual patients 
, compared to the anticipated DRG of that
patient. 
Sophisticated software packages have been developed 
which allow precisely this level of integration of financial and
clinical data. 
The results of this study suggest that in
addi tion to legitimate optimization of codes and thus DRGs  DRG 
creep " may be a by-product of this new attention to coding and
costs.  APPENDIX 
statistical Analysis

Because of the two-stage sample design, this report evaluated its
data by hospitals rather than by discharges. 
It ca1qulated 
proportions of events as the number of events over the total
number of discharges reviewed wi 
thin each bed size group. 
Estimates for the total sample (weighted proportions) were 
group. 
weighted by the number of hospitals in each  proj ections 
to the universe of discharges derived from the inverse of the 
sampling fractions with the estimates and variances calculated
accordingly. 
Post-stratification analysis followed HCFA practices for 
characteristics. 
classifying hospitals by their demographic 
Urban versus rural status depended on whether the hospital' 
location fell wi thin the boundaries of a standard metropolitan
area as defined by the Census Bureau. 
The HCFA considered a 
hospi tal to have teaching status if it has an accredited
residency program. These two characteristics warranted logi
analysis because they affected the hospital' 
s payment under PPS. 
This survey employed a generalized logi 
t analysis using weighted 
least squares for categorical data to test the effects of these 
study. 
variables on the various events measured in this  This 
procedure reweighted data to properly represent the effect of 
independent variables not controlled by the appropriate degrees 
of freedom and provided a test of the significance of the model 
parameters and the goodness of fit of the models.