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 

. sanctions, or civil money penalties. The 01 also oversees State Medicaid fraud control 

units which investigate and prosecute fraud and patient abuse in the Medicaid 

program. 

OFFCE OF EVALUATION AN 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 inspection reabstracted the International Classification of Diseases, 9th Edition 

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 nonsignificant $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 underreimbursements 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 overreimbursed 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 overreimbursement 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 underreimbursed. 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 ICDcodes 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. 

AGENCY COMM 

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


EXECUTIVE SUMY. 

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. 

ORIGIN OF TH PREEN STUY 

Since 1965, Medicare has provided hospital and medical insurance to the nation 

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 

reasonable-cost basis to a prospective-payment system (PPS).3 Under the PPS 

hospital payments depended upon the patient s diseases and procedures as defined by 

the International Classification of Diseases, 9th Edition, Clinical Modification (lCDCM).4 The PPS "grouped" various combinations of the approximately 10 000 ICDCM 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.


Department of Health and Human Servces (HHS).7 Following this statutory


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 specifcation of narrative diagnoses and hospital bilg 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 followup, 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. 

The AM 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' 

reabstraction, the AM 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). 

Finally, AM entered its reabstracted codes into a personal computer database 

provided by the OIG. A series of reliabilty checks verified the reproducibilty and 

accuracy of the AM 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 

MeII r= MeII 

No r: Pr Nopl 

Nome Gome 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


DRG CODING ERROR REUCE 

Of the sample discharges, 14.7 percent 

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 

error of 0.7 indicated this point estimate 

to be quite precise, a secondary effect of 

the sample size. (Figure 3). 

No hospital demographic characteristics 

had a statistically significant effect upon 

the proportion of DRG coding errors. 

Smaller hospitals had higher proportions 

of miscodes, but this apparent difference 

did not attain statistical signifcance. 

Percnt 

198 

198 

Figue 3: Proportion of discharges with 

DRG coding errors, 1985 and 1988 

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. 

DRG CR ELIATED 

The 1988 DRG errors divided evenly 

between errors that had over-reimbursed 

the hospital (50.7 percent) and underreimbursed 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 underreimbursed the hospital occurred across 

all hospital characteristics. For-profit 

, hospitals over-reimbursed themselves 

more than did other tyes of hospitals

but these differences did not attain 

Percnt Hospitals 

over.relmburs 

under-reimbursd 

1985 198 

Figue 4: Direction of DRG coding errors 

1985 and 1988

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 slightly less reimbursement for itself than it should have 

received. (Figure 5). 

/ MIPECIFCATION UNER-REIMURSES WH REEQUENCING . OVE-REIMURSES 

Each stage of the reimbursement Reason


process introduced coding errors for a


different reason. The OIG identified Mis-specification


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 ICDCM 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


significantly over-reimbursed them. Hos


Thus, the attending 100 ov-rburs physicians selected 18-r
 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


reimbursement than their employers -100MIwMIs 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 overreimbursement due to resequencing, approximately offset each other; so that overall 

hospitals received correct reimbursement. Whether this equilbrium will continue is 

unknown. 

CETAI DRGS AR 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), 
DRG description n (%) 

127 Heart failure and shock 133 (5.4) 

140 Angina pectoris 89 (3. 14 Specific cerebrovascular disorders except TIA 75 (3. 89 Simple pneumonia 74 (3. 

182 Esophagitis, gastrointestinal, and miscellaneous digestive disorders 66 (2.
 96 Bronchitis and asthma with complications 63 (2.


209 Major joint procedure 55 (2. 15 Transient ischemic attacks 47 (1.9)

138 Cardiac arrhythmia and conduction disorders 45 (1.

296 Nutritional and miscellaneous metabolic disorders with Other 1
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 overreimbursed 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 

/ $842.2 milion. 
RECOMMENDATIONS


~ The Peer Review Orgations 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 underreimbursed. 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 ICDcodes 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. 

AGENCY COMM 

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. 

Toula Nicholas, AR. 

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 

lt: "OWIG 

Date 

From 

Subject 

Health Care 

DEPARTMENT OF HEALTH & HUMAN SERVICES Financing Administration 

Memorandum 

MAY 2 9 


Wilam Toby; Jr. 

Actig Administrator 

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 

We have reviewed the above-referenced dr report which are 

updates to a previous OIG study which re-abstrctd the International 

Clasifcation of Diseaes, 9th Edtion, Cliica Modcation coes from a 

saple of 1985 Medicare discharges. These report update the previous study 

by: replicating the 1985 work on cog accuracy using 1988 data comparg 

the findings from 1985 and 1988, and identig pattrns of cog errors that 

the Prospectie Payment System (PPS) changes could mod. 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 

1985 Par A bil contaed cog errors that changed the dignosc related 

group (DRG) and that 61.7 percent of thes errors over-reimbursd the 

hospitals. This improper DRG cog incread tota PPS diurments 

9 percent or $308 m.on. Durig the 1988 study, OIG found that 

14.7 percent of the diharges hadDRG cog 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 

research and evaluation of cae mi 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 be attributed to a combination of factors including increased hospital 

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 including the role of coding. Three tyes of errors are defined at each stage of 

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 

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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 overreimburse 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 twostage sampling design and reported error rates for the nation of 20.8 percent 

(hospital-weighted) and 18.6 percent (case-weighted). The update report finds 

a 1988 error rate of 14.7 percent but compares it to the 1985 hospital-weighted 

statistic. The 1988 value, based on a simple random sample of cases, is 

equivalent to a case-weighted statistic and should not be compared with a 

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.