DATE: January 21, 1999 

FROM: June Gibbs Brown 

Inspector General 

SUBJECT: Monitoring the Accuracy of Hospital Coding (OEI-01-98-00420) 

TO: Nancy-Ann Min DeParle 

Administrator 

Health Care Financing Administration 

Since the inception of Medicare’s prospective payment system (PPS) in 1984, the Office of 

Inspector General has played an active role in monitoring diagnosis related group (DRG) 

upcoding. In this final memorandum report, I would like to share with you some concerns that 

have evolved out of our recent work in this area. While we understand that the Health Care 

Financing Administration (HCFA) is taking steps to increase monitoring of DRG coding, we 

elaborate here on our areas of concern and offer some options that HCFA could take to 

improve monitoring and oversight of DRG coding. 

Background 

In our recent report, “Using Software to Detect Upcoding of Hospital Bills” 

(OEI-01-97-00010), we examined the ability of commercially available software to 

identify DRG upcoding through analysis of electronic claims data. We used two 

software products to identify 299 hospitals with a high suspected rate of upcoding. We 

then used accredited medical records professionals to perform a blinded DRG 

validation on a sample of over 2,600 claims from 50 of these hospitals and a control 

group of 20 hospitals. 

In the course of conducting this study, we developed serious concerns about the 

potential for abuse of the DRG system through upcoding and about HCFA’s oversight 

of the accuracy of DRG coding. Specifically we found that, although the hospital 

payment system is functioning well as a whole, the system has significant vulnerabilities 

to upcoding that can easily be avoided. We also found that, despite these 

vulnerabilities, HCFA is not performing routine, ongoing monitoring and analysis of 

DRG coding to detect problematic DRGs, hospitals, and coding situations that require 

administrative, educational, or law enforcement intervention. 

Findings 

The DRG system is vulnerable to abuse by providers who wish to increase 

reimbursement inappropriately through upcoding, particularly so within certain 

DRGs. Our analysis found noticeable, detectable, and curable upcoding abuses among 

providers and within specific DRGs. 
Page 2 - Nancy-Ann Min DeParle 

< In a focused sample from a group of 299 hospitals that computer software identified as 

high upcoders, we found that an average of 11 percent of DRG bills submitted during 

1996 were upcoded, versus 5 percent of bills among a control sample of hospitals. 

Identifying Hospitals that Upcode 

Average 

Upcoding Rate 

Average 

Downcoding Rate 

OIG Experimental sample - hospitals with 

a high predicted rate of upcoding (n=50) 11.4% 5.1% 

OIG Control sample - hospitals without a 

high predicted rate of upcoding (n=20) 5.2% 3.9% 

Source: Office of Inspector General, “Using Software to Detect Upcoding of Hospital Bills” 

(OEI-01-97-00010), August 1998. 

The average rate of upcoding in the control sample of hospitals (those without a high 

predicted rate of upcoding) was not statistically different from the average downcoding 

rate. However, among hospitals that the software predicted would have a high rate of 

upcoding, the average upcoding rate was more than twice that of downcoding. The 

difference between upcoding and downcoding in these hospitals suggests intentional 

abuse of the DRG system by some providers. 

< Using data from both our focused review and the more broadly representative 1996 

DRG validation performed by HCFA’s clinical data abstraction centers (CDAC), we 

found that certain DRGs are particularly susceptible to upcoding. 

Three Highly Vulnerable DRGs 

OIG Experimental 

Sample 

OIG Control 

Sample 

CDAC 

Sample 

Upcoded Downcoded Upcoded Downcoded Upcoded Downcoded 

DRG 79: 

Respiratory Infections 

37.7% 

(n=60) 

0.0% 

(n=0) 

18.5% 

(n=5) 

0.0% 

(n=0) 

11.0% 

(n=48) 

0.7% 

(n=3) 

DRG 416: 

Septicemia 

21.2% 

(n=14) 

0.0% 

(n=0) 

16.7% 

(n=3) 

0.0% 

(n=0) 

13.3% 

(n=49) 

1.1% 

(n=4) 

DRG 14: Specific 

Cerebrovascular 

Disorders 

10.1% 

(n=10) 

0.0% 

(n=0) 

6.7% 

(n=2) 

0.0% 

(n=0) 

3.5% 

(n=24) 

0.4% 

(n=3) 

Claims billed for these three DRGs show a clear pattern that exemplifies the upcoding seen 

in a group of over half a dozen DRGs we examined. These DRGs were upcoded 
Page 3 - Nancy-Ann Min DeParle 

disproportionately, especially by our experimentally identified upcoding hospitals, but 

also among hospitals from the general population represented by the CDAC review and 

our control sample. 

The HCFA does not routinely analyze readily available billing and clinical data 

that could be used to proactively identify problems in DRG coding. 

< The HCFA does not routinely analyze data from the annual validation of DRG coding 

performed by its Clinical Data Abstraction Centers. 

Since 1995, HCFA has used two specialized contractors called Clinical Data 

Abstraction Centers to validate the DRGs on an annual national sample of over 20,000 

claims billed to Medicare. On a monthly basis, the CDACs report detailed data on each 

claim reviewed to HCFA’s Office of Clinical Standards and Quality. These data 

include original and validated diagnostic coding, original and validated DRGs, and 

reasons for any variance between the DRGs. The purpose of this validation effort is to 

provide HCFA with insight as to the accuracy of DRGs billed to Medicare. 

However, we found that HCFA performs no routine, ongoing analysis of CDAC data. 

In our interviews with staff at the two HCFA components that have responsibility for 

DRGs – the Office of Clinical Standards and Quality, and the Center for Health Plans 

and Providers – staff were unable to identify any routine monitoring and analysis of 

CDAC data. In our review of HCFA’s instructions to the peer review organizations 

(PROs), contractors that have statutory responsibility for DRG oversight, we found no 

instructions advising them to perform regular analysis of CDAC data. 

Yet we believe that analysis of CDAC data can be of great value to HCFA in 

overseeing the accuracy of DRG coding. For example, in HCFA’s 1996 DRG 

validation, the CDACs found a 4 percent upcoding rate with estimated net 

overpayment of $183 million. Some may suggest that overpayments of $183 million in 

an $80 billion program (less than one-quarter percent) indicate that the DRG payment 

system does not have major problems with upcoding and warrants no further analysis. 

However, our analysis presented above shows that by digging below the immediate 

surface, upcoding problems are readily apparent. 

< The HCFA does not routinely analyze data from hospitals, despite the fact that these 

data are ideally suited for monitoring and analysis of DRGs. 

The HCFA maintains valuable clinical, demographic, and administrative data that form 

the underlying basis of each of the over 10 million DRG-based claims billed to 

Medicare each year. Data for each hospitalization include diagnosis codes, procedure 

codes, beneficiary demographics, admission and discharge detail, cost reporting data, 

and hospital identifier for linkage with provider demographics. Whether used on its 

own to monitor billing patterns and trends or used to further explore potential problem 

areas identified within CDAC data, data from hospital claims can provide valuable 

information to assist in HCFA’s oversight of DRG coding. 
Page 4 - Nancy-Ann Min DeParle 

However, we found that HCFA does not make routine use of data from hospital claims 

for monitoring and analysis of DRG coding. In our interviews with staff at both 

HCFA’s Office of Clinical Standards and Quality and its Center for Health Plans and 

Providers, staff were unable to identify any routine monitoring and analysis of DRG 

billing data. Interviews at HCFA’s Program Integrity unit, within the Office of 

Financial Management, revealed that HCFA conducts some limited analysis of billing 

data. However, this analysis is done on a very broad level, primarily to identify 

coverage issues. 

We also reviewed HCFA’s current instructions to the Medicare PROs. We found no 

instructions to the PROs advising them to perform any routine monitoring and analysis 

of DRG coding, despite the fact that PROs already have a complete set of inpatient 

billing data provided to them by HCFA. In fact, HCFA staff told us that the PROs 

were instructed not to do “coding projects” within their current contract. We did find 

that PROs are involved in sporadic activity around DRG oversight; however, this 

activity often is in support of an OIG investigation. 

Recommendation 

The HCFA should perform routine monitoring and analysis of hospital billing 

data and clinical data to proactively identify aberrant patterns of upcoding. 

The HCFA is taking steps toward increased monitoring for DRG upcoding. For 

example, the agency is considering an increased role for monitoring DRG upcoding 

within the next round of contracts with the Medicare PROs. We offer our 

recommendation within this context. The following approaches are examples that the 

agency may wish to consider as it develops its monitoring and analysis approach to 

detect upcoding. 

< As a starting point, the agency could routinely analyze the DRG validation data 

that it receives from the clinical data abstraction centers with which it contracts. 

< The agency could opt to organize its monitoring effort in a variety of ways. It 

might choose to use its own staff; alternatively, it might determine that the 

preferable route is to contract with an outside party with which it currently has a 

contract, such as the fiscal intermediary fraud units or the peer review 

organizations; or it might work with a new entity under the Medicare Integrity 

Program. 

< The agency could use a variety of analytical approaches, such as examining claims 

data for patterns, trends, change pairs, and spikes in DRG billing volume. This 

analysis also could include examination of coding validation data for trends in 

miscoding as a way to detect problem DRGs, providers, or coding situations. 

< The agency could establish criteria, policies, and procedures to make referral for 

collection to the Office of Inspector General or to fiscal intermediaries, as 

appropriate, from providers who are deliberately exploiting and abusing the system 

through upcoding claims for payment. 
Page 5 - Nancy-Ann Min DeParle 

Comments on the Draft Report 

The HCFA concurs with our recommendation that it perform routine monitoring and analysis 

of hospital billing and clinical data to proactively identify aberrant patterns of upcoding. In its 

comments, HCFA specified the actions that it will take to reduce DRG upcoding. These 

actions are: 

< Establish a payment error prevention program in the upcoming PRO 6th Scope of 

Work to focus on reducing inappropriate DRG payments. 

< Expand the size and content of the annual CDAC DRG validation and use it to identify 

trends in coding practices and to point out problem areas. 

< Pilot a program in which a selected Medicare fiscal intermediary will use PRO/CDAC 

data to identify aberrancies and develop cases for referral to law enforcement. 

< Procure a statistical analysis contractor to conduct statistical and trend analysis. 

We applaud the steps that HCFA will take to increase monitoring and oversight of the 

accuracy of DRG coding. We look forward to working with HCFA in the future as we 

continue our work in this area. 

The full text of HCFA’s comments on our draft report appears on the following pages. 

In Closing 

Our work in DRG coding is continuing. We are currently developing reports on specific 

DRGs to illustrate techniques that can be used to detect aberrant upcoding situations. We look 

forward to continuing our work with you to eliminate DRG upcoding from the Medicare 

program and to ensure that Medicare pays accurately for all services delivered. 

If you have any questions, you may call me or George Grob, Deputy Inspector General for 

Evaluation and Inspections, or have your staff call Mary Beth Clarke at (202) 619-2481.