Assng 5

patience_24

Case9.pdf

Home >Business & Finance homework help >Operations Management homework help >Assng 5

Case 9: Coding Accuracy

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 predicted rate of upcoding. We then had

accredited medical records professionals 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 the Healthcare

Financing Administration’s (HCFA) 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.

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

identified as high upcoders, we found that an average of 11% of DRG bills submitted

during 1996 were upcoded, versus 5% 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: U.S. Department of Health and Human Services, Office of

Inspector General. (1998). Using software to detect upcoding of

hospital bills (Report no. OEI-01-97-00010). Retrieved from

https://oig.hhs.gov/oei/reports/oei-01-97-00010.pdf

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 Up-

coded

Down-

coded

Up-

coded

Down-

coded

Up-

coded

Down-

coded

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 disproportionately, especially by our experimentally identified upcoding

hospitals and 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. Additionally, 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 who have statutory responsibility for DRG oversight, we found no

instructions advising them to perform regular analysis of CDAC data.

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% 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 upon 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 identifiers for linkage with provider demographics. Whether used on their

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.

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.