Case Study: Coding Accuracy
Source: United States Department of Health and Human Services. (1999). Monitoring the accuracy of hospital 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.
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 |
||||
| 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, 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 who 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.
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.
CASE STUDY QUESTIONS
- Identify and describe the components, process, and impact of MS-DRG on the healthcare systems related to the coding and billing process in a healthcare facility.
- Identify and describe the function and structure the coding process.
- Why is it important for healthcare administrators to be very familiar with the coding and billing process and ensure their facilities processes are accurate and reliable?
- Regarding the case study above, what should HCFA do moving forward with regards to monitoring DRG upcoding?
