Special Article
Accuracy of Diagnostic Coding for Medicare Patients under the Prospective-Payment System
N Engl J Med 1988; 318:352-355February 11, 1988DOI: 10.1056/NEJM198802113180604
- Abstract
- Abstract
Reimbursement of hospitals by Medicare under the prospective-payment system is based on patients' diagnoses as coded at discharge. During the period October 1984 through March 1985, we studied the accuracy of the coding for diagnosis-related groups (DRGs) in hospitals receiving Medicare reimbursement. We used a two-stage cluster method to sample 7050 medical records from 239 hospitals that were stratified according to size. Using blinded techniques with reliability checks, medical-record specialists reabstracted the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to assign correct DRGs to discharged patients. The correct DRGs were then compared with those originally assigned by the physician and the hospital administration.
The study revealed an error rate of 20.8 percent in DRG coding. Errors were distributed equally between physicians and hospitals. Small hospitals had significantly higher error rates. Previous studies had found that errors occurred randomly, so that half the errors benefited the hospital financially and half penalized the hospital. The present study found that a statistically significant 61.7 percent of coding errors favored the hospital. These errors caused the average hospital's case-mix index — a measure of the complexity of illness of the hospital's patients—to increase by 1.9 percent. As a result, hospitals received higher net reimbursement from Medicare than was supportable by the medical records.
We conclude that "creep" does occur in the coding of DRGs, resulting in overpayment to hospitals for patients covered by Medicare. (N Engl J Med 1988; 318: 352–5.)
Media in This Article
Table 1
Comparison of Patients in the Sample and the Underlying Population According to Age, Sex, and Average Length of Hospital Stay.Table 2
DRG Changes According to Hospital Size.Article Activity
- Article
ON October 1, 1983, Medicare changed its method of paying hospitals from a cost-based, retrospective-reimbursement system to a prospective-payment system.1 This switch to diagnosis-related groups (DRGs) gave hospitals a financial incentive to contain rising health care costs2 and altered methods of processing claims to emphasize accurate numerical coding of clinical diseases.
The Office of the Inspector General for the Department of Health and Human Services is responsible for controlling waste, fraud, and abuse in Medicare.3 Under the prospective-payment system, reimbursement of hospitals is contingent on the accurate coding of patients' diseases and procedures. This study was designed to measure the amount of incorrect coding that occurs under the prospective-payment system.
A hospital files a claim for Medicare payment when a covered patient is discharged.4 , 5 At that time, the attending physician lists the principal diagnosis, secondary diagnoses, and any inpatient procedures on the chart's face sheet, and signs an attestation certifying the correctness of these statements.6 The hospital then assigns codes to the narrative diagnoses and procedures, using the rules of the Uniform Hospital Discharge Data Set.7 Up to five diagnosis codes and three procedure codes are entered on the hospital's claim form. The Health Care Financing Administration receives these codes through its fiscal intermediaries and processes them through the GROUPER computer program to assign a DRG to the discharged patient.8 9 10 11 12 As payment, the hospital receives an amount that is the product of the relative weight for the DRG multiplied by the standardized amount, adjusted for certain hospital-specific factors.
The case-mix index serves as a measure of the overall complexity of the illnesses of a hospital's discharged patients. Simply put, a hospital's case-mix index is the average of the relative weights assigned to its patients' DRGs. Hospitals that serve populations with more complex illnesses have higher case-mix indexes. The case-mix index has a direct relation to Medicare payments.
A study by the Rand Corporation attributed one third of an estimated 8.4 percent increase in case-mix indexes between 1981 and 1984 to changes in the documentation and coding practices of physicians and hospitals.13 Such changes may take the form of either "optimization," which conforms to the rules governing coding, or "creep," which does not.14 Examples of optimization include choosing among viable alternative principal diagnoses (selection) and adding more secondary diagnoses (supplementation).15 Sources of creep include mis-specification, miscoding, and resequencing. Mis-specification is the attending physician's selection of an incorrect diagnosis as the principal one or listing of diagnoses or procedures not supported by the medical record. Miscoding means that a hospital codes diseases or procedures not attested to by the attending physician, misapplies the coding rules, or selects an unnecessarily vague diagnosis code. In resequencing, a hospital changes the order of the narrative diagnoses, substituting a secondary diagnosis for the correct principal diagnosis. DRG creep also derives from the inclusion of multiple attestations, incomplete medical records, incorrect data on patients' ages, and other causes. The hospital bears responsibility for most of these errors.
For example, the attending physician who admits a patient with pneumonia (DRG 90; current relative weight, 0.8961) may mistakenly select an unrelated postadmission myocardial infarction as the principal diagnosis (DRG 121; relative weight, 1.7162).16 The hospital worker who codes the attending physician's diagnosis of transient ischemic attack (DRG 15; relative weight, 0.6293) as a cerebrovascular event (DRG 14; relative weight, 1.2429) miscodes the discharge. The hospital staffer who switches the attending physician's attested principal diagnosis of acute bronchitis (DRG 97; relative weight, 0.7151) with a secondary diagnosis of chronic obstructive pulmonary disease (DRG 88; relative weight, 1.1263) resequences the codes.17
Implementation of the prospective-payment system depends on accurate coding according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM).18 The National Center for Health Statistics originally sponsored the development of ICD-9-CM to automate the collection of morbidity and mortality statistics for epidemiologic research. The prospective-payment system radically changes the application of ICD-9-CM from the collection of pathology data to claims payment.19 Such use of a coding system created for a different purpose inevitably leads to certain classification problems.20 Taxonomic ambiguities of ICD-9-CM coding that may have little effect on epidemiologic research can substantially change DRG classifications and, subsequently, hospital reimbursement.
Methods
The National DRG Validation Study used a stratified two-stage sampling method. Hospitals served as the unit of sampling. In the first stage, simple random sampling without replacement was used to select 80 hospitals from each of three groups of hospitals: those with less than 100 beds, those with 100 to 299 beds, and those with 300 or more.
Three criteria defined the hospitals in the sample. First, only acute care, short-stay facilities were included. Specialty institutions, such as children's hospitals, were excluded. Second, the sample excluded facilities in the states of New York, New Jersey, Massachusetts, and Maryland because a waiver provision exempted those states from the prospective-payment system as of October 1, 1983. Third, to be included in the sampling frame, a facility had to have contributed data on discharged patients to the construction of the initial relative weights assigned to DRG categories at the start of the prospective-payment system, and also had to have participated as a Medicare provider at the beginning of the prospective-payment system.
The total number of hospitals available for study was 4913. Of the initial sample of 240 hospitals, 1 terminated its Medicare eligibility between the sampling period and the actual collection of medical records. The first-stage sample therefore included 239 randomly selected facilities (4.9 percent) that provided short-term acute care, had been eligible to receive Medicare reimbursement since 1981 or before, and were not located in a waiver state. In the second stage, systematic random sampling was used to select 30 discharged patients covered by Medicare (including patients who had transferred to other hospitals or died) from each of the 239 hospitals for the period October 1, 1984, through March 31,1985. If a hospital had discharged fewer than 30 such patients during that period, the sample selected as many as were available.
In mid-1986, the Office of the Inspector General requested copies of the medical records of each of the discharged patients who had been sampled. Administrative subpoenas were required to compel the cooperation of a few institutions. Of the 222,396 records available from the 239 hospitals, 7076 were selected with our sampling method. We ultimately received and reviewed 7050 medical records (99.6 percent).
The Office of the Inspector General contracted with the Health Data Institute of Lexington, Massachusetts, to perform DRG validation of the sample records. Using blinded techniques, accredited record technicians reviewed each chart and translated the supportable diagnoses and procedures into ICD-9-CM codes. A registered record administrator supervised the coding team, providing guidance on the application of coding rules and resolving coding issues as they arose. The GROUPER computer program processed the reabstracted codes to determine the correct DRGs.
The coders were instructed to ignore marginal problems or honest differences in judgment about appropriate coding. The error rates reported in this paper are therefore conservative estimates. If the reabstracted DRG differed from the DRG paid for by the Health Care Financing Administration, a member of a panel of physicians evaluated the record. The physicians did not know the assigned codes during the review. If the physician deemed that the anomalous discharge presented complex problems of classification, a committee of physicians resolved the case. The physicians who reviewed coding discrepancies had board certification in appropriate specialties, experience with ICD-9-CM classification, and recent responsibility for patient care.
Five percent of the sample was subjected to a second, blinded recoding by a different records technician to check the accuracy of the process. This quality-control procedure revealed no significant discrepancies in the process of assigning DRGs (agreement = 0.95, kappa = 0.856, Z = 2.12).
Results
Demographics
Table 1Table 1
Comparison of Patients in the Sample and the Underlying Population According to Age, Sex, and Average Length of Hospital Stay. compares the demographic characteristics of the sample with those of the underlying population. The results show the sample to be representative of the patients discharged from sampled hospitals in terms of age distribution, sex, and average length of hospital stay. The population represented here also resembles the population of all Medicare beneficiaries, both aged and disabled, who are hospitalized each year.Coding Accuracy
Table 2Table 2
DRG Changes According to Hospital Size. shows that 1374 of the 7050 discharges of patients in this sample were affected by coding changes that altered the DRG assignment — an average of 20.8 percent when weighted by the proportion of hospitals in each size stratum. Small hospitals had significantly more DRG changes than large hospitals, with medium hospitals falling somewhere in between (Wald chi-square, 39.4, 2 df; P<0.0001). Within each stratum, all the hospitals had similar rates of DRG changes regardless of whether they were urban or rural, were teaching hospitals, or were nonprofit institutions.In 61.7 percent of the hospitals' coding changes, the corrected DRG had a lower weight than the original one. This indicated that the hospitals' coding had resulted in larger Medicare payments than should have been received for the discharged patient in question. The direction of changes differed significantly from 50 percent, or the proportion one would expect from random DRG changes (Wald chi-square, 68.7, 2 df; P<0.0001). However, the hospital's size had no significant effect on the direction of its coding changes.
The sample's case-mix index was 1.0878 before recoding and decreased to 1.0720 after recoding — an overall decline of 1.9 percent when weighted by the proportion of hospitals in each size stratum. The difference in case-mix index ranged from 0.0274 (2.8 percent) in small hospitals to 0.0080 (0.7 percent) in large hospitals. On the basis of the actual reimbursement to each hospital in our study and the changes in the case-mix index, we determined that the hospitals were overpaid a net amount of approximately $478,000. The total overpayments were approximately twice the total underpayments by Medicare.
Reasons for Coding Changes
The discharge of a patient may present multiple coding problems. To avoid counting the discharge of any patient more than once, we show the reasons for coding changes in their chronologic sequence in Table 3Table 3
Reasons for DRG Changes, According to Hospital Size.. This "exclusive" analysis recognizes only the first cause of each coding change. For instance, if a physician listed the wrong narrative diagnosis and a hospital subsequently made an unrelated ICD-9-CM error, we categorized the discharge as a mis-specification. Similarly, if a hospital coded a diagnosis incorrectly and the code order also did not follow the sequencing guidelines of the Uniform Hospital Discharge Data Set, Table 3 lists it only as a miscoding. Other causes of changes come last. A "nonexclusive" analysis that listed multiple reasons for each coding change had proportions that were basically similar to those of our exclusive analysis.Mis-specification was the most common reason for changes in coding. The physician's erroneous selection of the principal diagnosis accounted for the majority of the changes. Resequencing was the second most frequent reason for coding changes; the remainder were made either because of the incorrect assignment of codes to diagnoses or for other reasons.
Discussion
This study revealed that the average hospital has a 20.8 percent chance of coding the discharge of a patient incorrectly. Previous studies, conducted during the period 1974 to 1981 (before the introduction of prospective payment) reported error rates of 17.1,21 37.0,22 47.4,23 53.0,23 and 76.9 percent.24 The studies' methods and number of DRGs differed slightly, but the data suggest that the present study's error rate falls at the low end of the range.
More important, our study found that the DRG changes did not occur randomly. A hospital that had a coding error had a statistically significant chance of overpaying itself (61.7 percent). In contrast, the previous studies found that coding changes either were random or resulted in insignificant rates of underpayment to the hospital.
Prospective payment may contribute to the relatively low miscoding rate and to hospital overpayment. The new financial importance of coding could induce hospitals to devote more attention to its accuracy and increase the prominence of professional coders in the hospital environment. At the same time, prospective payment potentially rewards hospitals for deciding ambiguous DRG assignments in their own favor.
Mis-specification by physicians caused half the coding changes: writing the wrong narrative diagnoses on the attestation sheet, selecting an incorrect diagnosis as the principal one, or failing to support the coded diagnoses with adequate documentation. Attending physicians receive no direct financial benefit from DRG optimization, but hospitals reportedly encourage their staffs to maximize reimbursement as well as to minimize expenses.25 Hospital-controlled activities caused the remainder of the changes in DRG assignments: miscoding nonattested diagnoses, miscoding correct narrative diagnoses with incorrect ICD-9-CM numbers, and resequencing the attending physician's diagnoses. Although genuine errors occurred (in 38.3 percent of changes, hospitals underpaid themselves), the statistical significance of the proportion of overpayments to the hospital for discharged patients is evidence of a systematic trend.
As we mentioned earlier, a study by the Rand Corporation estimated that the national case-mix index increased 3 percent between 1981 and 1984 because of coding changes, both permissible and impermissible.13 The present study suggests that there was a DRG creep of approximately 2 percent during fiscal year 1985. It also indicates that the monitoring of coding accuracy should concentrate on DRGs with high rates of coding errors and on hospitals at which DRG creep is evident.
In fiscal year 1985, the Health Care Financing Administration made approximately $27 billion in prospective payments. Extrapolating our study's net case-mix changes to the full year and assigning them a statistical weight according to the number of patients discharged leads us to estimate that $300 million of this sum represents overpayments caused by coding errors. Medicare may not necessarily be able to recover all of this amount. Reabstracting all the 8 million annual prospective-payment discharges at this study's level of effort would cost about $200 million.
However, the 150 percent benefit of such a targeted reabstraction program may underestimate its potential yield. By concentrating on the DRGs and hospitals that offer the highest potential for generating overpayments, the Professional Review Organizations, which bear primary responsibility for monitoring the accuracy of coding, could attain their current cost-to-benefit ratio of approximately $3.00 in disallowances for each dollar in their budgets. In addition, the Health Care Financing Administration could check a sufficient number of discharges at random to assure the general integrity of the prospective-payment system. Common knowledge of such a program should have a deterrent effect and provide additional, although unmeasurable, savings.
The views expressed in this paper do not represent the policies of any U.S. government agency.
We are indebted to Barton McCann, Patricia Brooks, Penni St. Hilaire, and Larry Chrest of the Health Care Financing Administration, and to Annette Delaney, Matt Klionsky, Janice Walker, Gerry Bernholtz, Laurie Moore, Michele Wiese, and their staff at the Health Data Institute, for their contributions to this project.
Source Information
From the Office of Analysis and Inspection, Office of the Inspector General, Department of Health and Human Services, Baltimore, Md. Address reprint requests to Dr. Hsia at HCB/OAI, East High Rise G-10, DHHS Office of the Inspector General, 6325 Security Blvd., Baltimore, MD 21207.
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