Articles A Study of the Relationship between Severity of Illness and Hospital Cost in New Jersey Hospitals Richard F Averill, Thomas E. McGuire, Beatrice E. Manning, Daniel A. Fowler, Susan D. Horn, Pamela S. Dickson, Molly J. Coye, David L. Knowlton, andJudith A. Bender In response to concerns over the equity of diagnosis-related group (DRG)-based prospective payment, the New Jersey Department of Health conducted a Severity of Illness evaluation study in which severity of illness, DRG, and uniform cost information were collectedfor 76, 798 patients in 25 hospitals. Severity of illness was measured using the Computerized Severity Index (CSI) and was found to be a This study was performed by 3M Health Information Systems for the New Jersey Department of Health. The Final Report contains a comprehensive discussion of the methods and results from this study. Copies of the Final Report may be obtained from Richard F. Averill or Beatrice E. Manning. Address correspondence and requests for reprints to Richard F. Averill, M.S., Research Manager, 3M Health Information Systems, 3M Health Care, 100 Barnes Road, Wallingford, CT 06492. Thomas E. McGuire, Ph.D., is Consultant, 3M Health Information Systems; Beatrice E. Manning, Ph.D., is Director of Research and Evaluation, the New Jersey Department of Health, CN 360, Trenton, NJ; Daniel A. Fowler, B.S., is Chief, Reimbursement Systems, Development and Evaluation, the New Jersey Department of Health; Susan D. Horn, Ph.D., is Senior Scientist, Intermountain Health Care, formerly Professor, Department of Health Policy and Management, Johns Hopkins University, Baltimore, MD; Pamela S. Dickson, M.B.A., is Assistant Commissioner, the New Jersey Department of Health; Molly J. Coye, M.D., M.P.H., is Director, California Department of Health Services, and was formerly Commissioner of the New Jersey Department of Health; David L. Knowlton, M.S., is Consultant, Knowlton Associates, Pennington, NJ, and was formerly Deputy Commissioner of the NewJersey Department of Health; andJudith A. Bender, Ph.D., is Post Doctoral Fellow, Department of Operations Research, Yale University, New Haven, CT. This article, submitted to Health Services Research on September 26, 1990, was revised and accepted for publication on February 13, 1992.

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significant determinant of hospital cost in 76 DRGs that accounted for 41.4 percent of the total direct hospital patient care costs and 27 percent of the patients. The addition of CSI severity levels to the 76 DRGs reduced the coefficient of variation of cost in these DRGs by 17.4 percent and improved the overall reduction in variance of cost within the 76 DRGs by 38.2 percent. The change in total hospital payments due to the addition of severity for the 76 DRGs variedfrom a positive 5. 71 percent to a negative 5.48 percent. These results demonstrate that a severity adjustment to this subset of DRGs would result in a more equitable DRGbased prospective payment system. Medicare, the states of New Jersey and New York, and several Blue Cross plans and state Medicaid programs pay hospitals based on prospective diagnosis-related group (DRG) payment rates. All DRGbased prospective payment systems establish DRG payment rates that reflect the average costs of patients in each DRG. A basic assumption underlying prospective payment by DRG is that patient costs within a DRG are homogeneous and, therefore, that an average cost-based payment formula will provide an equitable payment rate (Averill et al. 1989). However, researchers and hospitals have observed that data from many of the DRGs exhibit significant heterogeneity in resource use (Horn, Horn, and Sharkey 1984; Ament et al. 1982; Horn et al. 1986). The reason most often proposed for the heterogeneity within DRGs is that DRGs do not explicitly take into account the severity of illness of the patient. There are data indicating that some hospitals treat a disproportionately large share of patients at either higher or lower severity levels (Ament et al. 1982; Horn et al. 1986; Horn 1983; Horn et al. 1985). Hence, a DRG average cost payment formula can result in inappropriate levels of payment to certain institutions (Horn et al. 1985). The variations in DRGs due to severity of illness led Congress to require in the Omnibus Budget Reconciliation Act of 1986 that the Health Care Financing Administration evaluate the need to add a severity adjustment to the DRG structure (P.L. 99-509). In response to the growing concerns over the equity of a DRG-based prospective payment system, the New Jersey Department of Health initiated a Severity of Illness evaluation study. The primary purposes of the study were to determine whether or not patient resource use within individual DRGs varied significantly depending on the patient's severity of illness, and whether or not total hospital payments would be significantly affected by a severity adjustment to the DRGs.

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METHODS The Computerized Severity Index (CSI) was selected as the measure of patient severity for the New Jersey study because it was based on the physiological characteristics of the patient, the logic of the system was available to state researchers, and the available research on severity systems suggested that CSI showed a strong potential for being related to hospital cost (Coulton, McClish, Doremus, et al. 1986; Calore and Iezzoni 1987; Iezzoni and Moskowitz 1988; Jencks and Dobson 1987). CSI was developed by researchers at The Johns Hopkins Medical Institutions (Horn and Buckle 1989; lezzoni and Daley 1992). CSI measures severity of illness based on a patient's laboratory, radiology, and diagnostic test results; vital signs; and history and physical exam. CSI defines the diagnosis-specific patient factors that determine the severity level of each diagnosis. As an example, for pneumonia 18 different patient factors, such as white blood count, temperature, and chest x-ray findings, are considered important for determining severity. The severity level of a diagnosis is measured on a 1 to 4 scale (mild, moderate, severe, life threatening). For a pneumonia patient a fever below 100.4F is considered a level 1 severity finding, while fever in the ranges of 100.5°-102°F, 102.1°-103.9°F, and greater than 104°F are considered level 2, 3, and 4 severity findings, respectively. In order for a diagnosis to be considered at a particular severity level it must have at least two factors at that level. Thus, a high fever alone would not make a patient a level 3 pneumonia. There must also be at least one other level 3 finding present such as a chest x-ray showing infiltrate or consolidation in two lobes. In addition to computing a severity score for each disease, CSI also computes an overall severity score on a scale of 1 to 4 for the patient. Computation of the overall severity level takes into account the interaction of the principal diagnosis with the patient's secondary diagnoses as well as the severity level of the principal diagnosis and each secondary diagnosis (McGuire 1989). Thus, a pneumonia patient with a secondary diagnosis of a broken finger is not as severe overall as a pneumonia patient with a secondary diagnosis of congestive heart failure (CHF). Further, the overall severity level depends on the diagnosis-specific severity scores of the pneumonia and CHF. A patient's severity of illness can be computed for multiple time intervals during a hospital stay. The most common time intervals for which severity of illness is measured are: admission, discharge, and the maximum severity achieved during the stay. The maximum severity

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score represents the highest severity of illness experienced during a patient's stay and includes the effect of any post-admission complications. The DRGs, as currently defined, assign a patient to a different DRG when a significant complication occurs. To be consistent with the structure of the DRGs, the maximum severity was selected as the severity score to be used to adjust the DRGs in the New Jersey study, since it induded the impact of postadmission complications. Since the inception of the Medicare prospective payment system (PPS) in 1983, DRG improvements have focused on patients over age 65. Many known deficiencies in the DRGs relating to patients under age 65 have not been addressed. The State of New York initiated a DRG-based prospective payment system in January 1988 using an expansion of the Medicare DRG definitions as the basis of its payment system. The New York DRGs include additional DRGs for patients with an HIV infection, newborns, multiple trauma patients, patients with tracheostomies, transplant patients, and pediatric patients (New York State Grouper Version 6. 0 Definitions Manual 1988). The DRGs used in the New York and New Jersey payment systems in fiscal year 1989 are referred to as the New York Version 6.0 DRGs, and were used in the New Jersey Severity of Illness study. In both the New Jersey and Medicare prospective payment systems, outliers within a DRG are defined as patients with an atypical cost or length of stay. Hospitals are not paid the standard DRG prospective rate for outliers, but have a per diem adjustment applied to determine payment. New Jersey identifies outliers using DRG-specific high and low length of stay trim points while Medicare uses DRGspecific high length of stay trim points and per diem cost levels. In New Jersey, approximately 17 percent of hospital payments are associated with outliers while in Medicare, approximately 5 percent of hospital payments are associated with outliers. Thus, the method used to identify outliers and the accuracy of the outlier payment formula can have a significant effect on hospital payments. In this study outliers were defined using the standard high and low length of stay DRG trim points used in the New Jersey DRG payment system. Patients who are not outliers and for whom hospitals are paid the standard DRG rate are referred to as inliers. SAMPLING METHODOLOGY

All hospitals in New Jersey were invited to participate in the Severity of Illness study. Twenty-five hospitals volunteered to participate in the study. The costs associated with participation in the study were paid by

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the participating hospitals. The participating hospitals represented 28 percent of New Jersey hospitals and 34 percent of the discharges. The study hospitals were representative of New Jersey hospitals relative to location (e.g., urban versus rural) and bed size, but overrepresented the proportion of teaching hospitals. Based on previous research on the effect of severity on individual DRGs, a list of DRGs to be excluded from the severity study was developed. Excluded DRGs primarily represented elective surgery patients who would be unlikely to have surgery performed if they were severely ill. In addition, psychiatric and substance abuse DRGs as well as pediatric patients (age under 12 years) were excluded since the CSI severity criteria for these patients had not been completely field tested when the study began. Also, DRGs with less than 100 patients across the 25 study hospitals were excluded since there would have been an insufficient number of patients for the analysis. A total of 131 of the 470 DRGs were excluded from the study (28 percent) constituting approximately 40 percent of all patients in the study hospitals. A random sample of 85,232 patients in the study DRGs was selected from the study hospitals. The number of patients in the random sample from each hospital varied between 2000 and 4000 and was based on hospital size. Fiscal Year 1986 data were used in the study, since the 1986 data had been used to establish 1989 New Jersey hospital rates. HOSPITAL COST

The measure of hospital resources used in the study was direct patient care costs. Direct patient care costs exclude the cost of capital, certain administrative costs, and staff physician costs. The direct patient care costs were computed using departmental cost-to-charge ratios to convert charges into cost. The New Jersey payment system adjusts hospital DRG payments for wage rate variations and the indirect cost of teaching. The labor equalization factor was computed for each hospital by comparing nonphysician salary costs across 8 labor categories in 11 New Jersey labor market areas. The teaching neutralization factor was computed based on the ratio of approved residency programs to the number of cases treated (Graduate Medical Education 1989). Each residency program was weighted differently (e.g., a cardiac surgery residency program had a greater bearing on the indirect cost of teaching than a pediatric residency program). Based on the number of residency programs and patient volume in each hospital, a separate teaching neutralization factor was computed for surgery, medicine, obstetrics

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and gynecology, and pediatrics. Each DRG was assigned to one of these four specialty areas, and the product of the DRG cost and corresponding teaching neutralization factor removed the indirect cost of teaching from the DRG cost. All analyses were based on direct patient care costs that were labor equalized and teaching neutralized. EVALUATION MEASURES

Using the maximum severity scores, direct patient care cost, length of stay, and DRG information, the effect of dividing each DRG into four subgroups based on the CSI severity score was evaluated using three common statistical measures of predictive power and homogeneity: reduction in variance, reduction in weighted coefficient of variation, and reduction in mean absolute difference. Reduction in variance measures the decrease in the variance in the patient cost distribution that results from dividing a DRG into four severity subclasses. The reduction in weighted coefficient of variation measures the reduction in the coefficient of variation of the patient cost distribution that results from dividing the DRG into four severity subdasses. Reduction in mean absolute difference is similar to reduction in variance except that mean absolute differences are used instead of mean squared differences. The effect of severity on the total payments to hospitals was also evaluated. The 1986 number of patients, average cost, and payment rate for each of the 25 hospitals for each DRG was obtained from each hospital's rate schedule and used as the basis of the payment impact simulation. For each DRG within each hospital the actual number of patients in 1986 was separated into severity levels based on the fraction of patients at each severity level in the study database. Severity cost weights were computed for each DRG by dividing the average cost at each severity level by the average cost for the DRG. For each DRG, the standard payment was divided into severity levels using the severity cost weights for each DRG. The severity cost weights were normalized so that they would be budget neutral across the 25 hospitals. The cost and payment amounts were converted from 1986 dollars to 1989 dollars using a hospital-specific inflation factor. In some hospitals the number of patients in the sample was insufficient to allow some of the DRGs to be included in the payment impact analysis. Only DRGs in which there were at least ten patients in the sample, or in which the sample constituted at least 10 percent of the patients in the hospital, were included in the payment impact analysis for that hospital. For inlier patients the effect of severity on the total payments for

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each hospital was compared by computing the total payment a hospital would have received if it were paid the DRG rate versus if it were paid the DRG rate divided into four severity levels. Thus, the payment rate for each DRG was divided into four distinct payment rates using the severity cost weights for each DRG. Total severity-adjusted payments for a hospital were computed by multiplying the payment rate by severity level for each DRG times the number of patients at each severity level in the hospital. This DRG payment model is similar to the approach used in the Medicare prospective payment system, but differs from the payment methodology in use in New Jersey (Averill, McGuire, Manning, et al. 1989).

RESULTS At the end of the four-month data collection period, severity data on 76,798 of the 85,232 records in the sample (90.1 percent) had been submitted by the hospitals. The shortfall in the number of records submitted was due to the inability of hospitals to locate some of the medical records, the failure of several hospitals to complete data abstraction, and the elimination from the study of same-day surgery patients. Twenty-two of the 25 hospitals had completion rates greater than 80 percent, and the remaining three hospitals had completion rates of 68.5 percent, 53.8 percent, and 41.1 percent. The accuracy of the severity information collected in the study was evaluated. A random sample of charts for each individual hospital abstractor was evaluated by an independent CSI trainer who reabstracted the severity information for each chart. The abstracted values for each item of information (e.g., blood pressure) were compared between the hospital abstractor and the independent CSI trainer. The item agreement rate across hospitals varied from 91.8 percent to 97.6 percent, with an overall average of 95.5 percent. Of the 76,798 records with severity data collected in the study, 535 (0. 7 percent) lacked the physiological data necessary to assign diagnosisspecific and overall severity scores. These records were excluded from the study. In addition, 148 records were eliminated from the analysis because the cost data could not be merged with the severity data. In total, 76,115 records were used in the analysis. The distribution of severity scores across all study DRGs is shown in Table 1. For each DRG a statistical summary of the effect of dividing the DRG into four severity subclasses was computed for inlier patients. Based on the statistical summary, criteria were developed for selecting

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Table 1: Distribution of Severity Scores across All Study DRGs Severity 1 2 3 4

Count 52,665 12,614 4,354 6,482

Percent 69.2 16.6 5.7 8.5

Average Cost 1,639 3,299 4,926 7,901

the DRGs for which severity significantly affected patient cost. The DRG selection criteria were: 1. At least a 10 percent variance reduction of cost 2. At least a 5 percent reduction in weighted coefficient of variation of cost 3. At least a 5 percent reduction in mean absolute difference of cost 4. At least 20 percent of the patients at severity level 2 or higher 5. At least 100 inlier patients in the study database There were some DRGs in which virtually all patients were at severity level 1. For some of these DRGs, a few high-severity patients were very high cost. In such cases, the DRG could meet the first three statistical criteria. For example, DRG 119 vein ligation and stripping met the first three criteria but had 97.5 percent of the patients at severity level 1. Criterion four was imposed in order to exclude such DRGs, since these DRGs would require a large number of patients to have their severity evaluated in order to identify a few high-cost cases. The fifth criterion was imposed to provide a sample of sufficient size to be able to produce stable cost weights by severity level. Seventy-six DRGs met all of the five selection criteria. Thirty-one of the 76 DRGs were surgical DRGs and 45 were medical DRGs. The 76 DRGs tended to have higher percentages of high-severity patients than did the complete study population. The overall distribution of severity scores for inlier patients across the 76 DRGs is shown in Table 2. Patients are often assigned to different DRGs depending on whether or not the patient has a complication or comorbidity. None of the DRGs meeting the selection criteria were DRGs without a complication or comorbidity. For patients without a complication or comorbidity 92.8 percent of the patients were at severity level 1, while for patients with a complication or comorbidity only 52.8 percent of the

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patients were at severity level 1. Thus, the absence of any complications or comorbidities is associated with a low-severity patient. The Appendix contains for each of the 76 DRGs the number of inlier patients in the study, the reduction in weighted coefficient of variation of cost, reduction in variance of cost, reduction in mean absolute difference of cost, percent of patients by severity level, the average cost for the DRG, and the average cost by severity level. The F-statistic was computed for each DRG. The F-statistic determines whether statistically distinct groups were formed when the DRG was subdivided into four severity classes. The F-statistic was significant at the .001 level for all 76 DRGs. The statistical results for the 76 DRGs are summarized in Table 3. In general, the statistical results were better for surgical DRGs than for medical DRGs. For example, nine of the ten DRGs with highest reduction in variance were surgical DRGs, while eight of the ten DRGs with the lowest reduction in variance were medical DRGs. The 76 DRGs tended to be DRGs with high per case costs. In the 25 study hospitals, the 76 DRGs accounted for 41.4 percent of the total direct hospital patient care costs and 27.0 percent of the patients. In all but nine DRGs (18, 134, 144, 152, 188, 257, 263, 269, and 418), a monotonic increase in average cost occurred as severity level increased. For example, within DRG 1 Craniotomy, the average cost by severity level was $3,870, $5,665, $6,980, and $10,443, respectively. For the nine DRGs without a strict monotonic increase in cost, the severity level 3 patients tended to have a slightly lower cost than the level 2 patients. In all nine DRGs a small sample size existed at level 3 (always less than 30). Overall, across the 76 DRGs, the weighted coefficient of variation of cost by DRG was 0.695, while the coefficient of variation of cost by DRG and severity class was 0.574. Thus, overall there was a 17.4 percent decrease in the coefficient of variation of cost across the 76 DRGs. In terms of variance reduction, the 76 DRGs alone achieved a Table 2: Distribution of Severity Scores for Inlier Patients across the 76 DRGs Inlier

Severity 1

2 3 4

Count 14,503

7,501 2,808 3,209

Percent 51.8 26.8 10.0 11.4

Average Cost 1,934 2,773 3,797 5,367

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Table 3: The Number of DRGs from the 76 Study DRGs, with Statistical Results in the Specified Ranges Number of DRGs Percent Reduction 5- 9% 10-19% 20-29% 30-39% 40%+

Coefficient of Variation 13 34 23 6 0

Variance 0 33 26 12 5

Mean Absolute Difference 23 41 12 0 0

variance reduction of cost of 38.2 percent. The addition of the four severity levels to the DRGs increased the variance reduction of cost to 52.8 percent. Thus, the addition of the CSI severity score improved the variance reduction of cost of the 76 DRGs by 38.2 percent. [i.e., (52.8 percent - 38.2 percent)/38.2 percent = 38.2 percent]. The addition of severity to the standard DRG payment for the 76 DRGs changed the payment to hospitals in the range from an increase of 5.71 percent of the hospital's cost in the 76 DRGs to a decrease of 5.48 percent. Twelve hospitals had increases in payments while 13 hospitals had decreases in payments. The average absolute change in hospital payments was 2.53 percent. Seventeen of the hospitals had a change in the total payment in the 76 DRGs of greater than or equal to 2 percent of their cost. Impact on total hospital payment varied from a positive $980,784 to a negative $639,438. Hospital characteristics such as the number of beds, number of discharges, teaching status, location, DRG case-mix index, percent Medicaid, and percent Medicare were evaluated as explanatory variables of the change in total hospital payment due to the severity adjustment. A simple relationship between the characteristics of the hospitals and the effect of severity was not found. All of the above hospital characteristics had correlation coefficients with the change in total hospital payment of less than 0.25. However, a hospital categorization based on the number of discharges, percent Medicaid, and teaching status was found to be related to the change in hospital payments. For the purpose of hospital categorization, hospitals with any teaching programs were considered to be teaching hospitals. The effect of severity adjustment on total hospital payments by type of hospital is shown in Table 4 and can be summarized thus:

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* Small (less than 10,000 discharges), nonteaching, nonspecialty hospitals had total payments negatively affected by the severity adjustment. * Teaching hospitals with more than 10,000 discharges and a small Medicaid caseload (Medicaid patients constituting less than 6 percent of discharges) tended to have total payments negatively affected by the severity adjustment. * Teaching hospitals with more than 10,000 discharges and a moderate Medicaid caseload (Medicaid patients constituting 6 percent-20 percent of discharges) tended to have total payments positively affected by the severity adjustment. * Teaching hospitals with more than 10,000 discharges and a large Medicaid caseload (Medicaid patients constituting greater than 20 percent of discharges) tended to have total payments negatively affected by the severity adjustment. * Nonteaching community hospitals with more than 10,000 discharges had total payments positively affected by the

severity adjustment. This just-listed hospital categorization explained 50 percent of the variation in the change in total hospital payments due to severity adjustment. Outliers were determined at the DRG level and not at the severity level within a DRG. This resulted in a disproportionate number of severity level 4 patients being excluded as outliers. The overall distribution of severity scores for high length of stay outliers across all DRGs in the study is shown in Table 5. Conversely, the vast majority of low length of stay outliers discharged alive were severity level 1, as shown in Table 6. The average cost of high outliers is significantly affected by the severity level of the patients. Further, many of the severity level 4 patients would not be outliers if the outlier length of stay trim points were established by severity level within the DRG. A cost survey of 24 of the participating hospitals in the Severity of Illness study was conducted. The survey showed that approximately

$1,535,195 was spent for the 76,798 records collected in the study. Costs reported included labor, training, hardware, software, and supplies. The average cost per hospital for all these items was almost $64,000, which resulted in a cost of approximately $20 per study patient. The per study patient average cost within the individual hospitals varied from $6.20 to $31.85.

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Table 5: Distribution of Severity Scores for High Length of Stay Outliers Severity 1

2 3 4

Count 172 317 229 541

Percent 13.66 25.18 18.19 42.97

High Outliers Average Cost 7,063 9,519 10,324 15,129

Table 6: Distribution of Severity Scores for Low Length of Stay Outliers Severity 1

2 3 4

Count 1,153 115

22 12

Percent 88.56 8.83 1.69 0.92

Low Outliers Average Cost 739 556 642 1,340

DISCUSSION The finding that payments to small nonteaching hospitals are decreased by the addition of severity was not surprising, since small hospitals would not be expected to treat a disproportionately large number of high-severity patients. For teaching hospitals, the percentage of Medicaid patients was found to influence the payment impact of severity. Medicaid patients may be less likely to have a primary care physician and more likely to wait longer before seeking treatment. Therefore, they may be more likely to be more severely ill when hospitalized. Teaching hospitals with small Medicaid caseloads had payments reduced by the addition of severity. Those hospitals were either suburban hospitals or urban hospitals treating primarily suburban patients. Teaching hospitals with moderate Medicaid case loads had payments increased by the addition of severity. These hospitals were all urban hospitals. However, teaching hospitals with large Medicaid caseloads had payments decreased. It may be that once the percentage of Medicaid patients becomes large enough, the hospital may begin to play the role of an extended primary care facility. Patients with inadequate home environments may be admitted at low severity levels for social reasons.

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In interpreting the effect of a severity adjustment to the DRGs it is important to recognize that the DRGs themselves identify the patients requiring complex care (e.g., coronary bypass patients). Severity of illness measures the relative state of health of patients with similar conditions. For example, a hospital may do a large number of coronary bypass operations but its bypass patients may not be more severely ill than the bypass patients at other hospitals. This hospital's DRG payments, then, reflects the large number of coronary bypass operations performed, but a severity adjustment to the coronary bypass DRGs does not increase payments to the hospital. Thus, a hospital that does a great deal of complex surgery will not necessarily have payments increased by a severity adjustment to the DRGs. Conversely, a hospital that treats few patients requiring high-technology services but treats a relatively severely ill group of chronic medical patients (e.g., CHF, diabetes, etc.) may have its payments increased by a severity adjustment to the DRGs. The absolute value of the impact of severity on total hospital payment reflects the characteristics of the New Jersey payment system. In the New Jersey system, 27 percent of hospital costs are considered indirect costs and are excluded from the DRG rates, and of the remaining 73 percent of costs, 17 percent are excluded as outlier payments. Therefore, only 60.6 percent of the total hospital costs are included in the New Jersey inlier DRG payment rates. In contrast, under the Medicare system 11 percent of hospital costs are considered indirect costs and are excluded from the DRG rates and, of the remaining 89 percent of costs, 5 percent are excluded as outlier payments. Therefore, 84.5 percent of the total hospital cost is included in the Medicare inlier DRG payment rates. Thus, the Medicare inlier DRG payment rates include 39 percent more hospital cost than the New Jersey inlier payment rates. Under a Medicare payment system the impact of severity on total hospital payments would be estimated to have varied from approximately a positive $1,363,286 to a negative $888,818. Further, the impact of severity on total hospital payments found in this study may be a conservative estimate for hospitals nationwide since the study hospitals in New Jersey were relatively homogeneous. All of the hospitals in the study were within a small geographic area, and the majority of the hospitals had significant teaching programs. In a severity-adjusted DRG payment system, the length of stay outlier trim points should be defined separately for each severity level within a DRG. Having the trim points established at the severity level should improve the statistical performance of the system (e.g., lower coefficient of variation). While the establishment of trim points for

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each severity level within the DRGs would improve the accuracy of the identification of outliers, it would not eliminate the need for outliers within the payment system. As shown in Table 5 the cost of high length of stay outliers varied by severity level. Thus, consideration also needs to be given to establishing the high length of stay outlier payment by severity level. In general, the estimate of the effect of severity on total hospital payments is conservative, since the trim points used eliminated a disproportionately high number of severity level 4 patients and the effect of severity on high length of stay outlier payments was not considered. The study used the maximum severity score. The alternative would have been to base the severity adjustment to the DRGs on the severity at admission. The argument for using severity at admission is that if patients become more severely ill during their stay (i.e., maximum severity higher than admission severity), then the hospital should not be paid for the additional costs associated with the severity increase. Unfortunately, it is not possible without peer review to determine if the severity increase was unavoidable or the result of poor quality. Further, an increase in severity during a patient stay has not been found to be associated with quality of care problems (Iezzoni, Restuccia, Schwartz, et al. 1992). Basing the severity adjustment to DRGs on admission severity could result in hospitals not being paid adequately for patients whose severity increases after admission. Since patients with high admission severity are more likely to experience a severity increase (Horn, Sharkey, Buckle, et al. 1991), hospitals with the highest-severity patients would be affected most by the failure not to recognize the additional costs associated with postadmission severity increases. Since the objective of DRG payment is to provide equitable payment, and not to attempt to make judgments without peer review about the quality of the care rendered, the maximum severity score is the most appropriate basis for a severity adjustment to the DRGs. There are several methods of measuring patient severity in use. This study was limited to the CSI severity measure. The results could have been different if alternative severity measures had been utilized. The 76 DRGs represented 27 percent of the patients in the study hospitals. As discussed earlier, for patients with no complications or comorbidities 92.8 percent were found to be at severity level 1. This relationship can be used to reduce further the number of patients who need to have severity evaluated and can thus lower the implementation costs of a severity adjustment. The results of this study are generally consistent with the results of previous studies. The Severity of Illness Index (SII) was used in pre-

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vious studies to evaluate the effect of severity on individual DRGs and total hospital payments. SII achieved higher variance reductions than reported in this study and found that a severity adjustment to the DRGs affected total hospital payments in the range of a positive 23 percent to a negative 35 percent (Horn et al. 1986; Horn 1983; Horn et al. 1985). The reason for these differences may be that the subjective nature of the SII score resulted in a strong association between the SII score and hospital cost. The SII study was performed on untrimmed data, was based on the Medicare DRGs as opposed to the New York DRGs, used a less precise teaching adjustment, and compared a group of hospitals that were more heterogeneous than the 25 New Jersey hospitals. The MedisGroups admission and mid-stay severity scores have been used to evaluate the effect of severity on individual DRGs. In general, the MedisGroups admission and mid-stay severity scores were found to achieve lower variance reductions than reported in this study (Iezzoni et al. 1988; lezzoni et al. 1991). The New Jersey study and previous studies consistently report that a subset of the DRGs can be significantly improved by a severity adjustment. The New Jersey study is the most precise study of the effect of severity available. The severity measure was based only on objective physiological findings, and the definition of patient cost and outliers was very rigorous. Thus, based on this study and the results from previous studies, a severity adjustment to a subset of the DRGs would result in a more equitable DRG-based prospective payment system.

CONCLUSION Patient cost in 76 high-volume DRGs was found to be significantly affected by the severity level of the patient. The 76 DRGs accounted for 41.4 percent of the direct patient care costs and 27 percent of the patients in the study hospitals. The addition of the CSI severity levels to the 76 DRGs reduced the coefficient of variation of cost in these DRGs by 17.4 percent and improved the overall reduction in variance of cost within the 76 DRGs by 38.2 percent. Significant differences were found in the distribution of severity levels of patients treated in the different hospitals. Using a payment model similar to Medicare, the impact on total hospital payments in the 76 DRGs due to the addition of severity varied from a positive 5.71 percent to a negative 5.48 percent. Under the Medicare payment system, the impact on total hospital

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payments to individual hospitals could be as high as $1,363,289. This amount would be increased if outliers were defined at the severity level within each DRG and if length of stay outlier payments were adjusted for severity. Further, the estimated impact of severity on total hospital payments may be conservative since the study hospitals in New Jersey were relatively homogeneous. The results of the study demonstrate that a severity adjustment to a subset of the DRGs would result in a more equitable DRG-based prospective payment system.

ACKNOWLEDGMENTS The Severity of Illness study was a large project requiring the cooperation and coordination of many organizations and individuals. The staff at 3M Health Information Systems and QC, Inc., Laurence Gregg, Garry Archer, Amy Lippmann, Paul Lucchina, David Sparrow, Joyce Munson, Sue Andrews, Marilyn Marino, June Buckle, Linda Kalb, Lorna Thompson, and Julie Gassaway, provided installation, training, coordination, and support to the study hospitals. The staff at the New Jersey Department of Health, Dina Keller Moss, Vince Yarmlak, Grace Smith, Bernice Ferguson, and Manny Noggoh, provided project coordination and assistance in the design of the analysis and the interpretation of the results. The project team would like to thank the hospitals that volunteered for the study and the many individuals at the study hospitals who partidipated in the collection of the severity data.

REFERENCES Ament, R. P., J. L. Dreachslin, E. Kobrinski, and W. R. Wood. "Three Case Type Classifications: Suitability for Use in Reimbursing Hospitals." Medical Care 20 (May 1982): 460-67. Averill, R. F., R. W. Mullin, B. A. Steinbeck, and E. D. Ella. Diagnosis Related Groups, Sixth Revision: Definitions Manual. New Haven, CT: Health Systems International, 1989. Averill, R. F., T. E. McGuire, B. E. Manning, D. A. Fowler, S. D. Horn, and J. A. Bender. A Study of the Relationship between Severity of Illness and Hospital Cost in New Jersey Hospitals, Final Report. New Haven, CT: Health Systems International, December 1989. Calore, K., and L. Iezzoni. "Disease Staging and PMCs: Can They Improve DRGs?" Medical Care 25 (1987): 724-37. Coulton, C. J., S. McClish, H. Doremus, S. Powell, S. Smookler, and D. L. Jackson. "Implications of DRG Payments for Medical Intensive Care." Medical Care 23 (1986): 977-85.

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Graduate Medical Education Related Direct Patient Care Costs in the New Jersey Hospital Reimbursement Context. Randolph, NJ: Network, Inc., June 1989. Horn, S. D. "Measuring Severity of Illness: Comparisons across Institutions." American Journal of Public Health 73 (anuary 1983): 25-31. Horn, S. D., and J. M. Buckle. "Severity Indices: Potential Uses in Quality Measurement." Topics in Health Record Managmnt 10 (August 1989): 45-55. Horn, S. D., R. A. Horn, and P. D. Sharkey. "The Severity of Illness Index as a Severity Adjustment to DRG." Health Care Financing Review Annual Supplement (November 1984): 33-45. Horn, S. D., R. A. Horn, P. D. Sharkey, and A. F. Chambers. "Severity of Illness within DRGs: Homogeneity Study." Medical Care 24 (March 1986): 225-35. . "Severity of Illness within DRGs: Impact on Prospective Payment." American Journal of Public Health 75, no. 1 (October 1985): 195-99. Horn, S. D., P. D. Sharkey,J. M. Buckle,J. E. Backoffen, R. F. Averill, and R. A. Horn. "The Relationship between Severity of Illness and Hospital Length of Stay and Mortality." Medical Care 29 (April 1991): 305-17. lezzoni, L. I., A. S. Ash, J. L. Cobb, and M. A. Moskowitz. "Admission MedisGroups Score and the Cost of Hospitalizations." Medical Care 26 (November 1988): 1068-80. Iezzoni, L. I., A. S. Ash, G. A. Coffman, and M. A. Moskowitz. "Admission and Mid-Stay MedisGroups Scores as Predictors of Hospital Charges." Medical Care 29 (March 1991): 210-20. Iezzoni, L. I., and M. A. Moskowitz. "A Clinical and Analytic Assessment of MedisGroups." Journal of the American Medical Association 216 (December 1988): 3159-63. Iezzoni, L. I., and J. Daley. "A Description and Clinical Assessment of the Computerized Severity Index." Quality Review Bulktin 18 (February 1992): 43-52. Iezzoni, L. I., J. D. Restuccia, M. Schwartz, D. Schaumburg, G. A. Coffman, B. E. Kreger, J. R. Butterly, and H. P. Selker. "The Utility of Severity of Illness Information in Assessing the Quality of Hospital Care." Medical Care 30 (May 1992): 428-44. Jencks, S. F., and A. Dobson. "Refining Case-Mix Adjustment: The Research Evidence." New England Journal of Medicine 317 (1987): 679-86. McGuire, T. E. "Evaluation Criteria for Case-Mix Analysis Systems." Ph.D. diss., Yale University, 1989. New York State Grouper Version 6. 0 Definitions Manual. New, Haven, CT: Health Systems International, December 1988.

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A study of the relationship between severity of illness and hospital cost in New Jersey hospitals.

In response to concerns over the equity of diagnosis-related group (DRG)-based prospective payment, the New Jersey Department of Health conducted a Se...
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