Health Policy, 17 (1991) 151-164

0 1991 Elsevier Science Publishers B.V. (Biomedical Division) 0166.8510/91/$03.50

HPE 00391

Refi

Jean Departr

ned DRGs: Trial

L. Freeman

s in Europe

went of Community and Famii /y Medicine, Dartmouth Medical School,

IS1

Hanover, NH, U.S.A.

Accepted 23 November 1990

Summary

DRGs have recently been revised to account for severity of illness through a more refined use of additional diagnoses (comorbidities and complications). Under the refined model, patients are differentiated with respect to classes of additional di- agnoses that are disease and procedure specific. The Refined DRGs were evaluated with data from selected Barcelona hospitals and the findings compared to those obtained with Norwegian and English hospital discharge information. In terms of predlctive performance, the Refined DRGs represent only a very small Improvement over the second revision DRGs for the study’s sample of European data. This lack of significant improvement is likely attributed to misclassification of the European discharges due to limited reporting of additional diagnoses. It is recommended that European countries use the Refined DRGs as a descriptive framework for reporting utilization statistics in order to encourage more complete reporting of comorbidities and complications’.

Refined DRGs; Predictive performance of RDRGs; Misclassification of discharges; RDRGs trials in Europe

Background

Diagnosis Related Groups are a system for describing the types of patient discharged from acute care hospitals. Classes of patient are defined in terms of

*Revised version of a paper presented at the EURODRG Workshop on ‘DRG data production: Issues and action for international comparability’ held in Barcelona, Spain, 29-30 June, 1990.

Address for correspondence: Jean L. Freeman, PhD, Department of Community and Family Medicine, Dartmouth Medical School, Hanover, NH 03756, U.S.A.

152

one or more of the following variables: principal diagnosis, additional diagnoses, surgical procedures, age, sex and discharge status. The groups were designed to be clinically coherent in the sense that they are expected to evoke a set of clinical responses which result in a similar pattern of resource use. Hence, the type and amount of services ordered by a physician is expected to be similar for all patients he treats in a given DRG.

From a hospital management perspective, the groups represent ‘product lines’ in that patients within the group are provided similar packages or profiles of services [l]. For example, a patient hospitalized for acute appendicitis without peritonitis and without comorbid conditions might be expected to consume 12 meals, 4 days of hotel services, 16 hours of nursing care and 50 minutes of operating room time, in addition to other ancillary services, over the course of his stay.

In the context of these product lines, various aspects of production and operations management commonly employed by manufacturing firms - product selection and design, quality control and cost accounting - can be applied to hospitals for the purpose of increasing efficiency and quality of care. As in a manufacturing firm the set of products that constitutes the business of each hospital can be used as the basis for a flexible budgeting and cost control system. Each product is identified in terms of the treatment plan and set of services expected to be delivered to the patient. This constitutes the ‘bill of materials’ in manufacturing terms.

Payment policies in the United States have provided strong incentives to manage hospital services along product lines. Medicare now pays hospitals a fixed price per DRG for providing services to the program’s beneficiaries. Under this system, if a patient’s treatment plan costs less than the fixed rate, the hospital may keep the difference. But if its costs for providing services exceed the rate, it must absorb the loss.

Since the implementation of Medicare’s prospective payment system (PPS), dramatic changes in hospital utilization have been observed, some of which may be attributed to the payment mechanism. In particular, there is considerable evidence that hospitals have responded to the system as intended by eliminating some of the inefficiency in treating Medicare patients. Based on a study of utilization in a sample of discharges from 729 hospitals, DesHarnais et al. [2] found significant decreases in intensive care use, length of stay and pre-operative length of stay for Medicare patients in 1984. There was also a significant decline in Medicare admissions. Although hospital utilization of Medicare beneficiaries had been declining in the years prior to PPS, they found the drop in 1984 was greater than expected based on previous trends. Changes in the same direction were also observed for non- Medicare patients, but the differences were not significant.

A review and synthesis of other studies designed to evaluate the impact of PPS on various dimensions of health care delivery is given in Guterman and Dobson [3] and Guterman et al. [4]. These studies have found that nationally for Medicare beneficiaries in the three years after PPS: (a) length of stay has decreased; (b) severity at admission has increased; (c) readmissions within 30 days have remained stable; and (d) the use of home health services has increased, but at a slower rate than the years before PPS.

153

Since 1983 the DRGs as a classification system have also been evaluated on a yearly basis for the Medicare program by the Health Care Financing Administration. While revisions resulting from these yearly reviews have considerably improved the system’s ability to measure more precisely the complexity of a hospital’s case mix, there is still concern that the DRGs do not adequately account for one factor potentially associated with complexity - the severity of a patient’s illness. In response, the DRGs have recently been refined by the Health Systems Management Group (HSMG) with an approach consistent with alternative case mix systems [5]. Under the refined model, patients are differentiated with respect to classes of additional diagnoses (or comorbidities and complications) that are disease and procedure specific. Each class represents a different level of utilization for a given category of principal diagnosis (medical hospitalizations) or surgical procedure (surgical hospitalizations).

The refined model was evaluated in terms of its predictive performance for a sample of Medicare discharges and all adult discharges from Ohio acute care hospitals in 1986. Based on charge information for the non-outlier Medicare discharges (0.94 percent outliers), the r-square increased from 0.3340 under the fifth revision DRG model to 0.4562 under the Refined DRG system. For charges on non-outlier Ohio discharges, the increase was from 0.3470 to 0.4560. The level of predictive performance, though, is strongly associated with the percent of discharges defined as outliers. Assuming 3.75 percent outliers in the Medicare data, the predictive performance increased from 0.3933 to 0.5361.

There was considerable interest on the part of HSMG to investigate whether Refined DRGs improve the predictive performance of DRGs on discharge data collected in countries other than the United States. In particular, a collaborative project was initiated by the Institute Municipal de la Salut, Barcelona, and the HSMG to evaluate the extent to which the RDRGs could be used by Barcelona hospitals to classify patients with respect to resource use. Once the Spanish project was completed, further work was performed to see if the evaluation findings based on Spanish data were generalizable to other European countries. This paper contains the findings of that evaluation. Before presenting the results, descriptions of the general structure of the Refined DRGs and the data sources used in the evaluation are given.

Overview of Refined DRGS

The general structure of the Refined DRG system is illustrated in Fig. 1 with a tree diagram similar to those presented in the DRGs Fifth Revision Technical Definitions Manual. Patients are first assigned to one of twenty-three major diag- nostic categories (MDCs) based on their principal diagnosis code. Within an MDC, patients with a temporary tracheostomy are isolated as an initial group and are not considered further in the classification process. The remaining patients are then categorized as surgical or medical based on the presence or absence, respectively, of an operating room procedure.

Medical patients who died within two days of admission form a separate DRG

154

Major Diagnostic Category

Fig. 1. Structure of Refined DRG classification with medical and surgical classes of additional diagnoses.

referred to as ‘early death.’ All other medical patients (i.e., those not classified as ‘early death’ or ‘temporary tracheostomy’) are categorized into subgroups based on

their principal diagnosis. Likewise, the remaining surgical patients (i.e., those nut classified as ‘temporary tracheostomy’) are categorized into subgroups of operating room procedures. These medical and surgical subgroups are referred to as adjacent DRGs or ADRGs.

Finally, patients in each of the medical and surgical ADRGs are divided into final groups (DRGs) based on classes of additional diagnoses. The classes for medical hospitalizations represent subsets of additional diagnoses on the fifth revision’s comorbidities and complications (CC) list that have a major (MB), moderate (MC) and minor/no (MD) effect on resource use. Likewise, for surgical hospitalizations the classes represent those with a catastrophic (S,& major (Sa), moderate (SC) and minor/no (So) effect. For completeness, Mo and SD also contain all additional diagnoses not on the CC list.

These medical and surgical classes are therefore defined by lists of specific diagnosis codes. For the most part, the lists that define the medical classes are the same across all the medical ADRGs and the lists that define the surgical classes are the same for all surgical ADRGs. That is, there are standard or generic sets of diagnosis codes that define the classes for all medical (MB. Mc, Mo) and surgical (SA, Sg, SC, So) hospitalizations. However, selected codes may be added or excluded from these lists depending on the ADRG. These modifications reflect the differential effect that some additional diagnoses have on resource use depending on the patient’s principal diagnosis or surgical procedure.

In summary, under the DRG refinement model a patient is first assigned to an MDC based on his principal diagnosis code and then to a tracheostomy or early death group if he had a temporary tracheostomy or died within two days of admis- sion (medical patients only). A patient not classified as ‘early death’ or ‘temporary tracheostomy’ is assigned to one of 317 ADRGs based on his principal diagnosis (medical hospitalization) or major procedure (surgical hospitalization) then to the highest class containing one of his additional diagnoses. Patients with no additional diagnoses are assigned to class Mo or S D. This assignment algorithm applies to all MDCs except MDC 3 and MDC 15. In MDC 3 only medical patients can be assigned to the initial tracheostomy group. In MDC 15 a model specific to neonates was developed as part of a special substudy of the DRG Refinement Project.

Data

There are six sources of hospital discharge data used in this report - one for Barcelona hospitals, one for hospitals in the Mersey Region of the British National Health Service, one for Norwegian hospitals and three for U.S. hospitals. These sources are described in the following sections.

Barcelona hospitals

The discharge data from Barcelona consists of 59024 discharges from six hospitals during 1987. The six hospitals are Esperanca Municipal, Mar Municipal.

156

Sant Pau University, Creu Roja, Blanes and Matarb. Data on individual discharges include up to 5 diagnoses and up to 3 procedures. The diagnosis were coded in either ICD-9-CM or ICD-9 and the procedures in either ICD-9-CM or a local code [6].

National Hospital Discharge Survey

Data from the 1985 NHDS are used in this report to compare the relative frequency of recording additional diagnoses between countries. This survey is conducted yearly by the National Center for Health Statistics (NCHS). The survey’s scope includes patients discharged from non-federal, short-stay (average length of stay less than 30 days) hospitals with at least six beds for inpatient use, located in the 50 states and the District of Columbia. Estimates based on the survey are generalizable to the civilian resident population of the United States.

The data were obtained from NCHS in the form of a public use micro data tape. Each of the 194 801 observations in the sample represents a single discharge and has an associated weight factor that is used in determining estimates of total discharges in a particular category. Standard errors of these estimates may be computed on the basis of relative standard errors which appear in the appendix of publications pertaining to NHDS for 1985 [7].

Ohio hospital data base

Hospital discharge records for adult patients from all 161 acute care hospitals in the state of Ohio from 1986 are used in comparisons of predictive performance across countries. After eliminating discharges with the following characteristics: (1) age<l8; (2) invalid or zero length of stay; (3) invalid or zero charges; and (4) DRGs 468, 469. 470, 476. 477, a total of 940975 discharges were available for analysis.

Medicare hospital claims

The predictive performance of RDRGs on European data was also compared to that for U.S. data using a 20 percent sample of the fiscal year 1986 Medicare provider analysis and review (MEDPAR) file provided by the Health Care Financing Administration (HCFA). This 20 percent sample contained 1929 559 records from which records with the characteristics described above for Ohio were eliminated. A further subsample was drawn based on stratified random sampling within ADRGs that resulted in a final analysis data base of 1006 177 records. These records include up to 5 diagnoses and up to 3 procedures. Information on the predictive performance of RDRGs using these and the Ohio data were obtained from the DRG Refinement Final Report [5].

Mersey hospital discharge data

Data from England were obtained from the Mersey Region of me British National Health Service (NHS) and include all short-stay hospital discharges collected as part

157

of the Hospital Activity Analysis for 1985 (327 289 records). Excluded are most maternity cases and data from a few private hospitals. Diagnoses and procedures were coded in ICD-9 and the Office of Population Censuses and Surveys 3rd Revision procedures classification, respectively. Codes were mapped to ICD-9-CM for RDRG assignment using conversion tables generated by HSMG as part of a collaborative project with the DHSS.

Norway

Data from Norway are a sample (87 245 records) of short-stay hospital discharges for the first half of 1987. The sample was designed to be representative of all hos- pitals in Norway, but previous analyses have suggested that major referral hospitals may have been underrepresented [6]. Diagnoses were coded in a modification of ICD-9 and procedures in a national coding system. These codes were mapped to ICD-9-CM with a conversion table developed by the Health Systems Management Group as part of a collaborative research project with the Norwegian Institute for Hospital Research [ 81.

Table 1 Percent of patients by MDC and class for all medical discharges In the Barcelona and MEDPAR data bases

- Barcelona MEDPAR

early MB MC MD early MB MC MD- death death

1 Nervous 3.2 3.0 16.1 11.6 3.0 10.4 46.8 39.9 2 Eye 0.0 2.2 3.9 94.0 0.2 4.6 38.9 56.3 3 Ear, nose, throat 1.9 I.6 7.1 89.4 0.8 6.6 45.5 47.1 4 Respiratory 1.7 4.4 25.4 68.4 3.1 20.2 52.9 23.9 5 Circulatory 4.3 6.2 26. I 63.4 3.2 15.0 48.4 33.4 6 Digestive 1.3 2.5 20.2 76.0 I.4 7.0 51.9 33.7 7 Hepatobiliary 1.4 8.4 28.0 62.3 2.5 15.4 52.0 30.1 8 Musculoskeletal 0.4 1.8 6.8 91.0 0.4 7.1 38.2 54.3 9 Skin, subcutaneous tissue 1.4 2.4 21.3 14.9 0.9 7.7 52.6 38.8

IO Endocrine 1.3 3.0 21.1 74.6 1.9 12.3 51.5 34.4 I I Kidney and urinary 1.0 4.0 15.5 79.4 1.7 15.4 50.5 32.4 12 Male reproductive 1.2 2.8 24.8 71.2 1.5 8.1 50.9 39.5 13 Female reproductive 0.4 1.2 13.3 85.2 3.1 10.4 51.2 35.3 I4 Pregnancy 0.0 0.8 3.0 96.2 0.0 1.9 27.2 70.9 I6 Blood 1.2 5.6 16.6 76.6 I.1 10.5 56.2 32.2 17 Myeloproliferative 0.5 2.8 8.2 88.5 2.1 15.5 56.6 25.9 I8 Infectious 2.5 6.0 18.4 73.0 6.8 26.2 51.2 15.8 I9 Mental 0.0 1.7 10.4 87.9 0.1 5.7 38.0 56.2 20 Substance use 0.0 2.9 12.8 84.3 0.2 5.0 37.7 57.2‘ 2 I Injuries 6.1 4.1 8.5 81.2 1.1 11.3 49.4 38.2 22 Bums 0.0 0.0 0.0 100.0 2.4 9.7 42.1 45.8 23 Health status 6.4 1.8 7.0 84.8 I.3 9.3 43.1 46.3

All 1.9 3.6 17.4 77.1 2.4 13.4 49.7 34.5

158

Table 2 Percent of patients by MDC and class for all surgical discharges in the Barcelona and MEDPAR data bases

Barcelona MEDPAR

SA SB SC SD SA SB SC SD

1 Nervous 2 Eye 3 Ear, nose, throat 4 Respiratory 5 Circulatory 6 Digestive 7 Hepatobiliary 8 Musculoskeletal 9 Skin, subcutaneous tissue

10 Endocrine 11 Kidney and urinary 12 Male reproductive 13 Female reproductive 14 Pregnancy 16 Blood 17 Myeloproliferative 18 Infectious 19 Mental 2 1 Injuries 22 Bums 23 Health status

All

3.8 0.0 0.1 9.5 2.8 3.1 3.6 0.9 0.2 0.5 1.4

(9.: 0:4 1.9 0.0

17.1 11.1 6.6 0.0 6.5

4.3 0.4 0.5

10.1 10.9 5.0 6.7 2.0 1.3 1.8 3.3 0.7 1.5 0.5 1.9 5.5 8.6 0.0 4.6 0.0 3.2

1.4 0.7

9:: 8.2 2.7

10.3 0.8 1.8 2.7 6.9 0.9 1.4 7.8 0.0 6.3 5.7

90.5 98.9 99.1 74.6 78.1 89.2 79.5 96.3 96.8 95.0 88.4 98.1 97.1 91.4 96.3 88.3 68.6 88.9 85.5

100.0 87.1

1.5 3.1 2.9 92.5

11.3 20.2 17.1 51.5 0.9 5.8 17.4 75.8 4.8 11.8 17.4 65.9

23.1 37.0 17.2 22.6 19.4 33.5 21.3 25.9 14.9 27.3 16.4 41.4 14.3 25.5 26.1 34.1 6.2 21.6 15.6 56.7 6.1 18.6 20.3 55.0

10.4 38.4 10.5 40.7 9.1 14.6 31.4 44.9 2.8 8.4 32.2 56.7 2.0 16.2 20.7 61.1 0.9 5.6 29.2 64.3

25.8 21.6 17.5 35.1 13.5 24.0 17.7 44.9 37.3 35.0 8.5 19.3 16.8 28.9 17.4 36.9 14.7 20.9 28.0 36.5 24.2 16.7 13.6 45.5 4.1 20.2 18.8 57.0

10.4 21.9 20.5 47.1

Case mix of Barcelona hospitals

The case mix and predictive performance of RDRGs on Barcelona data was compared to that for U.S. data using the 20 percent sample of the fiscal year 1986 Medicare provider analysis and review (MEDPAR) file. Tables 1 and 2 present the percent of patients by MDC and class for medical and surgical hospitalizations in the Barcelona and MEDPAR data bases. One of the most striking observations about these descriptive statistics is the relatively low frequency of complex cases - the proportion of hospitalizations in the non-baseline classes (C=l, B=2 or A=3). Overall, for medical hospitalizations in the MEDPAR data base, the percent distribution of patients by early death and the three classes (MB, MC and MD) is 2.4. 13.4,49.7 and 34.5. By contrast, the percent distribution of Barcelona patients in these categories is 1.9, 3.6, 17.4 and 77.1. Differences in complexity are even more pronounced for surgical hospitalizations. The percent of Barcelona patients in the non-baseline classes (SA, SB, SC) is 7.5 compared to 52.8 for Medicare patients. In both data bases this profile of complexity varies by MDC.

The differences in the complexity distribution may be attributed in part to the different populations under comparison in Tables 1 and 2. However, the generally low proportion of complex cases persists in the Barcelona data when only patients, at least 65 years of age, are considered (Tables 3 and 4).

The differences may also result from a lower likelihood of reporting additional diagnoses in Barcelona hospitals. The percent distribution of cases by number

Table 3 Percent of oatients bv MDC and class or medical discharges in the Barcelona (Age x65) and MEDPAR data bases-

Barcelona (Age>-65)

early MB MC MD death

~~- MEDPAR

early MB MC MD death

I Nervous 5.9 2 Eye 0.0 3 Ear, nose, throat 3.9 4 Respiratory 2.x 5 Circulatory 5.4 6 Digestive 2.4 7 Hepatobiliary I.9 8 Musculoskeletal 0.6 Y Skin. subcutaneous tissue 3.X

IO Endocrine 2.X I I Kidney and urinary 2.1 I2 Male reproductive I.0 13 Female reproductive 0.0 14 Pregnancy 0.0 I6 Blood 1.X I7 Myeloproliferative 0.9 IX Infectious IO.6 IY Mental 0.0 20 Substance use 0.0 2 I Injuries 15.X 22 Bums 0.0 23 Health status IS.3

4.0 21.6 68.5 3.0 4.2 S.6 90. I 0.2 3.9 14.1 7X.2 0.X 3.6 31.7 62.0 3.1 7.0 2Y.2 58.5 3.2 S.0 25.X 66.X I.3 9.7 27.0 61.4 2.5 3.4 9.x 86.3 0.4 3.X 31.6 60.9 0.9 4.9 30. I 62.3 I.9 6.4 20.2 71.3 1.7 4.9 37.9 S6.3 1.5 2.5 22.0 7.5.4 3.1 0.0 6.3 Y3.X 0.0 3.7 23.X 70.7 I.1 3.5 10.0 84.6 2.1 7.3 24.5 57.6 6.X 3.3 22.2 74.5 0. I 0.0 1x.x XI.3 0.2 X.8 12.3 63.2 I.1 0.0 0.0 100.0 2.4 3.5 10.3 70.9 I .3

IO.4 46.X 3Y.Y 4.6 3x.9 56.3 6.6 45.5 37.1

20.2 52.9 23.9 IS.0 4X.4 33.4 7.0 57.9 33.7

IS.4 52.0 30. I 7.1 3x.2 54.3 7.7 52.6 3x.x

12.3 51.5 34.4 IS.4 SO.5 32.4 X.1 SO.9 3Y.S

10.4 51.2 35.3 1.9 27.2 70.‘)

IO.5 56.2 32.2 IS.5 56.6 25.9 26.2 51.2 15.x

5.7 38.0 56.2 5.0 37.7 S7.2

II.3 49.4 3x.2 9.7 42. I 45.x 9.3 43.1 36.3

,411 3.7 5.1 25.3 66.0 2.4 13.3 49.7 34.5

of recorded diagnoses is not available for the MEDPAR data base used in the DRG Refinement Project. However, a comparison may be made with the National Hospital Discharge Survey (Table 5). About 35 percent of the discharges from Barcelona have any additional diagnoses recorded compared to about 75 percent for the NHDS. (Note: for the NHDS, the category ‘7 diagnoses’ contains discharges with 7 or more diagnoses, only 7 of which were abstracted for the NHDS record.)

Predictive performance of RDRGs for Barcelona hospitals

The predictive performance of both the DRG and the DRG retinement models was evaluated with data from the Barcelona hospitals. Predictive performance was measured in terms of r-square - the proportion of variance in resource use which is explained by classifying patients into groups. Table 6 presents the r-square for all and non-extreme discharges, under the Refined and second revision DRG models, using log transformed length of stay. RDRG 4700 and all RDRGs with less than 30 observations are eliminated from this analysis.

Non-extreme discharges were identified through three different ‘trimming’ algo- rithms. The first algorithm (algorithm 1) is similar to the one used in the evaluation of Refined DRGs on U.S. data [5]. According to this algorithm. records with lengths

160

Table 4 Percent of patients by MDC and class for all surgical discharges in the Barcelona (Age > ~65) and MEDPAR data bases

Barcelona (Age >-65) MEDPAR

SA SB SC SD SA SB SC SD

1 Nervous 7.1 5.9 2 Eye 0.0 0.2 3 Ear, nose, throat 1.9 6.8 4 Respiratory 14.3 15.7 5 Circulatory 2.8 11.2 6 Digestive 6.3 9.7 7 Hepatobiliary 5.2 8.6 8 Musculoskeletal 1.2 2.8 9 Skin, subcutaneous tissue 0.0 3.7

10 Endocrine 0.0 5.9 11 Kidney and urinary 2.5 3.8 12 Male reproductive I.0 1.5 13 Female reproductive 0.6 3.0 14 Pregnancy 0.0 0.0 16 Blood 8.3 8.3 17 Myeloproliferative 0.0 2.9 18 Infectious 13.3 13.3 I9 Mental 50.0 0.0 2 1 Injuries 2.6 5.3 22 Bums 0.0 0.0 23 Health status 25.0 16.7

All 3.1 5.9

0.0 87.1 11.3 20.2 17.1 51.5 1.0 98.8 0.9 5.8 17.4 75.8 1.0 90.3 4.8 11.8 17.4 65.9 5.7 64.3 23.1 37.0 17.2 22.6

11.8 74.2 19.4 33.5 21.3 25.9 5.3 78.7 14.9 27.3 16.4 41.4

15.5 70.7 14.3 25.5 26.1 34.1 1.4 94.6 6.2 21.6 15.6 56.7 5.2 91.1 6.1 18.6 20.3 55.0

11.8 82.4 10.4 38.4 10.5 40.7 4.4 89.2 9.1 14.6 31.4 44.9 1.5 95.9 2.8 8.4 32.2 56.7 4.2 92.2 2.0 16.2 20.7 61.1

10.0 90.0 0.9 5.6 29.2 64.3 0.0 83.3 25.8 21.6 17.5 35.1

14.7 82.4 13.5 24.0 17.7 44.9 6.7 66.7 37.3 35.0 8.5 19.3 0.0 50.0 16.8 28.9 17.4 36.9 5.3 86.8 14.7 20.9 28.0 36.5 0.0 100.0 24.2 16.7 13.6 45.5 8.3 50.0 4.1 20.2 18.8 57.0

5.3 85.7 10.4 21.9 20.5 47.1

Table 5 Percent of discharges by number of diagnoses recorded: Barcelona hospitals vs. U.S. hos- pitals’

Number of diagnoses recorded

1 2 3 4 5 6 7

Barcelona U.S.

*Excluding MDC 15.

64.9 16.9 13.5 2.8 1.9 25.4 26.9 16.6 11.1 7.3 4i 8i

of stay outside the interval between exp (1nQt -1.7’(lnQ3 - lnQl))

and exp (lnQs+l .7*(lnQ3 - lnQI))

are identified as extreme observations, where Ql and Q3 are the first and third quartiles, respectively. This algorithm identified 5.1 percent of the discharges in the data base as extreme observations, 3.0 percent as low extremes and 2.1 percent as high extremes.

The second algorithm (algorithm 2) is consistent with the one employed by Palmer et al. [6] in their international comparison of hospital utilization by DRG. According to this algorithm extreme observations are defined as those records with a length of stay greater than

161

Table 6 Predictive performance (r-square) of the DRG Refinement and second revision DRG models at the system level: dependent variable = log length of stay

____. DRG Refined DRG

Barcelona hospitals

All discharges 0.3359 0.3532 Non-extreme discharges

algorithm 1 0.3647 0.3892 algorithm 2 0.3772 0.4026 algorithm 3 0.4214 0.4549

1986 MEDPAR sample

All discharges 0.1547 0.2136 Non-extreme discharges 0.2030 0.2809

1986 Ohio data base

All discharges 0.2509 0.3074 Non-extreme discharges 0.2773 0.3395

Q3 + 1.5’ (Q3 - 121). Hence, unlike the algorithm 1, algorithm 2 identifies only the extremely long values of length of stay. Approximately 6.3 percent of the discharges were identified 21s extreme observations with this algorithm.

The third algorithm (algorithm 3) is a variation of that discussed in Appendix B of Lichtig [9]. The first step of this algorithm involves computing estimates of the mean (ML) and standard deviation (SL) of length of stay based on the non-extreme discharges defined with algorithm 1. Under the assumption that the log transformed lengths of stay within each RDRG follow a normal distribution. new trim points of the transformed LOS are computed as:

Al = In(&) - 1/2(ln (SL2/&’ + 1)) - 3 (ln(SL2/Mr,* + l))lfi A2 = ln(ML) - 1/2(ln (&‘/ML2 + 1)) + 3 (ln(S1A2/ML” + l))lR

The corresponding points for the untransformed data arc: T1 = TRUNC (exp(Al)) + 1 T2 = TRUNC (exp(A;?)

where TRUNC(x) is the largest integer <= x. Hence, under algorithm 3 all observa- tions outside the interval between T1 and T2 are considered extreme observations. This algorithm identified 5.6 percent of the observations as extreme, 3.5 percent are low extremes and 2.1 percent are high extremes.

The r-square for the refined model for all discharges was 0.3532 compared to 0.3359 under the second revision DRG model. For non-extreme discharges, the r-square for the refined model was 0.3892 compared to 0.3647 for the second revision DRGs under algorithm 1; 0.4026 compared to 0.3772 under algorithm 2; and 0.4549 compared to 0.4214 under algorithm 3. Hence, the Refined DRGs represent only a modest improvement in predictive performance over the second revision model. However, the r-squares are higher than those found when the predictive performance was evaluated on a sample of MEDPAR discharges and all adult discharges from the state of Ohio [S]. Results from these evaluations are presented at the bottom of Table 6.

162

Case mix and predictive performance for Norway and Mersey

The case mix and predictive performance of RDRGs on Norwegian and English data were also evaluated. The English data are a 25 percent random sample of discharges from the Mersey Health Authority.

Table 7 presents the percent of patients by class for medical and surgical hospitalizations in these data bases. As noted for Barcelona, there is a relatively low frequency of complex cases. Overall, for medical hospitalizations in the Norwegian data base, the percent distribution of Norwegian patients by early death and the three classes MB, MC and MD is 1.7, 2.0, 8.3, and 88.1. The corresponding distribution for Mersey patients is 1.9, 1.8, 7.1 and 89.2. The percent of surgical patients in the non-baseline classes (SA, SB and SC) is 2.3 for Norway and 5.1 for Mersey.

Consistent with our observations on Barcelona’s data, it is likely that this low frequency of complex cases is attributed to the low reporting of secondary diagnoses, Table 8 presents the percent of discharges in the Norwegian and Mersey data bases by number of diagnoses recorded, with comparison data on Barcelona and the U.S. About 22 percent of the discharges from Norway and 28 percent of the discharges in Mersey have any additional diagnoses recorded. Note this level of reporting is lower than that for Barcelona.

The predictive performance of the RDRGs and the DRGs is presented in Table 9 for all and non-outlier discharges. Non-outlier discharges were identified using the second algorithm described for Barcelona. As found for the Spanish data, the Refined DRGs represent only a modest improvement in predictive performance over the second revision model.

Table 7 Percent of patents by type of hospitalization and class in the Norwegian and Mersey data base

Medical

early MB MC death

MD

Surgical

SA SB SC SD

Norway 1.7 2.0 8.3 88.1 0.5 0.9 0.9 97.8 Mersey 1.9 1.8 7.1 89.2 0.9 2.2 1.9 95.0

Table 8 Percent of discharges by number of Dx recorded*

Number of diagnoses recorded

I** 2 3 4 5 6 7

Barcelona 64.9 16.9 13.5 2.8 I.9 U.S. 25.4 26.9 16.6 I I.1 7.3 4.8 8.0

Norway 78.5 16.1 4.1 1.0 0.2 Mersey 72.0 20.3 5.5 1.5 0.5 0.2

*Excludes MDC 15. **Less than or equal to I.

161

Table 9 Predictive performance (r-square) of the DRG refinement and second revision DRG models at the system level

DRG Refined DRG

All discharges Non-outlier discharges

All Discharges Non-outlier discharges

Norwegian hospitals

0.32 0.32 0.38 0.39

Mersey hospitals

0.38 0.38 0.44 0.45

Summary and Discussion

The Refined DRGs were evaluated with data from selected Barcelona hospitals and the findings compared to those obtained with Norwegian and English hospital discharge information. Comparisons were also made to the U.S. using discharge data from Ohio, the National Hospital Discharge Survey and claims data from the Medicare program. Highlights of the study findings are:

- The percent of European discharges in the non- baseline or more complex Refined DRGs is considerably lower than that for a comparison group of U.S. discharges. Specifically, for patients at least 65 years of age, 34 percent of Barcelona medical hospitalizations are in non-baseline RDRGs compared to 65 percent for a sample of Medicare medical discharges. For surgical hospitalizations, only 14 percent of Barcelona discharges are in non-baseline RDRGs compared to 53 percent for Medicare discharges. Likewise, for Norway and Mersey only 10.3 percent and 8.9 percent, respectively, were classified into class B or C for medical hospitalizations; 2.3 percent and 5.0 percent were classified into class A, B. or C for surgical hospitalizations.

- There is some evidence that this lower level of case mix complexity may be attributed to a lower likelihood of reporting additional diagnoses.

- In terms of predictive performance, the Refined DRGs represent only a very small improvement over the second revision DRGs for the study’s sample of European discharge data. This lack of significant improvement is likely attributed to the under-reporting of additional diagnoses.

- Although different trimming algorithms produced different trim points by RDRG, choice of one algorithm or another to identify extreme observations ap- peared to make little difference in terms of the essential findings of the evaluation for Barcelona hospitals.

These findings have also raised the issue of ‘over-reporting’ of additional di- agnoses in the United States. That is, the U.S. discharge records might contain a higher proportion of additional diagnoses that are not clinically significant (or resource intensive) comorbidities and complications than the European discharge records. Cohen et al. [lo] refer to this as the ‘supplementation’ effect of PPS, for which precise national estimates of occurrence in the Medicare data are not avail- able. Nevertheless, the effect of supplementation on the Refined DRGs’ predictive performance is to reduce r-square. Hence, if the European discharge data reflect a

164

more precise classification of patients, we would expect greater gains in r-square from the DRGs to the Refined DRGs compared to the U.S. experience.

In conclusion, while a number of factors probably contribute to the lack of improvement in predictive performance with the Refined DRGs, it is likely that the major contributor is misclassification of the European discharges due to limited reporting of the additional diagnoses. Developers of alternative patient classification systems have all found diagnoses other than the principal to be extremely important factors in their conceptual and empirical models of the disease and treatment process [l l-151. Moreover, inclusion of additional diagnoses in the discharge abstract provides a more precise clinical description of the patient. Since coding of hospital discharges tends to improve through increased use and review of the data, the Refined DRGs should be viewed as a mechanism for encouraging more complete reporting of comorbidities and complications.

References

1 Fetter, R.B. and Freeman, J.L., Diagnosis Related Groups: Product line management within hospitals. Academy of Management Review 11 (1986) 41-54.

2 DesHamais, S., Kobrinski. E., Chesney, J., et al., The early effects of the prospective payment system on inpatient utilization and quality of care, Inquiry, 24 (1987) 7-16.

3 Guterman. S. and Dobson, A., Impact of the Medicare prospective payment system for hospitals, Health Care Financing Review, 7 (1986) 97-114.

4 Guterman, S., Eggers, P.W., Riley, G., et al., The first three years of Medicare prospective payment: An overview, Health Care Financing Review, 9 (1988) 67-77.

5 Health Systems Management Group: DRG Refinement with Diagnostic Specific Comorbidities and Complications: A Synthesis of Current Approaches to Patient Classification. Final Report, 3 Vols., Health Care Financing Administration Cooperative Agreement Nos. 15-C-98930/1-01 and 17-C 98930/l-0251. Health Systems Management Group, School of OQanization and Management, Yale University, 1989.

6 Palmer, G.R., Freeman, J.L., Fetter, R.B., et al.. International Comparisons of Hospital Usage: A Study of Nine Countries Based on DRGs, Health Systems Management Group, Yale School of Organization and Management, Yale University, New Haven, 1989.

7 National Center for Health Statistics, E.J. Graves: Utilization of short-stay hospitals, United States, 1985. Vital and Health Statistics, Series 13, No. 91. DHHS Pub. No. (PHS) 87-1752. Public Health Service, Washington. U.S. Government Printing Office, May 1987.

8 Aas, I.H.M., Freeman. J.L., Palmer, G.R., et al., The Making of Norwegian DRGs, Norwegian Institute for Hospital Research, Trondheim, 1989.

9 Lichtig. L., Hospital Information Systems for Case Mix Management, John Wiley and Sons, New York, 1986.

10 Cohen, B.R., Pokras, R., Meads, M.S., et al., How will Diagnosis-Related Groups affect epidemio- logic research? American Journal of Epidemiology, 126 (1987) 1-9.

11 Gonella, M.D., Hombmok, M.C. and Louis, D.Z., Staging of disease: A case-mix measurement, Journal of the American Medical Association, 251 (1984) 637-644.

12 Young, W.W., Incorporating severity of illness and comorbidity and case-mix measurement, Health Care Financing Review (supplement), 23-31. 1984.

13 Horn, S.D., Sharkey, P.D. and Bertram. D.A., Measuring severity of illness: Homogeneous case mix groups, Medical Care, 21 (1983) 14-30.

14 Horn, S.D. and Horn, R.A., The computerized severity index: A new tool for case mix measurement, Joumal of Medical Systems, 10 (1986) 73-78.

I5 Mendenhall, S., DRGs must be changed to take patient’s illness severity into account, Modern Health Care, 14 (1984) 86-88.