The development of cost information by DRG - experience in a Barcelona hospital

Health Policy, 17 (1991) 179-l 94 0 1991 Elsevier Science Publishers

B.V. (Biomedical

179

Division) 0168-8510/91/$03.50

HPE 00395

The development DRG - Experience

of cost information

by

in a Barcelona hospital*

Pere Ibern’, Josep Bisbel’ and Merck Casas2 f lnstitut Municipal d’Assist&xia Mkdica al Personal Municipal and 21nstitut Municipal de la Sal&, Ajuntament de Barcelona, Spain Accepted

2 December

1990

Summary The determination of hospital patient costing adjusted by case-mix is one of the possible applications arising from the emergence of patient classification systems, such as Diagnosis Related Groups (DRGs). Most of the experiences up until now have given priority to the determination of the total costs by DRG, making it difficult to establish comparisons between hospitals. In this article, results of an experience in Barcelona with a direct patient costing model are presented, with these later being grouped Into DRGs. Descriptive information is given together with statistical analysis of the intra-DRG variability and the relationship between costs and the length of stay. Patient costing systems represent a useful instrument for hospital management as they allow analyzing variation and its reasons at DRG level. It is in fact the primary level of information required in order to develop management control in hospitals. Diagnosis related group (DRG); Hospital cost accounting; Case-mix cost accounting; Patient costing

Introduction Within the framework of research undertaken by the Municipal Institute of Health of Barcelona on ‘Validation and Application of Diagnosis Related Groups (DRGs)

This paper presents the results of a study partially supported 88/t 146). ??

Address for correspondence:

Pere Ibem, PAMEM, Viladomat

by FIS Ministerio de San&d

127, 08015 Barcelona, Spain.

(Exp -

180

to Spanish Hospital Discharges’, this paper presents the results related to costs, the third phase of that study. Analyses of hospital costs carried out prior to case-mix classification systems, lack accurate definition of cost objects that identify the distribution of resources amongst the different inpatients. Among the choices in traditional cost accounting, those of bed-days and discharges (adjusted by the medical service in certain cases) were often found. The pitfalls of this kind of analysis in order to take decisions due to bias of information, gave rise to some doubts in cost accounting as a tool for management in the hospital sector. The development of DRGs has allowed gradual application of new hospital cost accounting systems. Although most studies applied the Yale Cost Model at first, a standard model of total costs by DRG where the DRG is the cost object, recent efforts have been directed towards developing actual costs systems by DRG by taking the patient as the cost object, a methodology also known as ‘Patient costing’

PI. Hospitals have progressively incorporated more and more outpatient and diagnostic services. Due to this, the scope of the cost analysis by DRG is only part of the complexity of hospital activity, whereas the remainder should be the object of further research.

Material and Methods Data bases The data bases required for drawing up a study of costs by patient are of three different types and are shown in Fig. 1. The three basic areas of information are: (1) Discharge Data. This area contains items allowing individualized identification of the medical processes of each inpatient, as well as the information required for assigning each patient to a given DRG. (2) Activities Data. This area contains items allowing individual identification of the number and type of services rendered by cost centre to each patient during the time in the hospital. It is also essential to have information on outpatient activities to calculate relative resources that do not pertain to inpatient services. (3) Accounting Data. In this area, departmental accounting costs should be available in order to assign direct costs to each centre and without any further imputations between centres. With the purpose of guaranteeing the validity of comparisons between institutions, or over different periods of time, it is essential that accounting criteria used in assigning the costs and defining the centres be consistent. These three areas, matched against each other two by two, generate certain areas of inter-relationship that allow determining the final cost information per patient discharge: - Matching discharge data with the activity data, resource consumption by discharge is obtained, expressed in physical units. - Matching activity data with the centre’s accounting information, unit costs by

181

ACCOUNTING .. .. .

Fig. 1. Model of information

relationship.

type of service are obtained. Once the basic conditions of a hospital information system for introducing a patient costing system had been defined, the six hospitals participating in the first phase of the DRG validation study based on the length of stay variable, were asked to take part in this costs phase. Upon analysis of information available, only one of the hospitals offered guarantees for developing the model. This was the ‘Hospital de 1’Esperanqa’ a municipal hospital with 235 beds. The discharge data corresponding to the year 1988 at this Barcelona hospital were chosen (N= 6585 cases). The cost model A cost model is an applied design for the analysis of accounting information where, from general accounting information available (where costs are grouped according to their nature) the costs referring to given objects or products will be obtained. The definition of a cost model should always show: (1) the costs included; (2) the cost object; and (3) the allocation method. The costs included in our model were only the direct ones - those related to the resources being used at each costing centre without any criteria for allocating overhead service centres to inpatient services. Thus, no attempt was made to analyze the total costs, in this way, by avoiding discretionary allocations to centres, a higher degree of reliability would be attained. It was decided to take the patient, rather than the DRG, as the cost object since this allowed a higher level of analysis. Specifically, analysis of the variability of resource consumption within DRGs is only possible from this perspective. Upon

182

having information available on costs on a patient level, it is possible to group them later with the case-mix classification system chosen which, in our particular case, was that of the Diagnosis Related Groups. Actual costs were used - costs actually incurred over the period - and the model includes only standard costs to evaluate the intermediate activity costs, such as Laboratory and Radiology. In the first case, relative value units as drawn up by the College of American Pathologists were used and, in the second, the relative value units of radiology by DRG from the Maryland cost study. The allocation of routine costs - physician and nursing costs - does not include measurement of the different workloads of nurses and physicians related to the patient. Although this would be of interest, there are no satisfactory developments of this kind in this area, and no data were available. The differences between the cost model designed and the one drawn up by Yale University are basically to be found in that the Yale Cost Model [2] attempts to obtain total costs by DRG whereas the Barcelona model considers direct costs by patient and avoids arbitrary allocations between cost centres. Fig. 2 shows our design of the model. From departmental accounting, the only ones chosen were (1) ancillary services, (2) medical services and (3) other

General

Fig. 2. Direct cost model.

Accounting

by

cost

centre

Table 1

1276210 687614

(5) Non-inpatient

58.36%

(Thousands

Ptas.)

(9)+(10)+(1 l)=(7)

costs by bed-day

(11) Assigned 1234057

79420

48.10%

3.10%

30.43%

14.57%

58.36%

3.76%

36.92%

17.68%

373880

costs by bed-day/service

(10) Assigned 780757

(7)+(8)=(4)

(9) Individually

assigned costs

1.99% 60.35%

1.64% 49.74%

42153

costs per patient 1276210

(8) Non-assigned

1234057

48.10%

(7) Assigned

costs per patient

7.13% 100.00%

5.87% 82.41%

150696 2114520

costs

(6) Assigned

(4)+(5)+(6)=(3)

32.52%

60.35%

% Direct costs

26.80%

49.74%

100.00%

2565713

(2)+(3=(l)

(4) Inpatient costs

costs

82.41%

451193 2114520

17.59%

% Total costs

(3) Direct costs

2565713

Ptas

(2) Indirect costs

(1) Total costs

Explained costs

6.44% 100.00%

96.70%

63.27%

30.30%

% Patient costs

6.22%

61.18%

29.30%

100.00%

3.30%

96.70%

% Hospital costs

184

cost centres (Pharmacy and Prosthesis) where there were individual consumption registers. The costs of General Services and those not related to hospitalization such as outpatients and emergencies - are not taken into consideration due to the reasons already expressed. Relative value units (RVU) used by ancillary services are shown in Table 1. In order to assign physician costs, the Heads of Medical Services gave an estimate on the proportion of time dedicated to inpatient activities compared to outpatient ones with the purpose of separating the resources that were to be included in the model. For nursing services, nursing costs by length of stay have been calculated, adjusted in accordance with the hospitalization ward coinciding with the medical service to which the patient was admitted. As stated earlier, this system does not allow any analysis of variability in the nursing workload per patient, due to the reasons mentioned above, and represents a first approach to the nursing resources used. It may be appreciated from the outline of the model that the product of unit cost of the relative value unit, multiplied by the units per patient, and upon adding information from individual consumption registers (pharmacy and prosthesis expressed in monetary units) gives the cost per patient. Once the grouping process in DRGs has been carried out, this offers us the costs for each by Diagnosis Related Group.

Results Global findings The direct inpatient costs illustrated through the model applied, as well as the proportion of global costs incurred during the period, are shown in Table 2. For the hospital under study, the application of the given model represents an analysis of 48% of the total costs incurred in one year. 63% of the total direct costs were allocated by length of stay adjusted in accordance with the service to which it was related, with the staff costs of nurses and physicians, whereas 30% was individually assigned to each patient. The remaining 7% of the total direct costs allocated on a per diem basis corresponds to meals expenses. Table 2 Relative value units

Ancillary service

Activity indicator

OR Lab (payroll) Lab (other) Radiotherapy Radiology ICU Nursing Meals

Time of intervention CAP RVU Test Radiation field Plate (**) Bed day Bed days by ward Bed days

* Anaesthesia included. ** Only used to calculate proportion

of hospital activity.

1x5

Table 3 LOS and direct costs by DRG (untrimmed

data) INDEXLOS

0.260

0.412

1.251

0.791

1.057

0.720

1.393

0.882

0.461

0.854

0.342

0.652

0.176

0.299

1.328

1.255

1.032

0.916

1.241

0.848

1.141

0.788

0.793

0.716

1.094

0.716

1.383

1.328

1.331

0.975

0.840

0.916

0.658

0.825

0.975

0.5”6

0.866

0.808

0.978

0.630

2.161

2.277

0.536

0.537

1.217

1.115

1.547

0.903

1.099

0.751

0.820

0.556

0.563

0.7.16

1.559

0.940

1.127

0.687

1.428

2.371

1.318

1.379

1.195

1.545

0.103

0.661

0.291

0.497

0.626

0.763

0.354

0.5.30

1.514

I.001

0.960

0.636

1.1*0

0.7H6

0.712

0.737

1.325

0.888

0.774

1.336

0.966

1.398

0.989

0.930

0.666

cl. 697

1.308

0.886

0.971

1.219

0.737

0.842

0.881

1.462

0.232

0.541

0.583

0.682

1.553

0.993

1.000

1.000

Table 3 shows the results for 52 DRGs with a number of cases over 30 (untrimmed data). They represent 51% of total discharges for this year. The last line shows the results for the global data set (N=6585). Average direct cost by discharge was 187,318 Ptas and average length of stay was 11.3. The relationship between costs and length of stay Research on the design of the DRGs has made references

to studies giving a

186

strong relationship between the average length of stay and the costs or resources used. The references used took into account the charges and not the costs actually incurred, although a strong relationship between costs and charges was in turn assumed [3]. In the last two columns of Table 3, relative weights by length of stay and direct costs for the DRGs in question are shown. Important differences may be appreciated between both indexes in certain specific groups, for example DRG 209, Major Joint and Limb Reattachment Procedures, where the length of stay index is 1.42 whereas the cost index is 2.37. If we take a close look at Table 4 where the relative weights divided between cost centres are given, we can conclude that the cause of this difference is to be found in the prosthesis being incorporated. A further example, inversely, shows that for DRG 88, Chronic Obstructive Pulmonary Disease, the length of stay index is 1.24 whereas the cost index is 0.84. Of the 52 DRGs with over 30 cases, half have a higher index in costs than in length of stay, and vice versa. Variation explained in costs and lengths of stay In order to understand the variation explained by DRGs according to whether we take cost or length of stay variables, and their logarithmic transformations, analyses were carried out on valid DRGs with over 30 cases; variance reductions as shown on Table 5 were obtained. The reduction obtained in the logarithmic transformation of costs and with the ‘trimmed’ data bases accounts for 33.8%, whereas for length of stay it is 42%. An even higher reduction is obtained in the length of stay variable and in Table 5 shows the results of different calculations. It should be kept in mind that these results have been obtained from just one hospital, and no general conclusions can be attained. Variation between DRGs In order to understand relative resource consumption by DRGs and to analyze the causes of variability between DRGs, tables were also drawn up on direct costs per cost centre as well as on relative weights (surgery, protheses, radiotherapy, intensive care unit, pharmacy, laboratory, medical service) as shown in Table 4. However, this type of presentation does not allow fast conclusions and it is due to this that we have analyzed the average costs per DRG according to whether they were medical or surgical. From the results of weighted regression according to the number of cases in each DRG level, cost functions as shown in Fig. 4 were obtained. Medical DRGs represent a below-average cost level, whereas the surgical DRGs go over average as the length of stay increases. Detailed results may be obtained from the principal author. Variability within DRGs The introduction of patient classification systems taking the patient as the cost object assumes that each group consumes a similar amount of resources, and therefore no high internal variation is to be expected. The first question to be

181

Table 4 Relative weights (untrimmed

data)

analyzed is whether internal variability is higher by costs or by length of stay. Table 6 shows the comparison between the coefficients of variation per DRG according to whether the variable is that of direct costs or length of stay. From here it may be gleaned that the coefficients of variation in costs are inclined to be inferior, particularly in those groups with values below 0.5, in spite of the shortcomings of the coefficient of variation as an indicator given our being faced with diverse distributions of frequency. On a global level, and using the ‘untrimmed’ data base, (N=6585), a coefficient of variation for cost reaches 1.24 (Table 7), whereas with the ‘trimmed’ data base (N=6018) a value of 0.85 is obtained and in no case does the coefticient of variation in any individual group exceed 1. Details for the 52 DRGs with over 30 cases per group are given in the same table.

188

Table 5 Variance reduction.

Data

Base

untrimmed

Trimmed

Untrimmed

Trimmed

Valid DRGs N r= 30

N

Variable

Levels

variance

variance

i

%

f

Reduction

3363

cost

52

3,3796E+lO

3363

LOS

52

107,432

86.728

19.2

3161

Cost

52

1.223E+lO

7,144E+9

41.5

3195

LOS

52

52.60

33.78

35.75

3363

In

(cost)

52

0,592

0.437

26.1

3363

In

(LOS)

52

0.929

0.589

36.5

3161

In

(cost)

52

0.476

0.316

33.8

3195

In

(LOS)

52

0.629

0.478

42.3

2,7997E+lO

17.3

PLOT OF COST UITH ESTA

++____+____+ .__ .- ___+____+____+____+____+____+____+____+____+____+~___+____+____++ +

5600000+ 1 I I

4800000+

+

C 0 1

S T 4000000: S

+

1 1

I

+

1

2400000: 1 Ill 1 1

1

1

1600000+

1

1 11

1 1

1

1

II

1 1

1

1 2 1 11 Ill 22 121 1 1 1 412 45122 3 1 11 800000+ 21231262966 611211 11 2 2 1 198F89606739164217731211 11 1 ] 13R ’ 32D8UNUOG3MCD8BCH6652 ***f***"KN75,, 3 1 I

I

1

11 11

1 1

;L*********pl2 OR****R

+ ++___-+____+__-_+____+____*___+----+____+~___+____+____+__ +____+____+____+____+---++ 0

25

50

75

100 LOS

125

150

175

6585 cases plotted. Regression statistics of COST on ESTA: Correlation .80349 R Squared .64560 S.E. of Est 137850.033 2-tailed Sig. Intercept(S.E.1 11553.0264(2337.0681) Slope(S.E.) 15562.935D(l42.11597)

Flg. 3. Plot variables

costs, LOS at patient level.

200

.DOOO

189

Thousands Ptas

10

Length -

All DRGs

Fig. 4. Cost functions,

+

16

of stay

Medical DRGs

Medical/Surgical

-+-

Surgical DRGs

DRGs.

The ‘trimming’ process, or in other words the exclusion of extreme observations, has thus brought about a reduction in the intra-DkG variability to a considerable degree. The direct costs variable This study of costs on a patient level has allowed an analysis to be made of the statistical behaviour of the direct costs variable. The statistical analyses of the. length of stay variable had shown up its adjustment with a lognormal distribution [4] although the statistical tests carried out on different data bases made it difficult to assert this fact conclusively. On the other hand, some published research work on the cost variable is, in actual fact, based on approximations to cost, obtained from information about charges [5-61. Fig. 5 and 6 show the distributions of frequency of the costs variable after having carried out the ‘trimming’ process and their logarithmic transformation, upon which a normal curve has been superimposed. The Kolmogorov Smirnov Table 6 Coefficient

Coefficient

of variation

by DRG by LOS and costs

of variation

costs

~_

LOS

co.5

11

4

0.5-0.99

29

38

1.0-l .49

12

x

LOW

High >=1.5 Total

0

2

52

52

___~_._.

test on the normality of a variable for the ‘trimmed’ direct costs logarithm proved satisfactory (z= 0.67, p= 0.75). It can therefore be asserted that, for the hospital COST

count 8 155

275 545 509 530 491 469 408 360 295 273 231 198 109 173 119 111 93 82 63 79 53 48 48 38 41 26 27 26 29 12 7 29 12 12 6 14 13 12 11 10 9 14

6

Midpoint 7291.67 21875.00 36458.33 51041.67 65625.00 80208.33 94791.67 109375.00 123958.33 138541.67 153125.00 167708.33 182291.67 196875.00 211458.33 226041.67 240625.00 255208.33 269791 .b7 284375.00 298958.33 313541.67 328125.00 342708.33 357291.67 371875.00 386458.33 401041.67 415625.00 430208.33 444791.67 459375.00 473958.33 488541.67 503125.00 517708.33 532291.67 546075.00 561458.33 576041.67 590625.00 605200.33 619791.67 634375.00 648958.33 663541.67 678125.00 692708.33

:m

:m

:m

:m :m :m :m

:m

2 . ..t....1....+....1....+....1....+....1....+....1 0

Fig. 5. Direct costs variable.

120 240 360 Histogram Frequency

Costs trimmed (k6018).

480

600

101 LCOST count 18 13 30 36 30 43 40 51 58 51 103 137 133 162 142 169 173 192 242 238 240 255 247 283 252 263 239 232 235 217 206 201 199 142 136 115 110 00 84 76 57 47 41 30 37 31 28 16

Midpoint

10.91 10.99 11.06 11.14 11.21 11.29 11.36 11.44 11.51 11.59

:-'I: :::-. ::-. :-:I :-*m :-' :-:I

11.89 11.96 12.04 12.11 12.19 12.26 12.34 12.41 12.49

:-:I I-* I-* I-. :-' :-' :-'I I-::-

0

Fig. 6. Log direct costs variable.

-

. .

80 160 240 Histogram Frequency

320

400

Costs trimmed (Ak6018).

under study and as from the analysis of costs carried out via the model put forth, direct hospitalization costs follow a lognormal distribution.

192

Conclusions The study of costs by DRG is one of the possible applications arising from the introduction of case-mix classification systems in hospital management. Hospital Cost Accounting should be a tool for taking decisions at an executive level, although the instrument’s credibility depends on the validity of its results. The application of a costs model to a given hospital has allowed demonstration of the degree to which it is possible to achieve certain results, and their limitations. The shortcomings of this study should be situated within those corresponding

Table 7 Direct costs by DRG (untrimmed DRG

R”CCOSl

006

32

012

31

014

45

039

165

55 53 060

264

064

56

082

105

088

59

089

74

090 099

41 161

122

34

127

64

130

51

131

113

132

62

134

36

135

40

146

32

167

5,

172

62

174

36

182

36

183

53

184

33

202 204

74

209

103

210

43

219

36

30

222

58

229 231

42 40 76 41 51 a3 44 100 123 211 35 31 31 35 50 58 37 38 43

232 240 241 243 256 294 315 316 318 319 320 331 332 355 362 369 395

data)

6585

94 438 53, 865

458

14.8

384 416 577 97 250 754

11.0 8.3 9.9 2.6 6.6 17.5

2467411 4592569 6070663 9090615 26402559 6470114 14794565 13168782 18014215 9370593 10923400 5501794 2159003, 8454703 11687756 8752805 17465113 6808653 5448117 471,140 13647608 5730777 12954563 6091404 5063104 5522181 4799820 13032326 3859901 45747846 11110939 10420220 7214872 3908150 5713657 7539285 7685850 6071276 12227647 60,297, 16639134 30782389 55246411 6100409 4050274 5146205 7990475 7885068 15880341 3747578 4854873 8000511

74370

11.3

1233492157

859

205 524 840 1224 82, 954 36, 1989 531 962 484 840 683 352 442 781 345 873 629 44, 491 210 1303 382 1661 640 486 264 138 283 304 701 553 1069 354 1496 1075 2301 391

233

2.9 14.1 11.9 15.7 5.2 3.9 2.0 15.0 11.7 14.0 12.9 9.0 12.4 15.6 15.0 9.5 7.4 11.0 9.8 11.1 24.4 6.1 14.1 17.5 12.4 9.3 6.4 17.6 12.7 16.1 14.9 13.5 4.6 3.3 7.1 4.0 17.1 10.8 12.9 8.0 15.0 8.7 10.9 11.2

7.5

STDDCOST

INDEXCOSl

“AXCOST

14814, 134904 165284 160016 122078 56040 23515, 171564 158824 147614 134190 134100 248668 182621 171624 154559 109817 151337 117929 426488 100540 208945 169206 140642 104192 145449 176113 120663 444154 258394 289451 124394 93051 142841 99201 1.37460 119045 147321 138022 166391 250263 261831 1,429, 130654 166007 228299 157701 273799 1012.56 127760 186058

35852.2 77170.1 95960.1 137127.4 62263.3 52144.5 22348.8 332994.4 141725.6 120084.5 72198.7 185104.9 140643.4 272920.4 221825.1 223180.4 113473.7 69514.8 181333.0 92599.2 332716.4 40694.2 275647.2 102634.8 81110.6 62192.7 104867.8 124829.0 78314.8 194574.3 147056.5 178210.2 85900.2 34888.5 128707.0 46330.0 130513.9 60378.6 95241.2 149876.0 104867.3 314711.2 347172.0 153967.5 94459.4 97618.2 210746.9 172940.3 49090.6 46844.9 118919.1 143667.0

0.46 0.51 0.70 0.82 0.39 0.42 0.40 1.40 0.82 0.75 0.49 1.36 1.04 1.08 1.21 1.29 0.73 0.63 1.18 0.78 0.7, 0.40 1.31 0.60 0.5, 0.59 0.71 0.70 0.60 0.44 0.56 0.61 0.68 0.37 0.89 0.46 0.69 0.50 0.64 1.0, 0.63 1.25 1.32 0.8, 0.71 0.58 0.91 1.09 0.18 0.46 0.92 0.76

0.260 1.251 1.057 1.393 0.461 0.342 0.176 1 328 1.032 1.241 1.141 0.793 1.094 1.383 1.331 0.840 0.658 0.975 0.866 0.978 2.161 0.536 1.247 1.54, I.099 0.820 0.563 1.559 1.127 1.42.9 1.318 1.195 0.403 0.*91 0.626 0.354 1.514 0.960 1.140 0.712 1.325 0.774 0.966 0.989 0.666 1.308 0.971 0.73, 0.881 0.232 0.583 1.553

0.412 0.791 0.720 0.882 0.854 0.652 0.299 1.255 0.916 0.848 0.788 0.716 0.716 1.328 0.975 0.916 0.825 0.586 0.808 0.630 2.27, 0.53, 1.115 0.903 0.751 0.556 0.776 0.940 0.68, 2.371 1.379 1.545 0.664 0.497 0.763 0.530 1.001 0.636 0.786 0.737 0.888 1.336 1.398 0.930 0.69, 0.886 1.219 0.842 I.462 0.541 0.682 0.993

240732 310073 478731 563345 518199 265159 272812 2158861 749888 87,279 340938 942664 861616 1538924 1446283 1143269 760823 422072 999735 508713 1762913 250333 1906682 461934 421171 33,789 522030 681091 39411, 1025878 807422 950362 523841 202462 741426 373371 676699 270911 419332 962434 575514 1922098 350,059 649031 49283, 428798 879591 831508 441380 266664 633416 821608

187318

231523.6

1.24

1.000

I.000

5349916

77107

193

to the patient classification system and in those of the data bases available. Both questions could - and should - be the object of improvement. The study has shown us that the information needed to introduce a model such as the proposed one in other hospitals already exists, although somewhat dispersedly. However, efforts should be made to link certain data bases with others on a patient basis, as well as to homogenize their contents. The application of models such as the one presented in this article can be extremely useful for evaluating the relative efficiency between the different service suppliers and for budget allocation to hospitals. Table 8 Direct costs by DRG (trimmed OR0

006 012 014

on 039 055 060 064 082 088 089 090 099 122 127 130 131 132 134 135 148 167 172 174 182 183

N”M

30 31 4, 52 162 52 243 48 98 58 73 38 146 32 60 46 108 59 33 36 28 52 59 3, 35 52 32 71 28

210

39

219

3. 55 ,o 36 72 37 51 83 42 96 112 195 32 27 31 31 ,I 53 33 3, 41

222 229

231 232 2*o 241 243 256 29, 315 316 318 319 320 331 332 355 362 369 395

6018

data)

LOSTOT

LOS

COSTTOT

73 438 491 711 782 196 470 379 973 782 92, 306 1589 423 772 282 735 601 265 339 639 289 680 540 428 464 187 1146 309 1661 515 ,35 232 122 I?? 262 501 553 1069 259 1353 589 1610 280 1.2 456 261 25. 505 68 175 621

2.4 14.1 11.2 13.7 4.8 3.8 1.9 7.9 9.9 13.5 12.7 8.1 10.9 13.2 12.9 6.1 6.8 10.2 8.0 9.4 22.8 5.6 11.5 15.9 12.2 a.9 5.8 16.1 11.0 16.1 13.2 12.8 1.2 3.1 4.9 3.6 13.5 10.8 12.9 6.2 14.1 5.3 8.3 8.8 5.3 14.8 0.4 5.8 9.5 2.1 5.1 15.1

2103127 ‘592569 5591932 ?520398 24949941 620,955 12578545 6412258 13877648 8.9331, 10582.62 3245507 14116502 6290666 0041100 4992738 1493,701 5770427 3488387 3333155 89.9960 469866, 922931, 5210957 ,6,1933 518,392 4277790 11397179 310,168

59596

9.9

4574784b

8527808 8832770 5885343 3522401 3809050 6568604 5652373 6071276 12227647 46,709, 1,632120 18948319 36842526 4318427 2684640 5146205 5213717 4469572 13090969 280028, 3100632 6595065 918099801

STODCOST

cv

,010, 1,81,? 1270.39 11,623 15.012 119326 5176, 13421, 141609 146.36 14,965 85410 96689 196583 13101.5 108538 13828, 9780, 105709 9260, 3196,l 90359 156429 153263 132627 99700 133681 160524 110863 1,115, 2186.5, 259787 107006 88060 10580, 91231 152767 119045 11,321 110645 152118 169181 188936 134951 99431 166007 168184 101581 262094 87281 9355, 160855

17743.0 75915.1 80377.6 107915.4 43913.1 48142.6 8708.6 85899.5 84261.3 73259.7 68509.7 35202.1 39914.1 132845.2 63021.5 87879.9 a1200.7 ,0920., 66525.2 ,101,.5 91524.0 22155.2 99940.9 79002.1 65316.9 52900.3 80169., 91934.0 39758.2 193627.3 71501.9 124142.1 ‘02,,.8 26830.7 3,016.3 26676.8 67111.1 59783.6 94665.2 5953,.5 79324.5 1,3500.2 138?,2.3 83568.5 3UO7.2 96030.2 117382.9 56002.3 30322.4 20468.5 ,880l.O 82447.0

0.25 0.51 0.63 0.?5 0.29 0.40 0.17 0.6, 0.60 0.50 0.4, 0.41 0.41 0.68 0.4, 0.81 0.59 0.12 0.63 0.1, 0.29 0.25 0.6, 0.52 0.,9 0.53 0.60 0.61 0.36 0.4, 0.33 0.18 0.38 0.30 0.32 0.29 0.1, 0.50 0.6, 0.5, 0.52 0.85 0.73 0.62 0.35 0.58 0.70 0.55 0.12 0.23 0.52 0.51

0.246 I.427 1.127 1.381 0.48, 0.381 0.195 0.797 1.003 1.361 1.278 0.813 1.099 1.335 1.299 0.619 0.687 1.029 0.811 0.951 2.305 0.561 1.164 1.60, 1.235 0.901 0.590 1.630 1.11, 1.628 1.333 1.292 0.426 0.308 0.196 0.36, 1.367 1.095 1.301 0.623 1.423 0.531 0.83, 0.88, 0.531 I..92 0.850 0.583 0.962 0.208 0.520 1.529

15,511

133615.0

0.85

1.000

AVOCOST

INOEXLOS

INDEXCOST

MAXCOST

0.145

0.918 0.978 0.75, 0.329 0.852 0.899 0.929 0.920 0.542 0.61, 1.2,s 0.851 0.689 0.878 0.621 0.671 0.588 2.029 0.57, 0.993 0.973 0.842 0.633 0.849 1.019 0.70, 2.819 I.388 1.649 0.679 0.559 0.6?2 0.579 0.970 0.756 0.935 0.702 0.967 1.074 1.199 0.857 0.631 1.05, 1.068 0.645 1.664 0.554 0.59, 1.021

115182 310073 313323 433061 269328 230268 74394 443059 37,925 370241 319855 17,580 210185 591616 328713 395079 390653 205018 265136 223652 549151 150521 380414 346558 293069 256756 343457 429478 198778 1025878 379603 569178 216839 162820 177505 170311 300877 270911 419332 23,092 373708 608307 632735 361780 1,891, 428798 406186 265901 332262 140031 228973 353077

1.000

2762511

0.940 0.80,

194

References 1 Scuteri. J., New directions for patient costing systems, Australian Case-mix Bulletin, 3 (1989) 7-8. 2 Freeman, J.L., Fetter, R.B., Newbold, R.C., Rodrigues, J.M. and Gamier, D., Development and adaptation of a Hospital Cost and Budgeting Model for Cross-National use, Journal of Management in Medicine 1986. 1 (1986) 38-57. 3 Luke, R.D., Dimensions in hospital case mix measurement, Inquiry, 16 (1979) 38-49. 4 Averill, R.F. and McMahon, L.F., A cost-benefit analysis of continued stay certification, Medical Care, 15 (1977) 158-173. 5 Willems, J.L., Muerisse, A., Ret&ens, S., Vleugels, A. and Peers, L., Use of diagnosis related groups for hospital management, Health Policy, 13 (1989) 121-133. 6 Patel, M., Mottaz, A., Blanc, T. and Shenker, L., Study of cost by type of diagnosis in Switzerland, Health Policy, 9 (1988) 167-175.