adjusting planning guidelines for cardiac-care units

Sm. Sk Mcd. Vol. 16. pp. II57 IO 1167. 19X? Prmted in Great Britam. All nghts reserved

0277-9536 82 I I I I57- I IJo3.00 0 CopyrIght 0 I982 Per&mm Press Ltd

ADJUSTING PLANNING GUIDELINES FOR CARDIAC-CARE UNITS*

JAMES GREENBERG’ and ROGER KROPF’

‘Department of Health Science. Brooklyn College. Brooklyn. NY I1210 and ‘Division of Public Health. University of Massachusetts. Amherst. MA 01003. U.S.A.

Abstract-The principal concern of this paper is the development of procedures for adjusting the criteria currently being used for federally-legislated health planning activities. These procedures would enable the planner to account for the demographic. geographic and health-system conditions which cause variations in the need for health-care services in local communities. A case-mix method. hospital chart- abstract data and demographic. geographic and health-system data from New Jersey were used to: create a list of diagnoses eligible for treatment in a cardiac-care Unit (CCU): select a sample of hospitals for study. and conduct a step-wise regression analysis of CCU utilization in these hospitals. It was concluded that CCU utilization was affected by factors such as the in-hospital availability of CCU beds. the type of hospital. CCU-patients’ clinical severity. and the availability of ambulances and mobile intensive care units. Procedures for adjusting planning criteria to account for local conditions have yet to be developed. However. a method for using the types of results presented in this paper to develop such adjustment procedures was presented and illustrated. It is recommended that this method be used to create such adjustment procedures for the planning criteria for all hospital services and hence to assist Health Systems Agencies in rationalizing the distribution of our hospital care.

INTRODUCTION

The National Health Planning and Resource Devel- opment Act of t974 (P.L. 93-641) required that the Secretary of Health. Education and Welfare issue “National Guidelines for Health Planning”. and that Health systems Agencies (HSAs) and State Health Planning and Development Agencies (SHPDAs) util- ize them in developing their plans. In September of 1977. a set of draft standards for resource allocation was issued as part of the Guidelines [I].

Within the next 3 months. DHEW (now DHHS) received more than 55.000 letters concerning those standards. many from residents of small towns and rural areas who feared that they would be adversely affected [2]. The Congress also ,received protests from constituents who feared that inflexible national standards concerning the supply and distribution of health resources would be imposed on their communities. The Congress has since passed amendments to the planning act (P.L. 96-79) that eliminate the requirement that SHPDA and HSA plans be consistent with the Guidelines.

This controversy also led to a recently published report from the Institute of Medicine [3] that affirmed the need for flexible standards and guidelines, and for the development of health information. analytic techniques and improved health planning methods for use in standards and guidelines development.

*This paper is based on research which was jointly funded by the National Center for Health Services Research and the Bureau of Health Planning in the Department of Health. Education and Welfare under Grant No. I R03 HS03538-01 NSS.

In another report. we have described a method for using a source of data only recently available to a few state governments-chart abstract data-to develop guidelines for the planning of Cardiac-Care Units (CCUs) [4]. This present paper focuses on the ques- tion of how such guidelines can be adjusted to fit local characteristics.

It presents and illustrates a powerful. inexpensive method for developing quantitative procedures for achieving this adjustment and recommends that it be used to develop such procedures for all acute-care hospital services.

OBJECTIVES

The principle objective of the resea’rch described in this paper was the analysis of factors effecting the utilization of CCU services. This was to be achieved by using the New Jersey hospital patient chart abstract data base-the Diagnosis-Related-Group (DRG) data base [S].

In addition. this analysis was to be used to investigate the development of procedures for adjusting CCU bed-need criteria to account for the demographic. geographic and health system conditions which cause legitimate variations in CCU utilization and therefore in CCU bed need on the local. area- wide level.

METHODS OF PROCEDURE

A detailed description of the procedures on which this paper is based has been presented elsewhere [4]. A brief summary is provided here.

1158 JAMES GREENBERG and RCGER KROPF

Table I. Categories of diagnoses treatable in the CCU

DICAT No. Diagnoses in DICAT*

I 2

I +2 3 4 5 6

7

l-7 8

Simple Acute Myocardial Infarction (AMI) Complicated AMI AMI (simple or complicated) Intermediate syndrome Rule-out MI Cardiac arrhythmia problems Intensive cardiac conditions treatable in a

regular intensive-care unit Other CCU-eligible cardiac conditions

not often seen in the CCU All CCU-Eligible Diagnoses Diagnoses which are not eligible for CCU treatment

l DlCAT represents diagnostic category

List of’ CCU-eligible diagnoses

A fundamental principle underlying this research is that the need for CCU beds depends primarily on case-mix or diagnosis. A patient with a complicated acute myocardial infarction. for instance. will need a CCU bed whereas a patient with a simple pelvic frac- ture will not. In fact. diagnosis is,crucial to the phys- ician in the practice of medicine. i.e. it is important in decisions concerning the utilization of health care.

In order to use diagnosis as a basis for studying CCU utilization. we required a list of diagnoses which are eligible for treatment in a CCU. Hence the DRG data base and a review of the CCU literature were employed in the development of such a list of CCU- eligible diagnoses [4].

The resulting list of CCU-eligible diagnoses was divided into seven categories. These categories. which were considered distinct in terms of their clinical characteristics and also in terms of CCU resource utilization. are listed in Table I.

Selection c$ho.spituls jbr study

Nineteen New Jersey hospitals were then selected for use in our study. They were selected because they had separate CCUs. rather than CCUs combined with intensive care units (CCU-ICUs), or no CCUs and because of accuracy in reporting CCU treatment in their chart-abstract data [4].

Measured in terms of 8 hospital characteristics. the sample of I9 hospitals was very similar to the whole population of New Jersey acute-care hospitals [4].

Three-phase model c$ CCU utilixtion

For the purpose of statistically analysing hospital- to-hospital variations in CCU utilization. we developed a simple 3 phase model of CCU utilization:

Phuse I : The period between onset of CCU-eligible symptoms and admission to a hospital. From the perspective of CCU utilization, the important variable representing the end point of this phase is the number of hospital admissions (or discharges) with CCU- eligible diagnoses.

Phase 2: The period between admission lo a hospital and admission to the CCU. From the perspective

‘DICAT represents diagnostic category-see Table I.

of CCU utilization, the important variable is the proportion of admitted patients with CCU-eligible diagnoses who are CCU treated.

Phase 3: The period between admission to and discharge from the CCU. This is most appropriately quantified by the average length of stay (ALOS) in the ecu.

Our analysis of CCU utilization considered each of

these 3 phases separately.

Regression unal_wis

The purpose of our statistical analysis was the explanation of hospital-to-hospital variations in the variables representing the end points of each of the three phases (the number of hospital admissions with CCU-eligible diagnoses for phase I ; the percentage of these hospitalized admissions which are CCU treated for phase 2; and the ALOS in the CCU for phase 3) in the 19 selected hospitals.

A regression analysis was used to attempt to statistically link these variables (dependent variables) with explanatory variables (independent variables) for each of the 3 phases. The Statistical Package for the Social Sciences (SPSS) software program [7] was used for this purpose.

As possible independent variables, we selected variables which we thought might be able to explain hospital-to-hospital variations in our dependent variables, and for which we had data (Table 3). Our selection of these possible independent variables was guided by our review of the literature on the CCU I?].

The statistical analysis for each of the 3 phases was performed using two versions of the dependent variable: one for all patients with AM1 (DICATs 1 + 2)*: and one for all patients with any CCU-eligible diagnosis (DICAT l-7). For instance, the two dependent variables for phase 3 were ALOS in the CCU of patients with AMI. and ALOS in the CCU of patients with any CCU-eligible diagnosis. This was done to compare CCU utilization by AMI patients with that by all CCU-eligible patients.

RESULTS OF REGRESSION ANALYSES

Phase I: Onset’of symptoms to hospitul admission

The objective of the statistical analysis of this phase

Adjusting planning guidelines fcr cardiac-care units 1159

was the identification of variables-independent variables-which were strongly related to the hospital- to-hospital variations in the number of patients with CCU-eligible diagnoses admitted to each of the 19 selected hospitals-the dependent variable (Table 2). In addition. it involved the determination of the magnitude. of the effect of each of the independent variables on the dependent variable.

Variables which were considered to possibly be significant independent variables in our regression analysis are listed in Table 3. Among these variables is a set of CCU service-area characteristics which were thought to possibly have an effect on the number of service-area residents suffering a CCU-e!ig- ible episode and then surviving long enough to travel and gain admission to a hospital. i.e. service-area characteristics which were considered to possibly be significant independent variables in phase I of our analysis. A list of corresponding quantitative indicators on which we had data is also presented in this table.

The size of each hospital’s CCU service area was determined by means of a patient-origin study using the patient’s residence code on the chart abstract [4]. This allowed the service area to be defined in terms of population rather than purely geographic parameters. Specifically. the population of each of the state’s cities was allocated to each of the state’s hospitals in proportion to the percentage of each cities’ CCU patients who utilized each hospital’s CCU in 1977.

The regression equations (Tables 4 and 5) both reveal that the size of the CCU service-area population was the most important independent variable we tested. This suggests that the larger a hospital’s CCU service-area population. the larger the number of its admissions with CCU-eligible diagnoses. Considered together with the high simple correlation between CCU service-area population and CCU bed size (Pearson Product Moment Correlation Coefficient.

r = 0.50). this result is in agreement with many studies on hospital bed utilization patterns [9] and also suggests a logical causal nexus: hospitals with large CCUs tend to attract large numbers of CCU- eligible patients and hence have large CCU service- area populations.

The regression equation for admissions with CCU- eligible diagnoses (Table 4) also shows thzt the larger the percentage of the hospital’s CCU service-area population with access to a Mobile Intensive Care Unit (MICU). the larger the number of its admissions with CC&eligible diagnoses.

This is probably due to the MICU’s ability to reduce in-transit mortality and hence increase the hospital admission rate of patients with CCU-eligible diagnoses.

It this proposed explanation is correct. the fact that access to a MICU was not a significant explanatory variable for the number of AMI admissions suggests that the MICU made less of a reduction in in-tran- sient mortality for AMI than for all CCU-eligible patients.

Phase 2: The period herwern admission r~, (I hospirul

and admission to its CCU

The purpose of the statistical analysis of this phase of the CCU-utilization process. phase 2. was the identification of independent variables which effected the proportion of hospitalized admissions with CCU-eligible diagnoses which were treated in the CCU-our dependent variable (Table 61. In other words. the purpose of this phase was the identification of variables which effected the CCU admission criteria used in the 19 selected hospitals.

Table 3 contains a number of variables which were considered to be possible independent variables in phase 2. i.e. variables which were thought to probably have an effect on any particular hospital’s CCU admission criteria.

Table 2. Discharges with CCU-eligible diagnoses in each of the 19 selected hospitals by DICAT (1977)

No. of discharges with CCU-eligible diagnoses in Hosp. DICAT DICAT DICAT DICAT DICAT DICAT DICAT DICAT DICAT No.* I 23 1+2 3 4 5 6 7 l-7

3 143 99 242 192 II24 415 259 303 2535 I2 242 102 344 201 1679 432 362 535 3553 14 195 I82 377 346 1700 II00 323 872 4718 I6 I46 71 217 248 1373 608 266 463 3175 20 84 85 I69 44 I101 485 179 393 2371 26 121 74 195 II3 922 439 224 417 2310 29 I I9 49 I68 31 1090 490 281 631 269 I 39 261 87 348 298 1735 644 408 827 4260 47 138 60 198 222 944 606 375 314 2659 48 150 43 193 I68 1775 439 336 480 3391 51 264 120 384 145 2015 748 459 762 4513 56 I08 52 160 145 504 I I9 220 203 1351 57 176 54 230 344 1471 513 380 x55 3793 58 35 I7 52 9 613 268 231 275 1448 78 88 44 132 75 987 379 271 566 2410 80 86 78 164 100 1643 598 213 500 521x 85 39 33 72 150 903 I88 142 253 170x 92 78 62 I40 366 1361 520 300 522 3209

112 105 44 149 I22 692 227 299 218 I707

*These numbers were used in our data p!ocessinp [4]

JAMES GREENBERG and ROGER KROPF 1160

Table 3. Possibly significant independent variables used in all three phases of the regresslon analysis

Variable Quantitative indicators Phases

Type of hospital

The extent to which the CCU is filled

Availability of CCU beds

Size of CCU

Availability of ambulances

Availability of a ‘91 I’ telephone service

Availability of mobile intensive- care units

Road density

Urban/rural

Population density

Service-area population socioeconomic status

Service area size

Socio-economic characteristics of Ccl-J-treated patients

CCU patient’s

age

CCU patient’s sex

CCU patient’s race

Diagnostic severity of hospitalized patients with CCU-eligible diagnoses

In-hospital demand for CCU beds

Teaching or non-teaching (VAROL Dl and El)*

Occupancy rate in CCU (VAR03l

No. of maintained CCU beds in the hospital per 100.000 service-area population (VAR04)

No. of maintained CCU beds (VAROS)

CCU service-area population per ambulance squad (VAR06)

Per cent CCU service-area population serviced by ‘91 I’ (VAR07)

Per cent CCU service-area population serviced by MlCUs (VAROS)

Dense. not so dense or sparse (VARO9, D2. D3 and E2)

Mostly urban. urban or mostly rural (VARlO. D4. D5 and E3)

Dense. not so dense or sparse (VARI I. D6. D7 and E4)

CCU service-area per capitu income (VAR 12)

Population of CCU service area (VARl3)

Per capira income of the service-area population (VAR 12)

Percentage of CC&treated discharges with diagnoses in DlCATs 1 + 2 and I-7t which are over 65 years (VAR46 and VAR53)

Percentage of CCU-treated discharges with diagnoses in DlCATs I + 2 and l-7 which are female (VAR66 and VAR73)

Percentage of CCU-treated discharges with diagnoses in DlCATs I + 2 and l-7 which are non-white (VAR86 and VAR93)

Percentage of discharges with diagnoses in DICAT l-7 which have diagnoses in DlCATs I. 2. I + 7. 3. 4. l-4. 5. 6 and 7 (VAR104-VARIIZ)

The number of discharges with diagnoses in DICATs. I. 2. I + 2. 3. 4. l-4. 5. 6. 7 and 1-7 per CCU bed , (VAR143-VAR152)

1. 2. 3

1.2.3

I. 2. 3

I

I. 3

1.3

1.3

I. 3

1. 3

1.3

I. 3

I

2

2. 3

2. 3

2

2

2

*The names in parentheses were used in our data processing. tSee Table I for a description of DICATs.

The regression equations show that both the pro- related to the CCU occupancy rate (/I = -0.61 for portion of hospital-admitted patients with CCU-elig- discharges with all CCU-eligible diagnoses-Table 7;

ible diagnoses, and the proportion of hospital-admit- and /? = -0.55 for discharges with AMI-Table 8). ted patients with AMls. were strongly and negatively In other words. CCU admission criteria in the 19

.

Adjusting planning guidehnes for cardiac-care units I161

Table 4. Phase I repression results for discharges with CCU-eligible diagnoses

Dqwtldrrlr rtrriuhlr VAR23 No. of admissions with DICAT I-7 diagnoses

Independrnr rctriuhles VAROI Service-area population serviced by a MICU VAR13 Service-area population

Rrgrcwim srurisric.~

Multiple r 0.7X367 F = 12.7. d.T. = 16.2 rz 0.61415 Adjusted rL 0.5659 I SE 745. I X726

Variable B /I SEofB F

VARO8 Il.77715 0.37946 4.82500 5.958 VAR13 O.IXXl027D-01 0.668 I2 0.0043x 18.470 (Constant) 946.3743

Note: All f values are significant at the 5”,, level.

Table 5. Phase I regression results for discharges with AMls

Dqrndtwt ruriuhlr VAR16 No. of admissions with DICAT I + 2 diagnoses

Indrpotdettt wriuhles VAR13 CCU service-area population

Rryrrssion sttrtisrics

Multiple r 0.82391 F = 35.9. d.f. = 17. I r2 0.67883 Adjusted r’ 0.65994 SE 55.64678

Variable B P SE of B F

VAR13 O.l957043D-02 0.x2.19 I o.OQO33 35.931 (Constant) 25.66X34

Note: All F values are significant at the I”,, level.

Table 6. Percentage of CCU-treated discharges in each of the I9 selected hospitals by DICAT (1977)

Per cent of discharges with CCU-eligible diagnoses in the following DlCATs which was CCU treated

Hosp. DICAT DICAT DICAT DICAT DICAT DICAT DICAT DICAT DICAT No.* I 2 I+2 3 4 5 6 7 l-7

3 I2 I4 I6 20 26 29 39 47 48 51 56 57 5x 7X x0 85 92

II2

SD

49.0 29.3 40.9 25.0 4.4 12.3 I I.2 I .o 10.2 84.3 70.6 80.2 63.7 22.2 21.3 Il.9 2.6 26. I 64. I 54.9 59.7 45.7 I I.1 16.5 8.0 I.5 16.X 45.9 49.3 47.0 27.0 5.8 13.x 7.9 I .7 I I.4 83.3 63.5 73.4 50.0 I I.5 12.0 7.x 0.x 14.7 85.1 66.2 77.9 69.9 12.4 16.4 14.3 I .4 19.7 x3.2 75.5 XI.0 51.6 16.X 26. I 31.3 4.8 21.6 34.9 25.3 32.5 13.1 I.7 3.7 2.9 0.7 5.2 79.0 X6.7 81.3 53.2 I I.9 21.1 6.7 I .o 20.6 82.7 X6.0 x3.4 69.0 Il.3 25.7 22.9 4.x 20.4 6 I .O 40.8 54.7 39.3 1 I.2 15.1 4.8 1.3 14.1 63.0 50.0 5x.7 46.2 10.5 I x.5 9.1 3.4 19.5 59.7 79.6 64.3 61.3 7.x 29.x I x.4 2.0 IX.8 77.1 76.5 76.9 77.8 25.6 34.3 15.6 2.9 23.5 85.2 X4.1 92.4 72.0 10.3 26. I 22. I I.9 I x.2 73.3 75.6 74.4 29.0 5.1 14.5 12.7 0.4 6.9 64.1 66.7 65.3 43.3 3.4 20.2 15.5 0.x 12.0 73.1 91.9 x I .4 61.7 IO.8 41.9 24.7 2.3 ‘4.6 43.x 56.X 47.7 27.0 7.1 12.x 17.1 2.3 13.9

68.0 64.7 67.0 48.7 10.6 20. I 13.9 2.0 16.8 15.8 19.3 16.6 18.5 6.0 9.0 7.5 1.3 5.8

*These numbers were used in our data processing [4]

1162 JAMES GREENBERG and ROGER KROPF

Table 7. Phase 2 regression results for discharges with CCU-eligible diagnoses

Dependent curiahlc VAR 103 T,, Discharges with DICAT l-7 diagnoses which are CCU treated

Indepvndcnt ruriuhles VAR03 CCU occupancy rate VAR04 No. CCU beds per 1000 service-area population VAR93 % CCU-treated discharges with DICAT l-7 diagnoses which are

non-white VAR152 No. of discharges with DICAT l-7 diagnoses per CCU bed

Regression statistics Multiple r 0.90208 F = 15.3. d.f. = 14. 4 r’ 0.81375 Adjusted rz 0.76053 SE 2.84901

Variable B B SEofB F

VAR03 -0.1504566 -0.61051 0.03179 22.400 VARO4 -0.7788721 -0.75818 0.26584 8.584 VAR93 0.7521701 1.00574 0.17517 18.439 VARl52 -0.2086798D-03 -1.04611 0.00004 32.682 (Constant) 40.90463

Note: All f values are significant at the 5% level.

hospitals under study were tighter in hospitals with high-occupancy CCUs and vice-versa.

This appears to be cogently explained by the following hypothesis: cardiologists in hospitals with high-occupancy CCUs are often left with little choice but to adopt stringent CCU admission criteria because their CCUs are full most of the time. Cardio- logists in hospitals with low-occupancy CC&. on the other hand. are able to use more liberal CCU admission criteria because there are usually some empty beds in their CCUs.

This is not to suggest that CCU practice in the 19 hospitals was clinically inappropriate in 1977. The lack of clear evidence on the clinical efficacy of CCU treatment [S] gives the cardiologist professional license to be flexible in the practice of CCU medicine. In other words, the lack of specific. proven clinical criteria for the need for CCU treatment requires the cardiologist to exercise judgment and flexibility in determining CCU admission criteria.

It is quite conceivable that this flexibility allows for variations in CCU admission criteria large enough to explain the relationship between CCU occupancy rates and admission criteria observed in Tables 7 and 8. This does suggest, however. that the CCU admission criteria in the 19 hospitals were not cost-effective, i.e. they did not admit only diagnoses for which the CCU has been proven to be the least expensive. clinically effective mode of treatment.

Another similar result is that the proportion of hospitalized discharges with CCU-eligible diagnoses which was CCU treated was negatively related to the number of CCU-eligible discharges per CCU bed for both AMIs and all CCUeligible discharges (/l = -0.48 for discharges with AMIs-Table 8; and fi = - 1.05 for those with all CCU-eligible diagnoses-Table 7).

This suggests that CCU admission criteria in the 19 hospitals were more stringent in those hospitals with large numbers of eligible patients for each CCU bed.

Table 8. Phase 2 regression results for discharges with AMls

Dependent ruriuhle VAR96 9; Discharges with DICAT I + 2 diagnoses which are CCU

treated

Indqwndrnt vuriuhlcs

VAR03 CCU occupancy rate VAR86 “,, Ccl-)-treated discharges with DICAT I + 2 diagnoses which

are non-white VARl45 No. of admissions with DICAT I + 2 diagnoses per CCU bed

Regression stutistics Multiple r 0.89162 F = 19.4. d.f. = 15. 3 r2 0.79498 Adjusted r’ 0.75398 SE 8.21931

Variable B B SE of B F VAR03 - 0.3837978 -0.54715 0.082 15 21.825 VAR86 1.464285 0.49606 0.345 17 17.996 VAR145 -0.353519313-02 -0.48413 0.00086 17.090 (Constant) 101.4198

Note: All F values are significant at the I”/. level.

Adjusting planning guidelines for cardiac-care units 1163

It seems plausible that this result can be explained by the same hypothesis offered to explain the strin-

gent CCU admission criteria in hospitals with high CCU-occupancy rates. Hospitals with large numbers of admissions with CCU-eligible diagnoses per CCU bed would tend to have ‘shortages’ of CCU beds. As discussed above. shortages of CCU beds could tend to encourage cardiologists to adopt stringent CCU- admission criteria.

The proportion of hospitalized discharges with CCU-eligible diagnoses which was CCU treated was negatively related to the number of CCU beds per 1000 population (fi = -0.76 in Table 7). Although this might appear to contradict the findings just discussed, it should be noted that the number of CCU beds per 1000 service-area population is not a measure of the availability of CCU beds within a hospital. In fact. this result does not appear to represent any plausible. generalizable characteristic of a CCU system. In addition. while it is significant at the 0.05 level. the number of CCU beds per 1000 service-area population is not as strong an independent variable as the others. as shown by the F scores in Table 7 and is not significant when only discharges with AMI are considered (Table 8).

Tables 7 and 8 also show that the proportion of discharges with CCU-eligible diagnoses which is CCU treated is positively related to the percentage of CCU-treated discharges with CCU-eligible diagnoses which are non-white (/l = 1.01 for discharges with all CCU-eligible diagnoses. and fi = 0.50 for discharges with AMI). This suggests that hospitals with more stringent CCU admission criteria tended to treat smaller proportions of non-white patients with CCU- eligible diagnoses in their CCUs than did hospitals with more liberal CCU-admission criteria and vice- versa.

Phase 3: Acemye length of stay in the CCU

The analysis of this phase of the CCU-utilization process, phase 3. attempted to identify the important independent variables which were related to hospital- to-hospital variations in CCU average length of stay (or discharge criteria) in the 19 selected hospitals (Table 9).

Almost all variables used as potentially important independent variables in the first two phases were tested as independent variables in this phase (Table 3).

This is because the phase I variables are service- area characteristics and the phase 2 variables are hospital characteristics and we felt that the average length of stay (ALOS) in the CCU could well be effected by the number and type of patients who reach a hospital. as well as conditions in the hospital after arrival [IO].

The resulting regression equations (Tables 10 and 11) show that ALOS in the CCU was positively related to CCU service-area population per ambulance squad (/3 = 0.44 for discharges with any CCU- eligible diagnosis and 0.35 for those with AMI).

In other words, ALOS in the CCU was higher in hospitals whose CCU service areas had fewer ambulances. This could possibly be due to the extra long CCU treatment needed by patients suffering from long delays between the onset of symptoms and the initiation of CCU treatment in areas with, a relative shortage of ambulances.

ALOS in the CCU for both AM1 patients and for all patients with CCU-eligible diagnoses was also positively related to the percentage of CCU-eligible. CCU-treated patients who have AMls (fi = 0.91 for discharges with any CCU-eligible diagnosis- Table 10 and 0.74 for those with AMI-Table 11).

This suggests a positive relationship between dis-

Table 9. Average length of stay in the CCU (days) in 19 selected hospitals by diagnostic category (1977)

Hosp. DICAT DICAT DICAT DICAT DICAT DICAT DICAT DICAT DICAT No.* I 2 1+2 3 4 5 6 7 l-7

3 4.77 12 4.82 14 IO.22 16 4.50 20 8.10 26 4.44 29 3.00 39 5.87 47 5.33 4x 4.00 51 4.80 56 4.77 57 4.15 58 3.59 78 3.66 80 5.80 85 6.20 92 4.73

I I2

Mean SD

4.36

5.1 I I .63

7.48 5.78

IO.09 7.35 9.00 3.87

7.60 5.82 5.81 6.16 7.61 5.89 5.84 2.00 5.96 5.00 5.77 6.12

6.28 1.75

5.58 4.82 5.77 4.91 4.23 7.00 5.25 5.07 3.20 2.87 3.08 3.06 2.57 3.60

IO.16 8.67 7.41 8.39 7.36 6.55 8.65 5.48 3.34 3.69 4.15 3.85 3.14 4.38 8.49 4.09 3.46 4.01 2.85 2.33 5.36 4.26 3.31 3.09 3.37 3.34 2.66 3.58 3.00 2.00 2.50 2.66 12.00 t 3.75 6.19 3.89 3.51 4.04 4.33 2.80 5.00 5.49 3.65 3.20 3.61 5.20 1.00 4.14 4.41 2.62 2.05 2.89 2.38 2.04 2.87 5.1 I 3.64 3.15 3.30 2.95 2.70 3.85 956 4.55 3.67 3.77 4.50 3.28 4.63 4.64 2.93 2.77 3.50 3.31 2.66 3.40 4.32 3.57 2.71 4.70 3.22 1.50 3.48 3.25 t 3.11 I.81 4.00 1.00 2.75 5.88 4.34 3.39 3.59 4.55 1.00 4.43 5.63 3.67 3.61 3.92 5.18 7.00 4.35 5.25 2.99 2.89 4.00 3.83 2.33 3.64 4.9x 2.78 2.71 3.41 3.03 2.00 3.54

5.40 3.75 3.45 3.83 4.38 2.98 4.25 I.59 1.36 I.19 1.29 2.11 I .86 I .26

*These hospital numbers were used in our data processing [4]. tValues were not able to be calculated for these elements because of poor coding of length of stay in the

CCU on the chart abstract.


Table IO. Phase 3. regression results for discharges with CCU-eligible diagnoses

Drpmdmf curiuhlu VAR133 ALOS in the CCU of discharges with DICAT l-7 diagnoses

Indrpmdmt cariuhlrs VARC6 Service-area population per ambulance squad VAR135 4, CCU-treated discharges with DICAT 1-7 diagnoses which

have DICAT 1 + 2 diagnoses VAR142 “/, CCU-treated discharges with DICAT l-7 diagnoses which

have DICAT 7 diagnoses Dl Dummy variable for teachmg hospital

Rryrrssion .stutistics

Multiple r 0.87146 f = 11.1. d.f. = 14. 4 IJ 0.75944 Ajdusted rz 0.6907 1

SE 0.71794

Variable B B SE of B F

VAR06 0.4761034D-03 0.44288 0.00015 9.768

VAR 135 0.3670648 0.90892 0.06949 27.903 VAR 142 0.4346523 0.36993 0.20128 4.663 DI 2.386449 0.69256 0.49921 22.852 (Constant) - I.600391

Note: All F values are significant at the 5”; level.

Table Il. Phase 3 regression results for discharges with AMls

Drpendenr ruriuhlr VAR126 ALOS in the CCU of discharges with DICAT I + 2 diagnoses

lndrprndrnt curiuhlrs

VARO6 Service-area population per ambulance squad VARl35 “” CCU-treated discharges with DICAT l-7 diagnoses which

have DICAT I + 2 diagnoses Dl Dummy variable for teaching hospital

Rryrrssion .stutistic’s

Multiple r 0.84203 F = 12.2. d.f. = 15. 3 r2 0.70902 Adjusted rz 0.65083 SE 0.96562

Variable B B SEofB F VAR06 0.4768735D-03 0.35043 0.00020 5.787 VAR I35 0.3773 184 0.73808 0.07266 26.966 DI 2.407482 0.55193 0.64773 13.815 (Constant) 0.3037301

Note: All F values are significant at the 5”,, level.

charge criteria and clinical need in the 19 CCUs because AMIs generally exhibit longer lengths of stay than do other CCU-treated diagnoses (see Table 9 for example).

ALOS in the CCU for patients with AMI and for those with all CCU-eligible diagnoses were positively related to the dummy variable for teaching hospital (/l = 0.69 for discharges with any CCU-eligible diagnoses-Table 10; and 0.55 for those with AMI- Table I I).

This indicates that ALOS in the CCU was greater in the teaching hospitals than in the non-teaching hospitals in the group of 19 hospitals. This could re-

flect the fact that the more critically-ill CCU-eligible patients. who needed to stay in the CCU longer, tended to be taken to teaching hospitals for CCU care. However. the proportion of patients in DICATs 1 and 2 was not statistically significantly different in

teaching than in non-teaching hospitals (f-test. 0.10 level). This requires further investigation.

The regression equation for discharges with CCU- eligible diagnoses indicates that the percentage of CCU-treated, CCU-eligible discharges which had diagnoses in DICAT 7 was positively related to the dependent variable (/l = 0.37 in Table 10). One poss-

ible explanation for this finding is that there was a subgroup of the 19 hospitals which had a high proportion of CCU-treated patients with diagnoses in DICAT 7 and which also had a high ALOS in their ecus.

LIMITATIONS OF THIS STUDY

While these results are consistent with the literature on in-patient care in general and CCU utilization in particular, the analyses could have probably been


even more revealing and significant had the following types of current data been readily available:

Chart-abstract data of higher quality. These would have provided a larger sample of hospitals for study, and hence allowed for results of higher cogency;

chart abstracts of New Jersey residents who were CCU treated out of state. Even though the lack of these data did not appear to detract from the validity of our results [4], their use would have added an extra dimension of completeness to our study;

reliable information on the efficiency and operating procedures of local ambulance systems. For instance, information on how these systems decided to which hospitals to transport CCU-eligible patients would have helped us develop a more comprehensive under- standing of the process via which CCU-eligible admissions occured;

current data on population socio-economic and demographic status at the minor civil division (MCD) level would have provided us with a means to more thoroughly investigate the factors effecting the need for CCU treatment;

reliable information on travel times in local areas in New Jersey would have enabled us to calculate travel times in the various CCU service areas and hence to investigate the effect of these travel times on CCU utilization and hence on CCU bed need;

incidence rates on all CCU-eligible diagnoses together with data on the proportion of the population with hospital coverage in each MCD would have allowed for a study of survival rates (to a hospital) of patients with CCU-eligible diagnoses who could afford hospitalization. This would have enabled an investigation of the effectiveness of local MICU and ambulance systems;

information from the hospitals, chart-abstracting services and the hospital association in New Jersey on the reliability and quality of chart-abstract data in individual hospitals in the state. This would have helped us to choose a larger sample of hospitals for study, and hence to have achieved more significance and certainty about our results and conclusions.

Information on the reliability and quality of chart- abstract data is probably the single most important missing item, and its availability would undoubtedly have added significantly to the’strength of our results. It is recommended that this information be made available in future to aid in the development of auth- enticated methods for planning. monitoring, reim- bursing and regulating the health-care system.

DEVELOPMENT OF PROCEDURES FOR ADJUSTING NEED CRITERIA

The final version of the resource standards issued by DHEW on 28 March 1978 provide HSAs and SHPDAs with guidance on making adjustments to the guidelines and criteria used in their planning activities. The purpose of this guidance is to help planners account for the demographic, geographic and health-care system conditions which cause legitimate variations in health-care needs on the local level.

In its guidance. DHEW stipulates that HSAs must make adjustments to take into account the special needs and circumstances of HMOs, services available

to local residents from Federal health care facilities and higher minimum and maximum standards estab- lished for state certificate-of-need and related pro- grams. Conditions which may justify adjustments in the standards for specific services are also pointed out. The standards for the supply of general hospital beds. for instance. may be adjusted to reflect a higher percentage of elderly people, seasonal population fluc- tuations, the need to make services accessible in rural areas and patient travel patterns within SMSAs and to referra; hospitals.

An adjustment may also be made if. after detailed analysis, the HSA concludes that residents of the area would not have access to necessary health services. significantly increased costs to a substantial number of patients would result, or care would be denied to persons with special needs resulting from moral and ethical values. A detailed justification for the adjustment and documentation of the circumstances that are the basis for the justification must appear in the HSA’s plan.

These factors reflect legitimate concerns about the impact of national standards on local communities and we believe that the type of analysis reported here could help provide planners with part of the ‘detailed justification’ and ‘documentation of the circumstances’ being sought by the Federal government.

Consider, for instance, the following four CCU bed need criteria which our review of HSA planning documents and the planning literature showed to be fre- quently used as planning and certificate of need criteria [4]:

No. of CCU beds per 100 general-care beds. No. of CCU beds per 100,000 service-area popula-

tion. No. AM1 discharges per 1000 service-area popula-

tion. No. CCU discharges per AM1 discharge.

These criteria do provide a means of accounting for some factors which could cause an extraordinary, but legitimate need for CCU beds in a particular local area.

The first one, for instance, bases its estimate of bed need on the number of general-care beds in the area under consideration. This accounts for the fact that large numbers of general-care beds are usually associated with large service-area populations. large numbers of admissions and large numbers of CCU- eligible patients needing a large number of CCU beds.

The second and third criteria yield estimates of CCU bed need which are based on the size of the service-area population. This accounts for the fact that hospitals with large service-area populations generally have need for large CCUs.

There are many other local demographic. geographic and health-care system factors which the above four criteria do not take into account.

For example. they only account for one of the factors revealed by our regression analysis as having a significant effect on CCU utilization. and hence on CCU bed need-service-area population. They provide no means of accounting for the other factors identified as significant. For instance. none of them account for the fact that the supply of ambulances is


associated with longer CCU lengths of stay and therefore greater CCU bed need.

Assuming that this factor causes legitimate variations in CCU bed need. planners would ‘need an adjustment procedure to take it into account. This adjustment procedure could be created if, for instance, separate empirical values of CCU bed-need criteria were developed for service areas with large, moderate and small supplies of ambulances. The case-mix method described here is well suited for this purpose.

In using this approach to develop adjustment procedures, it will be important to determine whether an observed variation in utilization is legitimate. The fact that utilization tends to be higher in hospitals with greater availabilities of CCU beds (see above), for instance, is not a legitimate variation in utilization. This is because it is not clinically indicated, nor has it been shown to lead to cost-effective CCU treatment. On the other hand. the fact that CCU utilization tends to be increased by the availability of MICU facilities (see above) probably is a legitimate variation. This is because-there were very few MICU facilities in New Jersey in 1977 [4] and there is reason to believe that they do improve the survivability of emergent patients [ 123.

The case-mix approach is well. suited for establishing legitimacy because, as mentioned above, diagnosis is important in establishing the legitimate clinical need for treatment. The statistical analysis reported in this paper, for instance, did not consider any discharges with diagnoses in DICAT 8 (see Table 1) because DICAT 8 represents diagnoses which are not considered by clinicians to be appropriately treated in the CCU.

The case-mix method we recommend for developing procedures for adjusting planning guidelines for CCUs to reflect local characteristics consists of the following 6 steps:

(1) Select a sample of CCUs and a suitable time period for study.

(2) Acquire the following data concerning these selected CCUs: the type of services available in each of the ecus; the type of services available in the hospital containing each of the CCUs; the socioeconomic and demographic characteristics, admitting and discharge diagnoses, location of residence. discharge status, admission date, length of stay in the CCU, other intensive care unit and hospital, and units within which the patient was treated; these data to be col- lected for each patient treated in each of the selected CCUs or admitted with a CCU-eligible diagnosis to each of the hospitals containing one of these CCUs.

(3) Use data on the residence location of each patient treated in each of the selected CCUs to conduct a patient origin study to define the service area of each of these CCUs.

(4) Collect data on factors in each CCU’s service area which could have an impact on utilization in the CCU. These factors include the geographic and morphological nature of the CCU service area, as well as the socioeconomic and

demographic characteristics of the population within the area. They also include the type and number of health facilities which could have an impact on CCU utilization in the CCU’s service area.

(5) Use data on patient’s admitting and discharge diagnoses and socioeconomic characteristics to distinguish CCU utilization which is legitimate from that which is not. If. for instance, it is found that only 75 of 100 patients treated in a particular CCU had diagnoses requiring CCU treatment. the utilization of CCU services by these 75 patients would be legitimate, whereas that of the other 25 would not be. The input of experienced cardiologists and a thorough review of the literature on CCU utilization is essential to the successful differentiation of legitimate from not legitimate utilization.

(6) Conduct statistical analyses using the data described above to determine which patient, CCU, in-hospital or CCU service area characteristics are related to legitimate CCU utilization in the CCUs in the sample. This should include a determination of the quantitative magnitude, as well as the direction, of the relationship between each of these characteristics and CCU utilization. These relationships can be expressed in the form of an algebraic equation by means of multiple regression techniques. If this were done, the magnitude and sign of each regression coefficient would represent the magnitude and direction of the relationship between the independent variable (patient, CCU, in-hospital or CCU service area characteristic) associated with that regression coefficient, and the dependent variable (CCU utilization). It is also possible to express these relationships by means of other multivariate statistical techniques, such as analysis of variance. The input of experienced cardiologists and a thorough review of the literature on CCU utilization should be used to ensure that the resulting statistical relationships are plausible representations of the actual CCU utilization process.

These types of empirical quantitative relationships between patient. CCU, in-hospital or CCU service area characteristics and legitimate CCU utilization would allow the planner to adjust CCU planning guidelines for local conditions.

Assume, for instance. it is found that there is a positive relationship between CCU utilization and the difference between the proportion of a CCU service area’s population over the age of 65 years and the national average of say 13% for this proportion. Assume. also, that CCU utilization has been found to increase lo/; for each 1% increase in this proporion above the national average. If the national guideline for CCU planning is 1 CCU bed per 100,000 population and the proportion of a particular local area’s population over the age of 65 years is 23”,/,, the guideline should be increased 10% to 1.1 CCU bed per 100,000 population in this particular local area.

The method described here could be used to develop adjustment procedures for any factor which has an important effect on legitimate CCU utilization.


These adjustment procedures would allow planners to account for the unique conditions which occur on the

emphasize the fact that the planner should adopt a system-wide approach that accounts for all com-

local level and cause bed need which is not ordinary ponents of the health-care system which interact with or average. the service for which plans are being developed.

This same case-mix method could be used to develop procedures for adjusting the need criteria for all other hospital services.

A large sample of CCUs. representing the types of local demographic. geographic and health-care system conditions for which adjustment procedures are desired would be needed to ensure an adequate amount of chart-abstract data for the statistical analyses that would be required in the development of the adjustment procedures.

The analysis also revealed that the average length of stay in the CCU was longer in hospitals with greater proportions of patients with AMI. suggesting that CCU discharge criteria were indeed related to clinically defined need.

Average length of stay in the CCU was found to be higher in teaching than in non-teaching hospitals. It seems important to explore this issue further because of the high cost of care in teaching hospitals.

CONCLUSIONS

There is an urgent need for planners to have empir- ically based procedures for adjusting need criteria to account for factors which cause extraordinary but legitimate need on the local. health service area level.

In conclusion. it is extremely important for planners to be able to account for conditions which cause legitimate variations in bed need on the local level and it is hoped that our work will aid in the development of techniques for this purpose.

REFERENCES

The case-mix method described here is a powerful, inexpensive method for the development of these procedures for CCUs and also for other acute-care hospital services.

1.

2.

It is recommended. therefore. that the case-mix method be used with chart abstracts from large samples of CCUs in several different parts of the country to develop such procedures to allow planners to be able to account for factors local conditions. It is recommended that this be done for all other acute- care hospital services.

3.

4.

The statistical analysis of CCU utilization described in this paper showed that the admission criteria in the 19 hospitals were looser in hospitals with lower CCU occupancy rates and a relative abundance of CCU beds; and tighter in hospitals with higher CCU occupancy rates and relative shortages of CCU beds.

5.

6.

This finding suggests that cardiologists in- the 19 hospitals were adjusting their admission criteria in accordance with the availability of CCU beds in their hospitals, rather than in accordance with rigorous. specific. empirical criteria for clinical need. As discussed above. this does not indicate clinically inappropriate CCU treatment beqause of the lack of specific. proven criteria for clinical need.

7.

8. 9.

However. in concert with some of the recent literature on the CCU. it does imply that CCU practice in some of the 19 hospitals is probably not cost-effective. Hence it is recommended that the cost-effectiveness of current CCU practice be investigated further.

Our findings concerning the influence of ambulances and MICUs on CCU utilization and bed need

10. II.

12.

13.

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Sciences. McGraw-Hill. New York. 1975. Greenberg J. A. er 01.. op. cir.. Chap. III. Aday L. The utilization of health services: indices and correlates. The Final Report of a project Publication No. PB-219 640. funded by the National Center for Health Services Research and Development. Washing- ton. DC. 1972. Greenberg J. A. rt 01.. op. cit.. Chap. IX. Health planning: national guidelines 28 March. Federal Register 43. 1304-1305. 1978. Clipson C. W. and Wehrer J. J. Phrning /br Carditrc

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