Tracing the Missing Link between Nursing Workload and Case Mix Groups: A Validation Study

Fall/Automne 1993

Volume 6, No. 3

Tracing the Missing Link between Nursing Workload and Case Mix Groups: A Validation Study by Stuart Halpine and Shelagh Maloney

F

or several years, Canadian hospitals have been using Case Mix Groups (CMGs*)and Resource Intensity Weights (RIws*)to compare themselves with other hospitals. In applying the lessons of this analysis, one major difficulty is that CMG groupings of cases do not reflect departments or work processes in the hospital. The Workload Information Systems for Hospitals Project initiated by the Hospital Medical Records Institute (HMRI)in April 1989 addressed this issue. Between April 1989 and March 1990, nursing workload information was collected on the abstracts of over 40,000 patients in five Ontario hospitals to examine the relationship of CMGs and RIWs to workload in nursing departments. Collection of nursing workload was seen as a pilot for the collection of other global dimension service components on the HMRI abstract. ~~

Two project goals were identified - to create a data base for inter-hospital nursing workload comparisons and to assess the validity of CMGs and RIws as categories for nursing workload. Inter-hospital comparisons were not appropriate, however, because data collection protocols were not uniform across project hospitals. This study reports on the suitability of CMGs and RIWs, not for inter-hospital nursing workload comparisons,but for program planning and nursing resource utilization within a given hospital. In Canada, the "dimensions of the hospital product"' are often defined in terms of CMGs, and hospital funding may be partially determined by CMG-defined case mix. CMGs isolate groups of cases with similar clinical characteristics and similar patterns of resource utilization. The RIW is a CMG-specific relative measure of expected costs. Each CMG has

~

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been assigned an RIW value based on the average Canadian length of stay (LOS) for the cases within the CMG and the cost per day experience from New York State. An RIW value of less than one indicates that the case required fewer resources than average while an RIW greater than one indicates more resources than average were required to treat the patient. For example, the RIW values for CMG 13 (stroke) and CMG 215 (angina) are 2.93 and 1.02, respectively. This indicates that stroke patients require almost three times as many hospital resources as patients treated for angina.

Purpose and objectives The reason for the CMG focus is that HMRI wanted to evaluate the validity of CMG-based reports for summarizing and reporting nursing workload. The goal of CMG development has been to account for as much variability as possible within the Canadian LOS distribution, where LOS serves as a proxy for resource utilization. One would expect nursing workload to be closely related to LOS. Nevertheless, the shift or the 24hour day has typicall been the level of aggregation for nursing workload rather than patient LOS. Also, because CMGs are determined by physician diagnoses and medical procedures, they may be insensitive to the workload required for nursing.' Three facets of validity were examined: the robustness of mean CMG workload differences across measurement systems, the power of CMGs to reduce or explain variance in nursing workload estimates, and the correlation of CMG nursing workload estimates with the nursing cost component of the RIWs. In the first facet of analysis, robustness, or stability of CMGs with respect to different estimates of nursing workload, was assessed with anticipated care estimates from the Medicus and GRASP nursing workload measurement systems. Correlation analysis was used to evaluate the robustness of CMG workload differences within the two measurement systems.

Y

The second facet of the validity analysis compared variance reduction in nursing workload estimates with variance reduction in LOS. In this analysis, the mean for a given category is used to predict the LOS or nursing workload of cases in that category. The success of a set of predictors (the category means) is indexed by the variance reduction statistic (R2) from least squares regression analysis. To the extent that they index different and homogeneous levels of resource intensity, CMGs will be found to be effective in reducing LOS or nursing workload variance in the patient data base. Nursing is a major component of hospital resource utilization, but less homogeneity for nursing workload estimates could be due to several factors. For example, nursing workload accounts for severity of illness and self-care deficits not addressed with medical 20

diagnoses4 Workload estimates may also reflect administrative and physical constraints in the hospital environment to a higher degree than other costs. Because CMGs are widely used to evaluate LOS differences in acute care settings, it was felt that if CMGs account for as much or more nursing workload variance as LOS variance then it would be appropriate to group nursing workload by CMG. Finally, the third facet of analysis focused on CMG nursing workload differences and their relationship to the proportions of corresponding RIWs that are attributable to nursing costs. In this analysis, the correlation across CMGs between a nursing cost weight, derived from the RIW, and nursing workload (in the Canadian data base) was calculated. In this way, it was possible to assess the convergence of the RIW estimates of nursing cost with Canadian estimates of nursing workload.

Data collection Five hospitals were selected to participate in the project. Selection of project sites was based on four criteria: use of Medicus or GRASP nursing workload measurement system; prompt submission of data to HMRI; proximity to researcher location; and willingness to participate in the project. Four teaching hospitals and one large community hospital were selected to participate in the project. In three of the five sites, nursing staff used the GRASP5 system to predict nursing hours while the Medicus' nursing workload information system was used at the other two sites.

To collect totals of nursing hours for each patient, nursing staff recorded each day's hours of predicted nursing care on the patient's chart. This information was tallied and recorded on the HMRI abstract by Health Records personnel. Nursing hour estimates did not include time in the operating and recovery rooms or in the labour and delivery rooms. The two Medicus hospitals collected workload information hospital-wide from the beginning of the project. In the Medicus sites daily point values, which are coded instead of hours, were recoded to hours. This conversion followed a standard procedure established for the hospital when the nursing workload measurement system was implemented.7 Because of reporting difficulties, data from a GRASP teaching hospital were excluded from the data base. The remaining GRASP sites started to capture workload information only for particular nursing units and slowly expanded their collection as the project progressed. Assignment of a CMG to each patient record was done using the HMRI CMG 1990 grouping software.8 The LOS trimpoints, used to identify outlier cases, Healthcare Management FORUM

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Table 2: Error Edits (Counts) by Hospital

Table 1:Typical Cases and Exclusions by Hospital

Hospital: Hospital:

A

B

C

LOSflrirn

Transfer

Signout

Death

Hrsnrirn

Low Vol CMGs Edits

3,326

10,983

9,199

7,548

31,056

10.7% 67.7% 152 10.4% 3.1% 145 5.4% 3.0% 16 10.1% .3% 111 15.2% 2.3% 388 36.2% 7.9% 291 14.7% 5.9% 481 29.4% 9.8%

35.4% 83.9% 551 37.6% 4.2% 530 19.8% 4.1% 64 40.5% .5% 144 19.7% 1 .I% 211 19.7% 1.6% 560 28.3% 4.3% 42 2.6%

29.6% 69.4% 550 37.5% 4.1% 1,785 66.8% 13.5% 44 27.8% .3% 367 50.1% 2.8% 267 24.9% 2.0% 771 38.9% 5.8% 274 1 6.8% 2.1%

24.3% 79.3% 214 14.6% 2.2% 212 7.9% 2.2% 34 21.5% .4% 110 15.0% 1.2% 205 19.1% 2.2% 360 18.2% 3.8% 838 513% 8.8%

76.2%

3%

No err

Hrsrrim

1,467 3.6% Lost1

2,672 6.6% LOS-1 158 .4% Zeros

C

D

1,635 4.0%

were from the 1990 reference LOS data base. The HMRI reference LOS data base is calculated every year to provide LOS information for patients who are "typical" for a CMG (i.e., those patients who have completed a full and successful course of treatment in a single institution). Thus, deaths, acute care transfers, sign-outs (patients leaving hospital against medical advice) and long-stay cases are not "typical" cases. "Nontypical" cases were also excluded from the nursing data base. A minimum of 20 cases from either the GRASP or Medicus sites had to be associated with each CMG. Totals for exclusion criteria by hospital are summarized in Table 1. In addition to standard HMRI exclusion criteria, three error edits were applied to the data base. Cases were excluded from the nursing data base if the project days (across nursing units) differed from the HMRIcalculated LOS by two or more days. This usually resulted from the transfer of patients from one nursing unit where nursing workload was collected to another nursing unit that was not participating in the project. The second edit was applied to cases with exceptionally high totals of nursing hours. Consistent with the

388

211

367

110

1,071

36.2% 7.9%

19.7% 1.6%

24.9% 2.0%

19.1% 2.2%

2.6%

105

14

71

64

254

41.3% 2.1%

5.5%

28.0% .5%

25.2% .7%

.6%

727 61.6%

1,181

.I%

372

28

54

31.5% 7.6%

2.4% .2%

4.6% .4%

4

-

.I%

1,071 2.6% 1,982 4.9%

4,081 12,832 12,716 8,478 38,067 10.6% 33.7% 33.4% 22.3% 93.4% 82.3% 98.1% 95.9% 89.0%

2.0%

732 1.8%

4,910 13,085 13,257 9,521 40,773 12.0% 32.1% 32.5% 23.4%

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B

Row Total Row Total

Typicals

A

D

-

2.9%

7.6%

149

47

200

74.5% 1.1%

23.5% .5%

.5%

4,910 13,085 13,257 9,521 40,773 12.0% 32.1% 32.5% 23.4% way HMRI treats outlying LOS values, cases with outlying totals for nursing hours were trimmed. Finally, cases were excluded from the data base if nursing hours were reported as zero. A total of 31,056 cases f-emainedin the nursing data base once the exclusion criteria and error edits were applied (11,811 surgeries, 10,959non-surgical cases and 8,286 obstetrical cases). These cases were assigned to 276 CMGs. Totals for these error edits are reported in Table 2.

Ana l ysis The first facet of analysis, the analysis of robustness, focused on the pattern of mean nursing hours (NHRS) by CMG in the GRASP and Medicus data sets. Figure 1,in which mean NHRS for Medicus and GRASP systems are compared over the first 60 CMGs, shows similar patterns of nursing workload across CMGs. Over the 117 CMGs with more than 20 typical cases from each system, the correlation between Medicus and GRASP means was .92. This high correlation was found despite some differences in nursing intensity (nursing hours per day) between the GRASP and Medicus hospitals in the data base. Table 3 shows the top 20 nursing intensity CMGs for GRASP and Medicus hospitals. On the Medicus list are 8 heart surgery CMGs and 2 transplant CMGs (very severe cases occurring in low volumes). The highest nursing intensity for non-surgical cases was found in a CMG for high severity neonates. In contrast, the high nursing intensity CMGs on the GRASP list predominantly reflect the activity of the one community

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Figure 1:Average NHRS by CMG - Medicus and GRASP Measurement Systems

Avg. NHRS

-

120

Medicus

+GRASP

0 CMGs

-

Figure 2: Average NHRS by CMG Medicus (sorted) vs. GRASP

Avg. NHRS

120 +.

Medicus

+GRASP

0 CMGs

hospital in the project. For this hospital, high nursing intensity is associated with short LOS and higher case volumes. A consistent pattern of differences in GRASP or Medicus mean estimates was not readily apparent. In Figure 2, which is sorted by Medicus mean nursing hours, there is no consistent difference between the mean Medicus nursing hours and the mean GRASP nursing hours across the 60 CMGs associated with the largest NHRS totals. Nevertheless, for 10,874 GRASP typical cases, the average nursing workload was 20.38 22

hours, and, for 20,182 Medicus typical cases, it was 22.00 hours. An analysis of variance (ANOVA) of Medicus/GRASP nursing hour differences was carried out in which CMG mean NHRS computed across pooled cases from both workload systems was specified as the covariate. The difference between these mean values was statistically significant (F = 12.16, p < 0.00). Thus, the results indicate a stable pattern of differences across CMGs but the magnitude of NHRS estimates varies slightly by measurement system. However, it is Healthcare Management FORUM

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Table 3: Top 20 Nursing Intensity CMGs (with Counts by Hospital) for Medicus and GRASP Hospitals

CMG

Nursing Hours/ day NHRS

LOS

Cases: Hospl

Hosp2

Medicus 310 85 179 177 181 182 644 180 500 2 183 178 84 645 195 187 194 779 503 519

LIVER TRANSPLANT TONSADEN PR(EX T &/A ONLY) CORONARY BYPASS, NO CARD CAT CRD VLV PR W PUMP, NO CRD CAT OTH CARD-THORAC PROC, NO PUM OTH CARD-THORAC PROC, W PUMP NEO,WT>2500G,MIN SRG,M.MJ.PR CORONARY BYPASS, W CARD CATH KIDNEY TRANSPLANT CRANIOTOMY PROCEDURES CC MAJ RECONSTR VASCULAR NOCC CRD VLV PR W PUMP W CARD CAT TONSIL AND/OR ADENOIDECT ONL NEO,WT>2500G,MIN SRG,1 MJ.PR AM1 WITH CARDIOVSC COMPLICTN OTH VASCULAR PROCEDURES AMI, NO CARDIOVSC COMPLICATN PERSONLTY/IMPLSECNTRL DlSOR KID/UR/MJ BLAD PROC, MALGNAN URINARY TRCT INFECT, >70 CC

8.64 8.51 7.57 6.92 6.64 6.56 6.32 6.14 6.07 5.87 5.78 5.78 5.47 4.67 4.66 4.58 4.52 4.51 4.41 4.38

338.98 15.81 100.87 103.77 61.36 79.97 47.96 133.90 120.06 98.02 82.73 153.25 11.96 22.1 7 57.54 30.79 45.67 56.32 78.84 29.23

9.28 9.19 7.51 7.32 6.49 6.33 6.32 6.15 6.01 5.78 5.59 5.58 5.53 5.49 5.45 5.41 5.34 5.13 5.10 5.06

90.48 98.91 19.23 9.64 19.88 9.63 21.10 12.89 15.18 25.51 12.15 24.17 49.74 12.40 15.58 50.96 14.67 87.80 13.25 16.37

39.25 1.86 13.33 15.00 9.24 12.20 7.58 21.79 19.79 16.71 14.31 26.50 2.19 4.74 12.34 6.72 10.10 12.49 17.86 6.67

24 21 111 28 71 46 24

14 129 10 1 15 45 10 13

81 48 90 36 48 83 25 103 54 59 27 8

GRASP 179 177 96 813 647 846 146 274 757 137 454 187 125 648 93 194 149 2 22 88

CORONARY BYPASS, NO CARD CAT CRD VLV PR W PUMP, NO CRD CAT LARYNGOTRACHEITIS DRUG REACTION, AGE48 NOCC NEO,WT>2500G,NO S RG,MNlOTH P SURGCL AFTRCARE, NO HST MALI BRONCHITIS/ASTHMA, AGE<70 NOC HERNIA, AGE48 VRL ILUFVR-?-ORIGIN, <18 NOC SMPL PNEUMON/PLEURSY, 4 8 NOC MINOR SKIN DISORD,AGE <70 NOC OTH VASCULAR PROCEDURES MAJOR CHEST PROCEDURES NEO,WT>2500G,NO SRG,NO PROB OTlT MEDl & UP RSP INF AGE <7 AMI, NO CARDIOVSC COMPLICATN RESP SIGNS/SYMPT, AGE<70 NOC CRANIOTOMY PROCEDURES CC SEIZURE & HDACHE AGE<70 NOCC OTHER ENT PROCEDURES

difficult to interpret this result because, in this small sample of hospitals, workload measurement systems may be confounded with hospital characteristics. In facet two, results from regressions of LOS on mean CMG LOS were compared with results of regressions of NHRS on mean CMG NHRS. Analyses were calculated with the separate GRASP/Medicus data bases and with the pooled data. The variance Gestion des soins de sante

-

9.75 10.76 2.56 1.32 3.06 1.52 3.34 2.10 2.52 4.42 2.17 4.33 9.00 2.26 2.86 9.42 2.75 17.11 2.60 3.23

76 29 249 41 49 4 579 42 65 60 13 14 12 27 103 64 27 141 12

44 2

10 52 10 9 16 1 35 11 48

reduction (R’) in the three regression analyses is reported in Table 4. For the regression with pooled LOS/NHRS values, the LOS and NHRS R2 statistics were almost equivalent, .60 and .61, respectively. Thus, CMGs were found to perform equally well for the two measures of hospital resource utilization. These results and results from

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FalUAutomne 1993

Table 4: R2 for Medicus and GRASP Separately and Together GRASP Pooled Medicus Trimmed Hours Trimmed LOS Cases

.56 .52 (14451)

.52 .44 (8502)

.61 .60 (31056)

the separate Medicus and GRASP regressions are presented in Table 4. In the Medicus and GRASP analyses, the predictors were LOS and NHRS means for CMGs with 20 or more cases within each workload measurement system. R2 statistics for the separate regressions are not as high as the R2 for the regressions with pooled data. That R2 for NHRS is higher than R2 for LOS in the separate analyses may be attributable to the similarity of estimation methods within each measurement system and to hospital-specific characteristics. The third and final sets of analyses focused on the correlation of workload measurements and the nursing component of the RIW. In the New York State cost data base used to calculate CMG-specific RIWs, costs were broken down by functional centres such as nursing, drugs, laboratory and radiology. The nursing costs were assigned intensity weights by a panel of nurses.9 Weighted nursing costs and other functional centre costs were adjusted for New York/HMRI reference data base LOS differences. In the RIW, these costs were standardized to the mean value of cases in the HMRI data base." The nursing cost weight then represents the average CMG nursing per diem cost, determined from the New York cost data base, multiplied by the mean CMG LOS (as per the HMRI 1990 reference data base): Nursing weightcw = RIW nursing per diemcm LOSCMG

Thus, the nursing component of the RIW reflects the Canadian LOS experience. The correlation between mean NHRS and the nursing component of RIWs was calculated with the pooled data set and the separate Medicus/GRASP data sets. The correlation, across CMGs, of mean NHRS with the nursing RIW was .93. Correlations for the Medicus and GRASP data sets were .92 and .87, respectively.

Discussion Difficultiesflimitationsof global dimension (NHRS) reporting Summary statistics related to the edits applied to nursing workload data highlighted quality issues that may arise if these data are routinely collected across hospitals.

24

Currently, hospital protocols for collecting nursing workload vary, as does the adequacy of nursing workload validation procedures." The variability observed in edit rates for pilot hospitals is an indication, then, that differencesin hospital management, automation systems and commitment to data quality all contribute to measurable differences in the accuracy of the data. Because of these same factors, administrators must also be wary of intra-hospital comparisons over time. Problems related to collecting good data inevitably require two data quality approaches: a system of edits and well-defined standards/data collection protocols. These approaches are related; the better defined the standards, the more rigorous the edits can be. Even with uniform data quality standards across hospital environments, studies designed to yield generalizable estimates of hospital behaviours are likely to require data edits such as those used in this study. Thus, it should be recognized, that, while energy may currently be spent on gaining consensus for uniform standards, additional energy will be required to create and maintain an edit system, with mechanisms for identifying and correcting data errors. Even after collection procedures are in place, accurate, reliable and useful data are the result of a sustained collaboration of trained hospital staff and the central data collection agency. CMG validation with NHRS data Despite results pointing to a small but statistically significant difference in workload estimates with the Medicus and GRASP measurement systems, supporting earlier work by O'Brien-Pallas,12 this study found that CMG NHRS differences were robust across the two workload measurement systems. It is not surprising that CMGs, which by design group cases with different LOS, also isolate similarly different totals of NHRS. Thus, factors specific to the hospital working environment or to differences among caregivers do not obscure a basic pattern of nursing workload differences delineated by case mix and LOS differences. Furthermore, even if the magnitude of the workload estimates varies by workload measurement system, results in this study indicate that the pattern of differences across CMGs does not. Given the stability of the workload differences, differences in the two measurement systems may be related to how nursing intensity is estimated. While nursing intensity is often a function of the severity of an illness, results from the project hospitals also suggest that it is a function of hospital-specific characteristics or to the role the hospital plays in the community or the metropolitan area. Some studies have shown that patients' evaluations of their hospital care is strongly related to perceptions of the nursing care they r e ~ e i v e d . l ~Thus, - ' ~ some Healthcare Management FORUM

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hospitals may invest more nursing resources in care related to patient satisfaction, whereas others may allocate nursing resources to high-profile services which may treat a relatively small proportion of the hospital’s patients. Factors related to nursing intensity, including patient severity and hospital organization, may explain the inconsistent patterns of nursing found with different measurement systems.16 However, it is not fair to argue that, because CMGs do not capture this type of variability, they are unsuitable as categories for nursing workload. Results in this study show a consistent pattern of workload differences across CMGs in both GRASP and Medicus estimates. These results highlight the robustness of CMG-related resource differences and the need to separate issues related to within-CMG analysis (e.g., few CMGs, hundreds of cases) from between-CMG analysis (e.g., many CMGs, thousands of cases).17 Regression results in this study make it clear that it is as legitimate to use CMGs to categorize patients’ nursing workload as it is to use CMGs to categorize patients’ LOS. With GRASP and Medicus cases pooled in the same analysis, equal proportions of variance were accounted for with NHRS and LOS CMG estimates. With typical cases, CMGs explained 60 percent of the NHRS or LOS variance. For the separate GRASP or Medicus regressions, the higher predicted, or explained, variance in the NHRS than the LOS analyses may be attributable to the homogeneity of workload estimates, which may not be the same as actual workload, for patients within particular CMGs. With either set of regression results, CMGs performed as well, if not better, with NHRS as with LOS. The last facet of the analysis, correlating the nursing cost weight from the RIW with the mean NHRS estimates by CMG, supported the validity of CMGs as well as the utility of the corresponding RIWs. Just as there is an expected LOS and an expected cost (or cost weight) corresponding to each CMG, there is also an expectation implicit in the use of the RIW that a certain proportion of the case cost is attributable to nursing. This study found that CMG NHRS estimates, for a small and non-random sample of hospitals, were highly correlated with average CMG nursing costs in the New York data base. Thus, for high volume CMGs in a given hospital, variability in hospital means, NHRS or LOS, is likely to be small relative to the variability of resource utilization estimates across CMGs. With these groups of cases, administrators may find the nursing component of the RIWs useful in assessing the allocation of nursing resources.

FalVAutomne 1993

that constitute a hospital’s “product line.” For nursing departments, the determination of staffing requirements has been the most important application of nursing workload information. Nevertheless, an initial argument for developing workload measurement systems was that, when the nursing component of the total budget is reasonably assessed, nursing administrators can more fully assume fiscal responsibility and be held accountable for their contribution to the cost of hospital services.’* Results in this study are encouraging for the administrator who would like to relate departmental expenditures to CMGs, RIW weighted cases and the hospital’s cost per weighted case. Most of the factors complicatingnursing workload across hospitals are absent from an analysis of a given hospital, and, in this context, the pattern of resource differences captured by CMGs and RIWs is as stable for nursing as for total hospital resources. Implicit in the RIW case weight is an estimate of the resources attributable to nursing. A comparable dollar estimate can be derived by multiplying the nursing share of the RIW by the hospital’s cost per weighted case (i.e., the inpatient budget divided by the total inpatient weighted cases). RIW-derived estimates of nursing costs for a CMG, a ward or the hospital can then be compared with actual nursing costs (salariesplus overhead) and nursing workload. For administrators, the value of this analysis is two-fold it links nursing costs with specific hospital “products” or services and it provides an additional perspective on cost and workload targets. Nursing cost weights, the percentage of the full RIW attributable to nursing costs, corresponding mean NHRS, and related statistics for 276 CMGs are available from the authors. For a more detailed ”snapshot” of the distribution of hospital nursing resources across CMGs, the hospital’s own nursing workload can be collected via the HMRI abstract and compared with nursing cost weights. This information, and the ”product line” perspective may lead to more efficient or effective use of patient care services; it may also be a valuable addition to the strategic planning process. CMGs and RIWs may thus hasten the day for a budget analysis of hospital outputs.

References and notes 1. Lave, J.R. and Lave, L.B. 1984. Hospital cost functions. Annual Review of Public Health 5: 193-213.

Implications for managers

2. Giovannetti, P. 1985. DRGs and nursing workload measures. Computers in Nursing 3: 88-91. 3. Halloran, E. and Kiley, M. 1985. The nurses’ role and length of stay. MedicaZ Care 23(9): 1122-1123.

Canadian hospitals have been able to gauge the cost of inputs, but it has been difficult to use this information to estimate the cost of outputs - the types of cases

4. Misener, T.R. and Biskey, V.P.. AVGs: the ambulatory side of DRGs - opportunity or nursing retrofit? Nursing Economics 14(1):36-43.

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5. Meyers, D. 1978. GRASP: A Patient Information and Workload Management System, Morganton, N.C., M.C.S. 6. Medicus Canada. 1985. The Medicus Experience Based Staffing Methology, Toronto. 7. Peat, Marwick, Stevenson & Kellogg. 1989. The Medicus Nursing Management Information System: System Description. 8. Giovannetti, P. and Mayer, G. 1984. Building confidence in patient classification systems. Nursing Management 15(8):31-34. 9. Anon. 1987.1983Service Intensity Weight Enhancements: Computation and Categorization, Randolph, N.J., Network, Inc. 10. Botz, C. 1991. Principles for funding on a case mix basis: construction of case weights (RIWs).Healthcare Management Forum 4(4): 22-32. 11.Price, K. and Lake, E. 1988. ProPAC’s assessment of DRGs and nursing intensity. Nursing Economics 6(1): 10-16. 12.01Brien-Pallas,L. 1987. Analysis of Variation in Nursing Workload Associated with Patients’ Medical and Nursing Diagnosis and Patient Classification Method. Doctoral dissertation, University of Toronto. 13. Hays, R., Larson, C., Nelson, E., and Batalden, P. 1991. Hospital quality trends: a short-form patientbased measure. Medical Cure 29(7):661-667.

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14. Hays, R.D., Nelson, E., Rubin, H.R., Ware, J., and Meterko, M. 1990. Further evaluations of the PJHQ scales. Medical Care 28(9):S29-S39. 15.Babakus, E. and Mangold, W.G. 1992. Adapting the SERVQUAL scale to hospital services. Health Services Research 26(6):767-786. 16. Fetter, R.B., Thompson, J.D., Ryan, J.F., Diers, D., Freeman, J.L., Lo, S.H., and Newbold, R.C. 1987. Diagnosis related groups (DRGs) and nursing resources. Final Report to the Health Care Financing Administration, New Haven, Conn., Yale University, Health Systems Management Group. 17.Cromwell, J. and Price, K. 1988. The sensitivity of DRG weights to variation in nursing intensity. Nursing Economics 6(1): 18-26. 18. Reitz, J.A. 1985.Toward a comprehensive nursing intensity index: part I, development. Nursing Management 16: 21-30. T M G and RIW are registered trademarks of HMRI.

Stuart Halpine, PhD, is a Researcher, Hospital Medical Records Institute, Don Mills, Ontario. Shelagh Maloney, BSc, is a Project Consultant, Hospital Medical Records Institute, Don Mills, Ontario.

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