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Nurse staff allocation by nurse patient ratio vs. a computerized nurse dependency management system: a comparative cost analysis of Australian and New Zealand hospitals.

DIFFERENT METHODS ARE USED to allocate nurses to staff hospital wards in Australia and New Zealand. Most methods are either unpublished or have been published in non-commercial form. Methods described in the literature may be underpinned by hours per patient day or organizational models for the delivery of nursing care such as primary nursing, patient allocation, task assignment, and team nursing (Twigg, Duffield, Bremner, Rapley, & Finn, 2011; Duffield, Roche, Diers, Catling-Paull, & Blay, 2010). For the purposes of this article, we refer to two nurse staff allocation methods used in various forms in some hospitals in Australia and New Zealand: nurse patient ratios (NPRs) and computerized nurse dependency workload systems. Generally, computerized nurse dependency workload systems incorporate the dimension of patient acuity. The terms patient acuity, nurse dependency, and patient dependency refer to similar phenomenon where patient needs for nursing care are described and/or measured. In some Australian health services, NPRs are part of a formalized system. In other health services, nurse-patient ratios have been adapted by hospital managers internally as a model for setting safe staff levels external to formalized structures.

NPRs are known as occupancy-based nurse patient allocation. They generally ensure the number of nurses matches the number of patients requiring care and that nurses in similar settings care for the same number of patients. In the state of Victoria, Australia, NPRs were mandated in 2000 for the public sectors. They are based on a workload complexity model that incorporates the type of shift of care, such as morning or evening, and the level of the hospital, such as major or minor metropolitan and rural. In the United States, they were mandated in California in 2004.

Differences exist between rules of NPRs for Victoria and California (Gerdtz & Nelson, 2007). In comparison to Victoria, California has been the site for many studies concerning NPRs (Aiken et al., 2010; Chart, Killeen, Vilke, Marshall, & Castillo, 2010; Chapman et al., 2009; Donaldson et al., 2005; Sochalski, Konetzka, Zhu, & Volpp, 2008). A literature synthesis on the impact of Californian-mandated acute care hospital nurse staffing ratios found they reduced the number of patients per licensed nurses and increased the number of nursing hours per patient day in hospitals (Donaldson & Shapiro, 2010). A U.S. national survey of registered nurse perceptions of NPRs showed 62% wanted minimum nurse staff ratios mandated; however, RNs employed in California, where ratios are mandated, were significantly more likely to agree (Buerhaus, DesRoches, Donelan, & Hess, 2009).

Computerized nurse dependency workload systems consider patient dependency requirements for nurses by quantifying variables of direct nursing care. Since 1984, when the Australian Council for Health Care Standards introduced the requirement for hospitals to show evidence of a staffing pattern for nurses according to patient need, computerized nurse dependency systems were developed as a solution. They were neither widely adopted nor extensively integrated in hospitals. In the 1990s, some computerized nurse dependency workload systems used in several hospitals were accessed for data to support development of relative nursing costs. Similarly, for hospitals in many other countries, patient classification models based on dependency measures have been generally accepted to represent actual nursing resource requirements for patients.

But even with general consensus on their utility for nurse staff allocation, today's computerized nurse dependency systems for nurse staffing allocation purposes in acute hospitals vary across nations, states, and settings (Aschan, Junttila, Fagerstrom, Kanerva, & Rauhala, 2009; Sawatzky-Dickson & Bodnaryk 2009; Sundaramoorthi, Chen, Rosenberger, Kim, & Buckley-Behan, 2009; Welton, Zone-Smith, & Bandyopadhyay, 2009). In Sweden, the Zebra and Beakta systems are common in hospitals and are used with general satisfaction by nurse managers (Perroca, & Ek, 2007). A model of intensity measurement also underpins a nurse dependency system for one large 1,500 bed hospital in Singapore (Hoi, Ismail, Ong, & Kang, 2010). Systems of computerized nurse staffing allocation based on dependency models are also used outside acute care, such as community (Brady, Byrne, Horan, Macgregor, & Begley, 2008) and rural health care settings (Harper & McCully, 2007). An earlier U.S. study compared differences in nurse staff allocation systems based on patient classification and variances were shown in nursing hours of up to 4.33 hours per day for the same patient (O'BrienPallas, Cockerill, & Leatt, 1992).

Few studies report nursing costs related to dependency models of nurse staff allocation; though costing methodologies are subject to debate (Chiang, 2009; Welton, Fischer, DeGrace, & Zone-Smith, 2006). Costs related to nursing care intensity have been calculated using point scales such as the Finnish RAFAELA system (Fagerstr6m & Rauhala, 2007). Literature suggests empirical measurement of nurse dependency linked to nursing costs remains a formative science (Buerhaus, 2009, 2010; Welton, 2008; Welton, Zone-Smith, & Bandyopadhyay, 2009). It is timely that the need for nursing data has been listed as a key recommendation in the Institute of Medicine report (2010) as comprehensive data will help understand future nursing workforce needs. This study provides one of the first cost modeling and analyses that compares two nurse staff allocation practices. We include a range of scenarios to model selected parameters related to NPRs and a propriety computer nurse dependency management system.

Methods

This study used statistical analysis and cost modeling of retrospective data obtained from a cohort of electronic nursing workload management reports from hospital information systems. Data were collected over a 4-month period in 2003 and 2004. Hospitals chosen to supply the workload management reports were users of a propriety-owned Computer Nursing Dependency Management System (CNDMS). At the time of the study more than 100 Australian and 14 New Zealand hospitals were system users. This system was chosen as it had capacity to simultaneously measure nursing workload by a dependency method of nursing hours per patient day (HPPD) by various patient types and categories, as well as by NPR. The authors have no pecuniary interest in the CNDMS mentioned in this study.

Patient dependency is measured in the CNDMS as nursing time required to care for specific patient types and for specific dependency categories (Lowe & Dunigan, 2001). The range of patient types amount to over 40 and include, for example, medical, surgical, maternity, labor ward, special care nursery, coronary care, pediatrics, pediatric intensive care, rehabilitation, day surgery, short stay, psychiatric, emergency department, palliative care, and aged care patients. There are five dependency categories that include every aspect of nursing care for every shift. These aspects of care are referred to as indicators. Examples of indicators include mobility, nutrition, hygiene, and thought processes. In addition, the system accounts for other aspects of care that include, for example, nurses' documentation, doctors' rounds, patient enquiries, simple medications, routine teaching, and counseling. The CNDMS is underpinned by more than a decade of development. The timings for patient types and specific dependency categories have been validated every 2-3 years based on user feedback. Functionally, the system allocates a higher category for complex care, depending on the patient type and category. For example, the indicator "mobility" has less time allocated for care of day surgery patient types than for rehabilitation patient types.

The hospitals selected were users of the propriety CNDMS and included 13 Australian hospitals and 8 from New Zealand. Just over 70% of shifts of care in the sample were provided by Australian hospitals and the remainder were from New Zealand. In the sample were 11 private hospitals and 10 public hospitals with 16 located in metropolitan and 6 in rural areas. The sample size was 103,269 shifts of care. Nurses work three shifts of care on a daily basis. Table 1 shows hospital variables based on country, region status and level, and the frequency and percentage of shifts by hospital variables. In Australia, level denotes the size and complexity of hospital services. In the state of Victoria where nurse patient ratios are mandated and fixed according to the hospital level, each hospital has a designated level. For example, a level 1 hospital is a major metropolitan provider of health services.

The units of measure were predicted nursing hours based on HPPD. HPPD refers to the total number of productive hours worked by nursing staff with direct care responsibilities, per patient per day. HPPD is the sum of the total nursing hours worked (or predicted) for 24 hours, divided by the number of patients occupying beds at midnight, plus other separations during the previous 24 hours (discharges, deaths, transfers). The unit of measure "predicted hours" is a function of the propriety system where the nurse reviews and updates indicators during a shift so that hours predicted for nursing care are automatically adjusted to reflect actual hours currently being worked. A feature of the system is that both "predicted" and "actual" nursing care requirements for acute inpatient care are recorded. Predicted nursing hours arise from the input of nurses as they review and update the CNDMS indicators so that hours predicted for nursing care are automatically adjusted to reflect actual nursing hours currently being worked for the patient category.

Several reports from the propriety system were requested from hospitals to capture the required data. The reports included predicted and actualized hours by category and summaries of the number of patients in each category. Statistical tests included descriptive statistics conducted on the raw data where an analysis of the characteristics of the sample was conducted, and costing analysis where the analysis of the costs for the sample was conducted on a range of variables, by both nurse staff allocation practices. Cost modeling techniques appropriate for different scenarios were used to inform the analysis.

Subsets of data were analyzed on scenarios that closely matched Victorian public hospital profiles to ensure equivalence for patient, ward, and hospital types in the sample. Scenarios were developed by examining frequencies of the shifts by patient type. Medical/ surgical wards were most frequent in level 2 Australian metropolitan public hospitals and formed the most common scenario. In other words, our analysis was undertaken using common scenarios to determine if the cost of care provided by the mandated NPR model was a close match with the cost of nursing care required, how this compared with the cost of care measured by the CDNMS and, importantly, if the costs approximated or were close to the "actual" for the hospital types. Analyses of the scenarios for all patient categories and for the different hospital groups would indicate which method performs best at predicting actual clinical requirements. The mean and standard deviation were calculated in each of these cases. The means are in hours per ward/per shift.

Results

Cost prediction comparison. This part of the analysis gives the Australian dollar values to actual inpatient hours (total hospital direct care nursing costs), propriety system predicted hours, and NPR predicted hours. The analyses are all hospitals (see Table 2); all hospitals, by patient type category (see Table 3); Australian, public, metropolitan, level 2 (see Table 4); and Australian, public, metropolitan, level 1 or level 2, by patient type category (see Table 5).

Patient type categories refer to the following six categories: medical/surgical, pediatric, ante/postnatal, adult critical care, non-adult critical care, and other. Forty-one CDNMS patient types were mapped to these six patient type categories. The term hospital level refers to the manner in which hospitals were categorized at the time of the study and were based on the Victorian Department of Health classification of health services. The three levels represent general types of hospital. For example, level 1 hospitals are large and metropolitan, level 2 include smaller metropolitan and large rural hospitals, and level 3 comprises smaller hospitals in both metropolitan and rural settings. We undertook detailed mapping of non-Victorian hospitals to determine equivalence for these levels.

The dollar value is based on an industry-accepted working measure of $40 per nursing hour in Australia for the year 2003. This amount is derived from all nursing wage levels but excludes executive managers. The amount includes compulsory on-costs of superannuation, shift penalties, annual leave, long service leave, payroll tax, orientation, and safety training. It excludes sick leave and other training. Table 2 shows that, for our sample, the CNDMS system under-predicted the total actual inpatient hours by about as much as NPR seemed to over-predict.

There were also several cases where NPR over-predicted the actual total more than the propriety system, and vice-versa. For example, in Table 3, where there is further breakdown in patient type such as medical/surgical and pediatric, variation is shown within the overall sample and the CNDMS is not always lower for different patient types.

Tables 4 and 5 show the analysis undertaken by selected patient type and hospital variables. For example, Table 4 illustrates selected patient types and hospital variables of designated hospital level.

Additional analyses using scenarios. Data were analyzed further to consider four possible scenarios. The mean and standard deviation were calculated for each scenario. The means are presented in hours per ward, per shift. The scenarios are shifts of care where (a) both CNDMS and NPRs over-predict, (b) both CNDMS and NPRs under-predict, (c) CNDMS over-predicts but NPR is satisfactory, and (d) NPR over-predicts and CNDMS is satisfactory. The scenarios were analyzed on a subset of data which we selected through a rigorous process to closely match shifts of care in Victorian public hospitals. Hence the additional cost analysis was conducted on data from Australian public hospitals, for three hospital levels and two regions, with a focus on medical/surgical, pediatric, and maternity patient types.

The parameters selected are where (a) CNDMS predicted less than actual and NPR predicted less than actual, (b) CNDMS predicted less than actual and NPR predicted at least as much as actual, (c) CNDMS predicted at least as much as actual and NPR predicted less than actual, and (d) CNDMS predicted at least as much as actual and NPR predicted at least as much as actual. In other words, the parameters selected are where (a) both CNDMS and NPR "underbudgeted" nurses for care actually required, (b) CNDMS "under-budgeted" for nurses, but NPR did not, (c) NPR "under-budgeted" for nurses, but CNDMS did not, and (d) neither CNDMS nor NPR "underbudgeted" for nurses. In each of these cases, the focus was on (a) the CNDMS variance (actual--CNDMS predicted); (b) the NPR variance (actual--NPR predicted); and (c) which prediction method was closest to actual. To investigate (a) and (b), we considered four scenarios for all patient categories and for each of the patient type categories and for the different hospital groups. Tables 6 and 7 provide analyses of two scenarios.

For all hospitals and all cases, the scenario when both CNDMS and NPR under-predicted actual hours, the average number of hours per ward and per shift that CNDMS under-predicted was 4.0273 hours (see Table 7). This corresponds to $40 per hour or $161 per ward, per shift, on average for this sample. In comparison, NPR under-predicts by 5.4519 hours, or $218 per ward, per shift, on average.

The hours for tables in the additional analysis are reported in terms of "per cent of actual." This is because for some wards, under-predicting by 4 hours might not be a lot whereas for other wards it might be very significant. It is clear that, on average, for all hospitals and wards, when both CNDMS and NPR under-predict actual need, CNDMS under-predicts by 27.8% of actual, whereas NPR under-predicts by 35% of actual.

To determine part (c), "ABS (NPR variance)--ABS (CNDMS variance)" was used, which is simply the difference in the absolute values of the two different variance measures. In Table 7, where both CNDMS and NPR under-predicted actual, NPR under-predicted by 7.2% of actual more than CNDMS did. This means that if budgets are developed on predicted hours, then hospitals using CNDMS would have paid out more (7.2% more) for these cases than hospitals using NPR. If the NPR variance is larger in absolute value than the CNDMS variance, then this measure will be positive. In all but two cases, this number was positive, meaning CNDMS almost always appears to outperform the NPR method for predicting actual clinical need.

Discussion

Strengths and limitations. We pursued national representation by conducting the study with support from many CNDMS administrators spread across Australia and New Zealand with variation in hospital categories and location. The study is based on a large sample of shifts of care.

The analysis focused on cost comparison using selected data from a propriety information system. We do not claim the CNDMS-predicted HPPD are a reliable measure of nursing care requirements. Still, the CNDMS remains accepted in practice as it is used in over 100 Australian hospitals for nurse allocation and related patient management. We have presented a study that compares and models cost variations in two primary nurse staff allocation practices. To our knowledge, there is no published literature that has compared variations between two nurse staffing allocation systems endorsed in practice using cost analysis and modeling.

Interpretation of the results. The findings are that the budgeted cost of nursing care is less if hospitals used the CNDMS for allocation of nurse staffing rather than NPR. We have shown that the CNDMS under-predicts the costs of the actual hours of care by as much as NPR over-predicts those costs. This demonstrates that the budgeted cost of nursing care would be less for hospitals using the CNDMS.

The most important interpretation of the cost analysis is the provision of nursing care at lower cost may be possible through use of a computerized nurse dependency system that allows for staff allocation to meet patients' varying requirements for nursing hours and skill mix. This is an important outcome for knowing costs of nursing care and for distribution of limited nursing resources.

Recommendations for practice and further research. Too few studies are available on the topic of acuity-based nurse costing. We recommend further analyses of nurse costing and development of costing methodologies. Our study suggests that in comparison to NPRs, the CNDMS provides managers with continuous patient dependency data for allocating and predicting staff allocation based on nursing care requirements. It helps managers prepare dependency-based budgets, and provides a tool for staff allocation. The CNDMS measures with detail the complexity of patient needs and their dependency on nurses. It records in electronic form the actual care and predicts the care required for individual patients following each shift. The system has capacity to measure trends in patient dependency and nursing resource consumption efficiency because the system quantities, classifies, and codes nursing care and makes, to some extent, the elements of nursing care visible in an information system. We do not seek to promote a particular computerized nurse dependency system; nevertheless, the system used in this study provided a database where we showed differences in cost for two methods of nurse allocation.

The analysis has increased the technological capacity of a health care information system to show differences in nursing costs between two nurse allocation practices. Computerized nurse dependency management systems may trim costs by assisting unit and other managers to allocate nursing staff by hours and skill mix and may improve nurse staff allocation relative to patient dependency requirements. Nursing and quality managers form an integral part of hospital governance and require local and timely data to inform decision making. This study has identified patient-level nursing workforce and staffing allocation data that can provide quality coordinators with a valuable resource to predict nursing staff requirements. Nevertheless, national implications are that costing measures of nurse workload require development and refinement in Australian and New Zealand public and private hospitals. This research makes a significant contribution to the nursing administrative literature. It has applications for government, businesses, health care organizations, professional associations, and the insurance industry. It furthers understanding of ways in which predictive models have national significance for workforce development.

Conclusions

Over recent decades, computerized nurse dependency systems based on patient classification and acuity systems have developed to support nurse management and, in particular, staff allocation. The study sought to determine if it would cost hospitals more to provide nursing care requirements by NPR practices or by a computerized nurse dependency system. The costing analysis showed that NPR allocation practices were less predictable than CNDMS and were a higher cost compared to similar costs for CNDMS.

Australian public and private hospitals require capacity to attribute direct nursing care costs to patient-level data. The states of Victoria, Australia, and California, United States, have set benchmarks for NPRs and have established, to some extent, their favorable impact on nurse practice environments in acute care; however, information system data structures need development to support nurse staff allocation at the operational level. The computerized systems to transform nurse staff allocation could be NPRs or dependency/acuity based but they need sufficient detail at the patient level to enable accurate nurse costing analyses.

ACKNOWLEDGMENTS: The authors thank Emeritus Professor Donna Diers, Dr. Lee Seldon, Ms. Cherrie Lowe, and Mr. Dean Athan for providing advice and guidance for this study; Dr Kathryn Forbes for assisting with data analysis; and the hospital teams who assisted with data transfer. Part of this research was supported by Monash University.

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LIZA HESLOR PhD, RN, is Professor, Faculty of Health, Science, and Engineering, Victoria University, Melbourne, Victoria, Australia.

VIRGINIA PLUMMER, PhD, RN, is a Course Coordinator, Monash University, Melbourne, Victoria, Australia.
Table 1.
Hospital Variables and Frequency of Shifts by Hospital Variables

             Variable     Frequency   Percentage

            Australia      74,299        71.9
Country    New Zealand     28,970        28.1
              Total        103,269      100.0

           Metropolitan    75,047        72.7
Region        Rural        28,222        27.3
              Total        103,269      100.0

              Public       45,363        43.9
Status       Private       57,906        56.1
              Total        103,269      100.0

                1          22,555        21.8
Level           2          62,060        60.1
                3          18,654        18.1
              Total        103,269      100.0

Table 2
Cost Analysis: All Hospitals, All Shifts

                             N             Sum         Mean

Actual inpatient hours   103,269      2,051,049.23    19.8612
CNDMS-predicted hours    103,269      2,019,200.30    19.5528
NPR -predicted hours     103,269      2,088,085.03    20.2199

                        Standard
                        Deviation    Total Cost

Actual inpatient hours  17.73146    $82,041,969
CNDMS-predicted hours   19.12870    $80,768,012
NPR -predicted hours    19.86600    $83,523,401

Table 3.
Cost Analysis: All Hospitals, All Shifts by Patient Type Category

Patient Type                     Category            N
                          Actual inpatient hours   63,363
Medical/Surgical          CNDMS-predicted hours    63,363
                           NPR-predicted hours     63,363
                          Actual inpatient hours    7,209
Pediatric                 CNDMS-predicted hours     7,209
                           NPR-predicted hours      7,209
                          Actual inpatient hours   15,197
Ante/Post Natal           CNDMS-predicted hours    15,197
                           NPR-predicted hours     15,197
                          Actual inpatient hours    9,319
Adult Critical Care       CNDMS-predicted hours     9,319
                           NPR-predicted hours      9,319
                          Actual inpatient hours    1,431
Non-Adult Critical Care   CNDMS-predicted hours     1,431
                           NPR-predicted hours      1,431
                          Actual inpatient hours    6,750
Other                     CNDMS-predicted hours     6,750
                           NPR-predicted hours      6,750

Patient Type                     Category              Sum

                          Actual inpatient hours   1,279,878.05
Medical/Surgical          CNDMS-predicted hours    1,283,228.98
                           NPR-predicted hours     1,360,295.40
                          Actual inpatient hours     118,770.15
Pediatric                 CNDMS-predicted hours      112,598.27
                           NPR-predicted hours        95,865.87
                          Actual inpatient hours     234,189.23
Ante/Post Natal           CNDMS-predicted hours      207,556.28
                           NPR-predicted hours       176,592.10
                          Actual inpatient hours     223,229.87
Adult Critical Care       CNDMS-predicted hours      225,356.83
                           NPR-predicted hours       263,585.88
                          Actual inpatient hours      47,746.48
Non-Adult Critical Care   CNDMS-predicted hours       47,417.85
                           NPR-predicted hours        30,112.08
                          Actual inpatient hours     147,235.45
Other                     CNDMS-predicted hours      143,042.08
                           NPR-predicted hours       161,633.70

Patient Type                     Category           Mean

                          Actual inpatient hours   20.1991
Medical/Surgical          CNDMS-predicted hours    20.2520
                           NPR-predicted hours     21.4683
                          Actual inpatient hours   16.4753
Pediatric                 CNDMS-predicted hours    15.6191
                           NPR-predicted hours     13.2981
                          Actual inpatient hours   15.4102
Ante/Post Natal           CNDMS-predicted hours    13.6577
                           NPR-predicted hours     11.6202
                          Actual inpatient hours   23.9543
Adult Critical Care       CNDMS-predicted hours    24.1825
                           NPR-predicted hours     28.2848
                          Actual inpatient hours   33.3658
Non-Adult Critical Care   CNDMS-predicted hours    33.1362
                           NPR-predicted hours     21.0427
                          Actual inpatient hours   21.8127
Other                     CNDMS-predicted hours    21.1914
                           NPR-predicted hours     23.9457

                                                   Standard
Patient Type                     Category          Deviation
                          Actual inpatient hours   18.45949
Medical/Surgical          CNDMS-predicted hours    19.90268
                           NPR-predicted hours     20.37116
                          Actual inpatient hours   13.69405
Pediatric                 CNDMS-predicted hours    14.80118
                           NPR-predicted hours     12.39210
                          Actual inpatient hours   13.37345
Ante/Post Natal           CNDMS-predicted hours    14.27377
                           NPR-predicted hours     12.34282
                          Actual inpatient hours   17.88241
Adult Critical Care       CNDMS-predicted hours    19.64746
                           NPR-predicted hours     24.19768
                          Actual inpatient hours   28.50000
Non-Adult Critical Care   CNDMS-predicted hours    28.21746
                           NPR-predicted hours     26.08304
                          Actual inpatient hours   16.47870
Other                     CNDMS-predicted hours    17.74230
                           NPR-predicted hours     18.62731

Patient Type                     Category          Total Cost

                          Actual inpatient hours   $51,1951,22
Medical/Surgical          CNDMS-predicted hours     $51,329,159
                           NPR-predicted hours      $54,411,816
                          Actual inpatient hours     $4,750,806
Pediatric                 CNDMS-predicted hours      $4,503,931
                           NPR-predicted hours       $3,834,635
                          Actual inpatient hours     $9,367,569
Ante/Post Natal           CNDMS-predicted hours      $8,302,251
                           NPR-predicted hours       $7,063,684
                          Actual inpatient hours     $8,929,195
Adult Critical Care       CNDMS-predicted hours      $9,014,273
                           NPR-predicted hours      $10,543,435
                          Actual inpatient hours     $1,909,859
Non-Adult Critical Care   CNDMS-predicted hours      $1,896,714
                           NPR-predicted hours       $1,204,483
                          Actual inpatient hours     $5,889,418
Other                     CNDMS-predicted hours      $5,721,683
                           NPR-predicted hours       $6,465,348

Table 4.
Cost Analysis: Australian Metropolitan Public Level 2 Hospitals
by Patient Category

Patient Type                Category            N        Sum

                     Actual inpatient hours   4,023   82,599.97
Medical/Surgical     CNDMS-predicted hours    4,023   87,474.97
                     NPR-predicted hours      4,023   88,337.00
                     Actual inpatient hours     716   16,235.35
Adult Critical Care  CNDMS-predicted hours      716   14,489.62
                     NPR predicted hours        716   14,497.02

                                                        Standard
Patient Type                Category           Mean     Deviation

                     Actual inpatient hours   20.5319   16.10417
Medical/Surgical     CNDMS-predicted hours    21.7437   17.85130
                     NPR-predicted hours      21.9580   17.85618
                     Actual inpatient hours   22.6751   14.58144
Adult Critical Care  CNDMS-predicted hours    20.2369   14.50061
                     NPR predicted hours      20.2472   13.36028

Patient Type                Category          Total Cost

                     Actual inpatient hours    $3,303,999
Medical/Surgical     CNDMS-predicted hours     $3,498,999
                     NPR-predicted hours       $3,533,480
                     Actual inpatient hours      $649,414
Adult Critical Care  CNDMS-predicted hours       $579,585
                     NPR predicted hours         $579,881

Table 5.
Cost Analysis: Australian Metropolitan Public Level 1 and 2 Hospitals
by Patient Type Category

Patient Type                     Category            N         Sum

                          Actual inpatient hours    4023    82,599.97
Medical/Surgical          CNDMS-predicted hours     4023    87,474.97
                          NPR-predicted hours       4023    88,337.00
                          Actual inpatient hours    2419    64,215.23
Pediatric                 CNDMS-predicted hours     2419    65,291.67
                          NPR-predicted hours       2419    53,571.00
                          Actual inpatient hours    2518    55,512.13
Ante/Post Natal           CNDMS-predicted hours     2518    54,896.57
                          NPR-predicted hours       2518    42,734.00
                          Actual inpatient hours     716    16,235.35
Adult Critical Care       CNDMS-predicted hours      716    14,489.62
                          NPR-predicted hours        716    14,497.02
                          Actual inpatient hours     763    43,486.98
Non-Adult Critical Care   CNDMS-predicted hours      763    42,340.25
                          NPR-predicted hours        763    27,246.00

                                                             Standard
Patient Type                     Category           Mean     Deviation

                          Actual inpatient hours   20.5319   16.10417
Medical/Surgical          CNDMS-predicted hours    21.7437   17.85130
                          NPR-predicted hours      21.9580   17.85618
                          Actual inpatient hours   26.5462   14.09407
Pediatric                 CNDMS-predicted hours    26.9912   16.01600
                          NPR-predicted hours      22.1459   13.25130
                          Actual inpatient hours   22.0461   21.74175
Ante/Post Natal           CNDMS-predicted hours    21.8017   22.75990
                          NPR-predicted hours      16.9714   18.76285
                          Actual inpatient hours   22.6751   14.58144
Adult Critical Care       CNDMS-predicted hours    20.2369   14.50061
                          NPR-predicted hours      20.2472   13.36028
                          Actual inpatient hours   56.9947   17.72631
Non-Adult Critical Care   CNDMS-predicted hours    55.4918   20.14025
                          NPR-predicted hours      35.7090   28.34835

                                                     Total
Patient Type                     Category             Cost

                          Actual inpatient hours   $3,303,999
Medical/Surgical          CNDMS-predicted hours    $3,498,999
                          NPR-predicted hours      $3,533,480
                          Actual inpatient hours   $2,568,609
Pediatric                 CNDMS-predicted hours    $2,611,667
                          NPR-predicted hours      $2,142,840
                          Actual inpatient hours   $2,220,485
Ante/Post Natal           CNDMS-predicted hours    $2,195,863
                          NPR-predicted hours      $1,709,360
                          Actual inpatient hours     $649,414
Adult Critical Care       CNDMS-predicted hours      $579,585
                          NPR-predicted hours        $579,881
                          Actual inpatient hours   $1,739,479
Non-Adult Critical Care   CNDMS-predicted hours    $1,693,610
                          NPR-predicted hours      $1,089,840

Table 6.
Cost Analysis by Scenario
(All Cases, All Australian Public Hospitals)

Indicator    Indicator
for CNDMS     for NPR
Larger         Larger
than            than
Actual         Actual                             N          Mean

CNDMS        NPR          CNDMS Variance        34,833      4.0273
predicted    predicted    (Actual--Predicted)
less than    less than    NPR Variance          34,833      5.4519
actual       actual       (Actual--Predicted)
                          ABS (NPR Variance)--  34,833      1.4245
                          ABS (CNDMS Variance)

             NPR          CNDMS Variance        18,525      2.3136
             predicted    (Actual--Predicted)
             at least     NPR Variance          18,525      -4.2592
             as much as   (Actual--Predicted)
             actual       ABS (NPR Variance)--  18,525      1.9456
                          ABS (CNDMS Variance)
                          CNDMS Variance        16,185      -1.9203

CNDMS        NPR          (Actual--Predicted)
predicted    predicted    NPR Variance          16,185      3.4942
at least     less than    (Actual--Predicted)
as  much     actual       ABS (NPR Variance)--  16,185      1.5738
as actual                 ABS (CNDMS Variance)

             NPR          CNDMS Variance        33,726      -3.5644
             predicted    (Actual--Predicted)
             at least     NPR Variance          33,726      -6.0663
             as much as   (Actual--Predicted)
             actual       ABS (NPR Variance)--  33,726      2.5018
                          ABS (CNDMS Variance)

Indicator   Indicator
for CNDMS    for NPR
Larger        Larger                                         Percent
than           than                              Standard      of
Actual        Actual                             Deviation   Actual

CNDMS        NPR          CNDMS Variance          4.38686     27.8%
predicted    predicted    (Actual--Predicted)
less than    less than    NPR Variance            6.13494     35.0%
actual       actual       (Actual--Predicted)
                          ABS (NPR Variance)--    5.10348      7.2%
                          ABS (CNDMS Variance)

             NPR          CNDMS Variance          2.83058     17.2%
             predicted    (Actual--Predicted)
             at least     NPR Variance            5.48736    -34.3%
             as much as   (Actual--Predicted)
             actual       ABS (NPR Variance)--    5.58337     17.1%
                          ABS (CNDMS Variance)
                          CNDMS Variance          2.66141    -10.8%

CNDMS        NPR          (Actual--Predicted)
predicted    predicted    NPR Variance            5.46970     20.9%
at least     less than    (Actual--Predicted)
as much      actual       ABS (NPR Variance)--    5.36311     10.1%
as actual                 ABS (CNDMS Variance)

             NPR          CNDMS Variance          4.26754    -35.2%
             predicted    (Actual--Predicted)
             at least     NPR Variance            6.93401    -59.5%
             as much as   (Actual--Predicted)
             actual       ABS (NPR Variance)--    5.97656     24.4%
                          ABS (CNDMS Variance)

Table 7.
Cost Analysis by Scenario
(Medical/Surgical Patient Type Category, Australian Public Hospitals)

Indicator       Indicator
for CNDMS        for NPR
Larger than    Larger than
Actual            Actual                                N       Mean

CNDMS          NPR            CNDMS Variance
predicted      predicted      (Actual--Predicted)    18,506    3.5870
less than      less than
actual         actual         NPR Variance           18,506     3.9782
                              (Actual--Predicted)

                              ABS (NPR Variance)--   18,506     0.3912
                              ABS (CNDMS Variance)

               NPR            CNDMS Variance
               predicted at   (Actual--Predicted)    11,958     2.0582
               least as
               much as        NPR Variance           11,958    -3.5956
               actual         (Actual--Predicted)

                              ABS (NPR Variance)--   11,958     1.5374
                              ABS (CNDMS Variance)

CNDMS          NPR            CNDMS Variance          9,764    -1.4741
predicted at   predicted      (Actual--Predicted)
least as       less than
much as        actual         NPR Variance            9,764     2.1787
actual                        (Actual--Predicted)

                              ABS (NPR Variance)--    9,764     0.7046
                              ABS (CNDMS Variance)

               NPR            CNDMS Variance         23,135    -3.4559
               predicted at   (Actual--Predicted)
               least as
               much as        NPR Variance           23,135    -5.7192
               actual         (Actual--Predicted)

                              ABS (NPR Variance)--   23,135     2.2633
                              ABS (CNDMS Variance)

Indicator       Indicator
for CNDMS        for NPR                                        Percent
Larger than    Larger than                           Standard      of
Actual            Actual                             Deviation   Actual

CNDMS          NPR            CNDMS Variance           4.26279    24.9%
predicted      predicted      (Actual--Predicted)
less than      less than
actual         actual         NPR Variance             4.14125    29.5%
                              (Actual--Predicted)

                              ABS (NPR Variance)--     3.42594     4.6%
                              ABS (CNDMS Variance)

               NPR            CNDMS Variance           2.44436    15.1%
               predicted at   (Actual--Predicted)
               least as
               much as        NPR Variance             3.98693   -29.3%
               actual         (Actual--Predicted)

                              ABS (NPR Variance)--     4.40200    14.2%
                              ABS (CNDMS Variance)

CNDMS          NPR            CNDMS Variance           2.30324    -9.4%
predicted at   predicted      (Actual--Predicted)
least as       less than
much as        actual         NPR Variance             2.65084    18.6%
actual                        (Actual--Predicted)

                              ABS (NPR Variance)--     2.92624     9.2%
                              ABS (CNDMS Variance)

               NPR            CNDMS Variance           4.14781   -27.7%
               predicted at   (Actual--Predicted)
               least as
               much as        NPR Variance             6.05151   -52.7%
               actual         (Actual--Predicted)

                              ABS (NPR Variance)--     5.12234    25.0%
                              ABS (CNDMS Variance)
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Author:Heslop, Liza; Plummer, Virginia
Publication:Nursing Economics
Geographic Code:8AUST
Date:Nov 1, 2012
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