Case Study From the General Wards of One Central Hospital Rafaela

ISSUES IN CLINICAL NURSING

Assessment of nursing management and utilization of nursing resources

with the RAFAELA patient classification system – case study from the

general wards of one central hospital

Anna-Kaisa Rainio MSc, RN

Development Manager, Vasa Central Hospital, Vasa, Finland

Arto E Ohinmaa PhD

Associate Professor Department of Public Health Sciences, University of Alberta, Edmonton, Alberta, Canada

Submitted for publication: 28 January 2004

Accepted for publication: 2 November 2004

Correspondence:

Anna-Kaisa Rainio

Vasa Central Hospital

Hietalahdenkatu 2-4

SF-65130 Vaasa

Finland

Telephone: þ35863231725

E-mail: [email protected]

RAINIO A-K & OHINMAA AE (2005)RAINIO A-K & OHINMAA AE (2005) Journal of Clinical Nursing 14, 674–684

Assessment of nursing management and utilization of nursing resources with the

RAFAELA patient classification system – case study from the general wards of one

central hospital

Background. RAFAELA is a new Finnish PCS, which is used in several University

Hospitals and Central Hospitals and has aroused considerable interest in hospitals

in Europe.

Aims and objectives. The aim of the research is firstly to assess the feasibility of the

RAFAELA Patient Classification System (PCS) in nursing staff management and,

secondly, whether it can be seen as the transferring of nursing resources between

wards according to the information received from nursing care intensity classifica-

tion.

Methods. The material was received from the Central Hospital’s 12 general wards

between 2000 and 2001. The RAFAELA PCS consists of three different measures:

a system measuring patient care intensity, a system recording daily nursing resources,

and a system measuring the optimal nursing care intensity/nurse situation. The data

were analysed in proportion to the labour costs of nursing work and, from that, we

calculated the employer’s loss (a situation below the optimal level) and savings

(a situation above the optimal level) per ward as both costs and the number of nurses.

Results. In 2000 the wards had on average 77 days below the optimal level and

106 days above it. In 2001 the wards had on average 71 days below the optimal level

and 129 above it. Converting all these days to monetary and personnel resources the

employer lost €307,745 or 9.84 nurses and saved €369,080 or 11.80 nurses in total in

2000. In 2001 the employer lost in total €242,143 or 7.58 nurses and saved €457,615

or 14.32 nurses. During the time period of the research nursing resources seemed not

have been transferred between wards.

Conclusions.RAFAELAPCS is applicable to the allocation of nursing resources but its

possibilities have not been entirely used in the researched hospital. Themanagement of

nursing work should actively use the information received in nursing care intensity

classification and plan and implement the transferring of nursing resources in order to

ensure the quality of patient care.

Relevance to clinical practice. Information on which units resources should be allo-

cated to is needed in the planning of staff resources of the whole hospital. More

resources do not solve the managerial problem of the right allocation of resources.

674 � 2005 Blackwell Publishing Ltd

If resources are placed wrongly, the problems of daily staff management and cost

control continue.

Key words: classification, cost, management, nursing, patient, resources

Introduction

Personnel planning and the optimal allocation of nursing

resources as well as effective management represent an

internationally important subject of health care research.

Literature on the productivity of hospitals (Fare et al. 1997,

Linna 1998, Linna & Hakkinen 1999) shows that there are

clear cost efficiency and productivity differences between

hospitals and wards. These are largely due to their different

staff costs in relation to their output such as bed-days and

outpatient visits. The management of staff resources in

hospitals and their wards requires tools which take into

consideration both the amount and quality of nursing staff

and patients and are able to react to changes in them.

The nursing care intensity for different patient groups is a

large factor in the needs of staff resources. This does not

mean how demanding the medical treatment is, since a

patient’s need for care may be more demanding than what a

medical diagnosis might predict (Christ-Grundmann 1997,

Van Slyck & Johnson 2001). In recent years, patient

classification systems have been developed in a new way

due to improved information systems that facilitate the

receiving of complete information relating to patients, wards

and hospitals (Levenstam & Bergbom Engberg 1997,

Bjorkgren et al. 1999, Mueller 2000). It is possible to analyse

and use these comprehensive information sources in the

planning and production of health care, as well as evaluating

its productivity (Hofdijk 1997, Sanderson & Mountney

1997).

Classification systems of health care have improved

throughout recent decades. They are usually based on

medical diagnosis, such as ICD-10. Diagnostic-Related-

Groups (DRG), a system for describing hospital production

developed in the USA, is a good example of such a system

combining clinical diagnosis and treatment. It determines the

amount of resources used to care for a patient within a

certain ICD-10 category (Fetter & Freeman 1986, Freeman

et al. 1995, Muldoon 1999). The DRG cost weights are

country specific and based on statistical analysis of the

costing data in each country (Mikkola et al. 1998). Imple-

mentation of DRG in the 1980s strongly influenced the

development of PCS because measurements data based on

medical diagnosis do not show the information for nursing

management and its productivity and costs and do not solve

the problem of allocating nursing resources optimally to

ensure qualitative care. (Malloch & Conovaloff 1999, Averill

& Fairbrother 2000)

The only way to measure the work done by nurses from a

patient perspective and on a hospital or ward level is to use a

patient classification system where the information on the use

of resources can be used in staff management and cost

accounting (Thomas & Vaughan 1986, Hoffman 1988). In

this study, we use a patient classification system to analyse

the use of nursing resources. A patient classification system

(PCS) assesses and classifies patients according to their need

of care as well as the activities that are necessary to fulfill the

needs of the care process during a certain time period. It can

be determined by methods and processes that are used to

identify, validate and monitor the needs of an individual

patient and provide information for human resource admin-

istration, accounting, budgeting and other functions of

management. (Huhkabay & Skonieczny 1981, De Groot

1989a,b)

The aim of this study is, through one case, to assess how

health-care managers have used the new Finnish PCS in

nursing care management during a two-year period. Active

management should result in the transferring of nursing

resources from wards with a low nursing care intensity per

nurse ratio (situation below the optimal nursing level) to

wards with a high nursing care intensity per nurse ratio

(situation above the optimal nursing level). The study aims

can be summarized in the following study questions:

1 How feasible is the used PCS in nursing management to

measure the utilization of resources and their cost impli-

cations?

2 Does the PCS reflect the differences in utilization of nursing

resources between different general inpatient wards?

3 How much resources have been ‘saved’ or ‘lost’ in nursing

care management when the nurses’ working days are too

busy or too quiet, in other words when the nursing care

intensity per nurse ratio has been above or below the

optimal level in each ward?

4 Has there been active nursing management through the

transfer of nursing resources in two consecutive years

depending on the need for patient care, in other words

from wards below the optimal level of nursing

care intensity per nurse to wards with above optimal

levels?

Issues in clinical nursing Assessment of nursing management and utilization of nursing resources

� 2005 Blackwell Publishing Ltd, Journal of Clinical Nursing, 14, 674–684 675

Methods

Presentation of the PCS (RAFAELA-system)

The development of the patient classification system (PCS)

at Vasa Central Hospital started in 1992 and it was taken

into experimental use in all hospital patient wards in

1994–1995. The PCS received the name RAFAELA from

the research team who developed it: Rainio – Fagerstrom –

Rauhala. (Rainio 1999) The RAFAELA-system consists of

three parts: Oulu Patient Classification (OPC) system

(Kaustinen 1995), nurse resource registry, and Professional

Assessment of Optimal Nursing Care Intensity level

(PAONCIL) measure. Using the OPC system and nurse

resource registry, we are able to calculate nursing care

intensity points per nurse during each day. The PAONCIL

measure is used to estimate, using linear regression analy-

sis, the optimal nursing care intensity level per nurse,

which describes the needed nurse resources in proportion

to the need of patient care. (Fagerstrom & Rainio 1999a,

Fagerstrom et al. 2000b).

The RAFAELA PCS can be described by the following

formula:

RAFAELA PCS ¼ (OPC/Nursing resource) and PAONCIL

method.

The RAFAELA nursing care intensity classification system

consists of data shown in Fig. 1. The data are part of the

hospital’s electronic patient administration system. The

RAFAELA system is not described in detail but previous

articles are referred to.

Oulu Patient Classification (OPC)

The OPC (Oulun yliopistollinen keskussairaala 1994, Kaus-

tinen 1995) consists of six subsections of nursing care:

(1) planning and co-ordination of care; (2) breathing, blood

circulation and symptoms of disease; (3) nutrition and

medication; (4) personal hygiene and excretion; (5) activity,

movement, sleep and rest; (6) teaching, guidance in care and

follow-up care, emotional support. In each of these areas the

nurse classifies patients into one of four classes according to

their need of care (from A ¼ 1 point to D ¼ 4 points). In the

NURSING CARE MANAGEMENT REPORT

FROM RAFAELA PCS

HOSPITAL’S ELECTRONIC RECORDS

PATIENT MANAGEMENT ADMINISTRATION

• ••

In–out patient Accounting• Resource booking Human resources• Waiting lists• Patient discharge and invoice

HILMO

Discharged patient record (national registry)

-8 weeks

RESOURCES

• Nurse resources / day• Salary + benefits

PAONCIL

(Professional Assessment of Optimal Nursing Care Intensity Level)

Research in the wards during 6–8 weeks

OPC

(Patient classification)

Nursing care intensity scoresdepending on patients’ need for care / ward / day

Nursing care intensity per nurse for personal administration / day / month / year• days below the optimal

level• days at the optimal level• days above the optimal

level

Nursing care intensityscores per• patient• nurse• day / month / year• patient room• DRG group

Figure 1 RAFAELA PCS as a part of the

electronic administrative system in Vasa

Central Hospital.

A-K Rainio and AE Ohinmaa

676 � 2005 Blackwell Publishing Ltd, Journal of Clinical Nursing, 14, 674–684

classification a 10-paged classification manual is used. The

OPC summary score is calculated by adding up the points in

the six care areas of the system. Thus the care intensity of a

patient can be 6–24. The higher the score, the higher the

nursing care intensity of the patient. The OPC classification is

based on the principles of nursing presented in the quality

control programme and on Roper’s model of nursing. (Oulun

yliopistollinen keskussairaala 1994) The development of the

classification system is based on the Hospital Systems Study

Group (HSSG) – classification (Goldstone et al. 1983).

The OPC measure is used once a day. The OPC measure

does not describe how nurses have filled their day with

different tasks, but how they have answered the patients’ needs

with the different processes and interventions of nursing work

(Hoffman 1988, Rainio 1994, Kaustinen 1995, Rainio 1998).

The validity and reliability of theOPCmeasure has been tested

in previous studies (Fagerstrom et al. 2000a, Fagerstrom &

Rauhala 2001, 2003). Even from the point of view of the

patient and the nursing care intensity content of the measure

has been improved (Fagerstrom et al. 1998, 1999b).

Nursing resources

Each day the number of nurses who have worked with the

patients in a ward is registered into the nurse resource registry

(Fig. 1). The OPC score is divided by the number of the

nurses in the ward on each day. The nursing care intensity

point per nurse ratio describes the productivity of nursing

care in the ward (Rainio 1999, Fagerstrom & Rainio 1999a,

Fagerstrom et al. 2000b).

Professional Assessment of Nursing Care Intensity Level

(PAONCIL)

The third part of the RAFAELA PCS is PAONCIL and it

answers the question, when does the nursing care intensity/

nurse ratio reach the level of good care, in other words when

do nurses estimate that they have given their patients good

care? PAONCIL is an alternative to observation research and

time measurement. Nurses assess how they have experienced

their shift’s workload using a seven-class measure. Zero is the

optimal situation, 1–3 is a situation where nurses have too

much work and the bigger the figure is, the more nurses have

to prioritise tasks. Minus three to �1 describes a situation

where the closer nurses are to �3, the more they feel they

have time for less important tasks. Classification guidelines

have been developed for the whole numbers of the scale.

Material is gathered during a period of approximately two

months, so that it would include an estimate of several

hundred shifts depending on the ward size and number of

nurses. Using a linear regression analysis it is possible to

examine to what extent the nursing care intensity/nurse ratio

explains the workload experienced by the nurses (PAONCIL

score). The regression model is then used to estimate the level

of nursing care intensity per nurse ratio where the corres-

ponding PAONCIL value is zero. With an administrative

decision the study’s results have been used so that when the

nursing care intensity point per nurse is, e.g., 27, the optimal

nursing care intensity level in the ward can vary ±3.5 points,

indicating that the optimal range would be between 23.5 and

30.5 nursing care intensity point/nurse per day. There will

always be a variation between days in the nursing care

intensity point; which variation is acceptable is decided

democratically by nurse managers. The decision is based on

practical management, no scientific method has been found

by researchers to do that. This nursing work assessment

method was developed in Vasa Central Hospital (Rainio

1996, 1999, Fagerstrom & Rainio 1999a, Fagerstrom et al.

2000b). The validity of the content of the PAONCIL method

has been tested by an expert group of 150 nurses. The

classification has been found universally applicable and its

validity acceptable. (Fagerstrom et al. 2002).

In the RAFAELA system each ward has its own optimal

level of nursing care, which is estimated using the PAONCIL-

measure (Table 1). It indicates a situation where nurses assess

that they have given the patients good quality care.

RAFAELA and nursing benchmarking in Finland

The aim of the RAFAELA-system has been, with respect to

nursing philosophy and for the purpose of using the data, to

give a picture of modern, professional nursing care. The long-

term development of the system has been based on the

philosophy of good, comprehensive care for the patient.

The RAFAELA-system has been tested nationally using

material from seven hospitals during the years 2000–2001.

The study has researched the state of Finnish nursing and

benchmarking data using the RAFAELA-system. In the study,

most specialized fields of nursing had a nursing care intensity

ratio above the optimal area during approximately 40–50%

of days, and below the lowest limit during 10–20% of days.

There were differences between specialized fields and, for

example, the nursing care intensity in the paediatric wards

was above the optimal area during 25% of days and below

the optimal area during 50% of days (Fagerstrom & Rauhala

2001, 2003). From the point of view of nursing management,

data from many hospitals do not show the efficiency or

activity of the management of an individual hospital, but

show the special health care situation on a national level and

offer unique reference material.



Data

The study data have been collected fromVasaCentralHospital

in Finland, which includes 24 specialized care wards. The

hospital’s catchment area comprises approximately 170 000

people. The data consist of information collected from 12

general specialized health care wards from the years 2000 and

2001, excluding psychiatric specialized care. The researched

wards are four internal medicine wards (A–D), two oncologic

wards (E and F), four surgical wards (G–J), a prenatal and

gynecological ward (K) and a paediatric ward (L).

The line of nursing management is formed in the following

way: the head nurse of a ward is responsible for the nursing

of an entire ward, the nurse manager is responsible for several

care units and the director of nursing is responsible for

nursing in the entire hospital and is on the executive board.

Table 1 shows the researched ward’s numbers of patients,

bed days, average numbers of nurses per day and average

nursing care intensity/nurse ratio (OPC/nursing resources) for

2000 and 2001 as well as the level of optimal nursing care

intensity per nurse (PAONCIL) in the different wards. The

number of nurses has changed most in ward D, where in

2001 there was on average one more nurse/day than in 2000.

Cost estimations in RAFAELA

The nursing management of inpatient wards was analysed in

the context of used resources and RAFAELA PCS, and the

one-day wage cost (W) in each ward was calculated using the

following formula: W ¼ (X�Z)/366, where:

• X is the ward’s gross salaries during one year including

benefits;

• Z is the employer’s social insurance reimbursement of

nurses’ sickness leaves;

• 366 is the number of days during year 2000 (365 in 2001).

In the analysis of the total cost of care (Y) in each ward,

only the days that were included into the RAFAELA data (D)

were included (Y ¼ W * D).

The RAFAELA system was used to calculate the nursing

care intensity points (T) of the days registered in the system

(D). The cost of one nursing care intensity point (S) was

calculated by dividing the total cost of care Y by nursing care

intensity points T during a year (S ¼ Y/T).

The cost of one nursing care intensity point (S) describes

how much resources (in monetary units) have been used to

satisfy the patients’ need for care. It is assumed that one

nursing care intensity point is equivalent in each ward, that is,

the way the assessment reflects the response of a nurse to the

needs of a patient is similar between the patients in the wards.

The analysis of the nurse management in the context of costs

and the nursing care intensity per nurse ratio was carried out

by comparing the actual nursing care intensity level on one

day (from PAONCIL) to the optimal range in the ward

(Table 1).

First the data were analysed to find out what resources the

employer had lost financially and in nurse numbers during the

days when the nursing care intensity/nurse ratio had been

below the optimal level. For example, the minimum of the

optimal level in ward E is 23.1 and a certain day’s measured

ratio is 17.7 (a difference of 5.4). When this figure is

Table 1 Beds, numbers of patients, inpatient days, average number of nurses/day and average nursing care intensity/nurse in the wards and the

optimal level in nursing care intensity by PAONCIL-method

Ward

Beds

inward

Admitted

patients Inpatient days

Number of

nurses/day

Nursing care

ntensity point/nurseOptimal

level**2000 2001 2000 2001 2000 2001 2000* 2001*

A 20 1026 1185 8606 7516 9.59 9.66 26.11 27.58 20,3–27,3

B 27 2247 2406 10900 10448 10.6 10.8 30.66 31.71 20,3–27,3

C 28 2337 2823 12049 11646 11.1 11.5 27.79 29.75 21–28

D 27 1840 1931 11476 11268 12 12.9 31.36 32.76 22,4–29,4

E 15 848 742 4782 4111 7.21 6.97 28.28 27.51 23,1–30,1

F 15 839 1070 4334 5287 7.09 7.49 26.95 27.72 23,1–30,1

G 30 1741 1771 10269 10497 12.3 12.5 28 27.65 21,7–28,7

H 32 1725 1719 11925 11763 14.4 14.7 28.28 28.21 22,4–29,4

I 20 1287 1372 7033 7326 11.6 11.9 21.35 22.54 17,5–24,5

J 24 1786 1745 7399 7233 10.5 10.7 29.19 29.4 26,6–33,6

K 14 1329 1497 5221 5629 7.84 8.04 19.53 20.72 18,9–25,9

L 30 2105 2508 8994 9898 20.5 20.9 14.98 15.19 11,2–18,9

*An average value on each year.

**Determined in a consensus by nurse administrators.



multiplied by the number of nurses that day, we get the

nursing care intensity point which is needed to reach the

minimum of the optimal level (23.1). When this nursing care

intensity point is multiplied by the price of ward E’s nursing

care intensity point (€6,35 in 2000 and €6,65 in 2001), we

reach the amount that the employer had lost with inefficiently

used nurse resources.

In the analysis we added up the days below the optimal

level in each ward. This figure was then divided by a nurse’s

average basic labour costs without benefits. This shows the

employer’s loss in nurse resources. Secondly, we analysed the

days that had exceeded the optimal level, that is the days

when the employer had saved on nurse resources, in the same

way.

For example, the upper limit for the optimal limit in ward

E is 30.1, and the nursing care intensity/nurse ratio for a

certain day is 31 (a difference of 0.9). When this figure is

multiplied by the number of nurses that day, we obtain the

figure which is needed to reach the optimal level (30.1) as

nursing care intensity points. When this figure is multiplied

by the cost of the ward’s nursing care intensity point (€6,35 in

2000 and €6,65 in 2001), we find out the employer’s financial

saving. When we add up the savings of the employer during a

year and divide it by the basic salary of a nurse, we get an

estimate of the number of additional nurses needed in the

ward to reach the optimal nursing care intensity per nurse

level.

Results

The average numbers of nursing care intensity points per

nurse were on the optimal level on 10 out of 12 wards in

2000 and eight wards in 2001 (Table 1). In nine wards the

average nursing care intensity level/year has risen from 2000

to 2001.

Table 2 shows that the labour costs of nursing have risen

from 2000 to 2001 in all wards except E, K and L. Income

from medical insurance has risen in 2001. The number of

Table 2 Labour costs (X), health insurance income (Z), daily labour costs (W), days registered in the RAFAELA (D) and their costs (Y), nursing

care intensity points during a year in the ward (T) and the cost of one nursing care intensity point (S) during 2000 and 2001

X Salaries

Z Sickness

leave

¼W (N ¼ 366)

Salary/day

Measured

days (D)

Cost of

excluded days

Y Measured

cost

T Care intensity

points

S (Y/T) Cost

of point

Year 2000 wards

A 690755 5458 1872 162 381969 303328 38580 7.86

B 723690 8737 1953 364 3907 711046 114409 6.21

C 755628 11827 2032 365 2032 741768 109544 6.77

D 655354 2084 1785 273 3570 649700 102501 6.34

E 462160 8440 1240 341 30992 422728 66564 6.35

F 419036 2705 1138 321 51188 365142 58022 6.29

G 771721 6050 2092 362 8368 757304 116962 6.47

H 937118 21639 2501 362 10005 905475 140253 6.46

I 806194 3016 2194 366 0 803178 45928 17.49

J 564592 10439 1514 268 148380 405773 80364 5.05

K 578883 9598 1555 366 0 569285 54435 10.46

L 1541125 25004 4142 364 8285 1507837 105173 14.34

Total 8906257 114997 24020 3914 648695 8142564 1032735 7.88

Year 2001 wards

A 697160 14051 1872 364 1872 681238 91920 7.41

B 760120 11277 2052 361 8207 740636 117937 6.28

C 838180 8030 2274 365 0 830149 121310 6.84

D 846915 8321 2298 365 0 838594 146163 5.74

E 448936 3408 1221 297 83002 362525 54539 6.65

F 473899 14660 1258 364 1258 457981 71631 6.39

G 825016 14592 2220 364 2220 808204 121206 6.67

H 958255 14334 2586 365 0 943920 142809 6.61

I 854834 10370 2314 365 0 844464 94397 8.95

J 578929 3454 1577 275 141898 433578 81154 5.34

K 572321 5656 1553 355 15525 551140 57924 9.51

L 1513147 12821 4110 363 8221 1492105 109148 13.67

Total 9367713 120975 25264 4203 262203 8984534 1210138 7.42

The hospital’s expenses in 2000 were €90 897 434 and €96 774 312 in 2001.



days registered in the data rose in 2001. In 2001 the cost of

care intensity points went down in wards A, D, I, K and L, in

other words in proportion to costs the patients’ needs were

answered at a lower cost in 2001 compared to 2000, even

though Table 1 shows a slight increase in average nurse

resources. The cost of care intensity points had increased in

seven wards, in other words the patient’s needs were fulfilled

at a higher cost than in 2000. The change in the cost of care

intensity points does not seem to have a connection to the

changes in average nurse resources shown in Table 1. The

changes in the cost per nursing care point within the wards

were small except in surgical ward I (8.54 points), which had

an exceptionally small number of surgeries during 2000 and

can be considered as an exception in the data. If this ward is

excluded from the average cost of a nursing care point, there

was a €0,13 decrease in the average nursing care point cost.

Table 3 shows that wards operated for approximately

77 days (21%) and 71 days (19%) on average below the

optimal nursing care intensity range in 2000 and 2001,

respectively. The period above the optimal level increased

from 106 days (29%) to 129 days (35%) on average. When

these days were transformed into the number of nurses per

year in resource utilization below or above the optimal

resource, the employer can be seen to have lost a resource of

9.84 nurses in 2000. Transformed to monetary units this is

equivalent to €307,745. Respectively in 2000, there was a

shortage of 11.80 nurses (€369,080) in relation to the need of

patient care. More effective allocation of resources would

have been needed, especially in wards B, C, D, G and H,

where the employer had saved more in resources than what

had been lost, and in wards J–L, where resources were

inefficiently utilized. The resources needed for patient care

Table 3 Nurses operating below and above the optimal nursing care intensity range measured by days, wages and number of nurses in days

registered to RAFAELA during 2000 and 2001

RAFAELA

Days

Nurses operating below optimal level Nurses operating above optimal level

Days Lost wages Nurses/year Days Saved wages Nurses/year

Year 2000 ward

A 162 26 4096 0.13 43 9209 0.29

B 364 26 6156 0.20 249 86923 2.78

C 365 31 5077 0.16 144 31750 1.01

D 273 15 5324 0.17 191 53329 1.70

E 341 103 20291 0.65 111 25122 0.80

F 321 123 29261 0.94 86 18982 0.61

G 362 61 17388 0.56 128 37822 1.21

H 362 56 11294 0.36 109 25576 0.82

I 366 86 38285 1.22 73 35149 1.12

J 268 125 44627 1.43 45 7450 0.24

K 366 187 66497 2.13 44 9683 0.31

L 364 90 59450 1.90 47 28084 0.90

Total 3914 929 307745 9.84 1270 369080 11.80

Average 77 25645 0.82 106 30757 0.98

Year 2001 ward

A 364 50 11171 0.35 147 39789 1.25

B 361 15 3466 0.11 259 96538 3.02

C 365 11 1684 0.05 220 54503 1.71

D 365 11 1783 0.06 254 85672 2.68

E 297 104 22800 0.71 88 19336 0.61

F 364 131 32564 1.02 119 27050 0.85

G 364 60 13328 0.42 124 32594 1.02

H 365 63 26078 0.82 117 30647 0.96

I 365 66 17153 0.54 96 28523 0.89

J 275 123 36545 1.14 52 11512 0.36

K 355 146 36049 1.13 44 9651 0.30

L 363 70 39524 1.24 33 21799 0.68

Total 4203 850 242143 7.58 1553 457615 14.32

Average 71 20179 0.63 129 38135 1.19

The labour costs of one nurse’s basic salary in 2000 was €31 283 and in 2001 €31 956.



would have partly been in use in general wards if they had

been more effectively used by operational management. This

would have required transferring nurses to new wards and

the hiring of two new nurses.

In 2001 the employer lost 7.58 nurses in resources. This is

equivalent to a cost of €242,143 for these days below the

optimal level. There was a shortage of 14.32 nurses

(€457,615) in relation to the need for patient care. The

working pace seems to have increased especially in all the

wards for internal medicine and surgical ward G. In 2001

the need to hire new nurses in the hospital increased

compared to the previous year.

The data do not directly show the active operative

management of nursing or the transferring of nursing

resources according to the patients’ need for care over time.

From 2000 to 2001 ward D received the additional work

contribution of 0.9 nurses/day on average. In other wards the

changes in nurse resources were below 0.5 nurses/day on

average (Table 1). In wards A–D the situation became more

difficult in 2001 compared to 2000. In wards E–I the

situation remained approximately the same and differences

in nurses operating above and below the optimal nursing care

intensity range was small. In wards J–L nurses operated

below the optimal range more frequently than above it,

indicating that these wards had some excess nursing

resources.

Discussion

The essential purpose of the RAFAELA-system is to use

information in the allocation of nursing resources to where

the good quality of patient care requires it, in other words to

work as an instrument for effective nursing care manage-

ment. The study shows that the RAFAELA-system is suitable

for analyzing nursing management and its cost effects.

The wards of the researched hospital did not completely

succeed in the allocation of nursing resources according to

the need of patient care. Also, the workload in nursing

increased from 2000 to 2001, as nurses operated above the

optimal limit more than below it. There were not always

nurses where they would have been needed, in other words

there have been problems in ensuring the quality of the care

of patients. On the other hand, the employer lost nursing

resources in almost all the wards, because during both years

the level was below the optimal limit and the nurses had

inefficient working time.

The results are inconclusive, because data from a period of

only two years were used and the data cover only the general

wards of one central hospital. However, this case study

shows how the PCS could be used to assess active nursing

care management. The optimal upper and lower limits of the

PAONCIL-measure have been set by an administrative

decision, so it is impossible here to assess how correct they

are. The principle was to treat each ward according to the

same criteria (Rainio 1999). The RAFAELA-system has been

shown to possess a high level of validity, (Fagerstrom et al.

1998, Fagerstrom et al. 2000b) and its development and

testing has continued in a national program (Fagerstrom &

Rauhala 2001, 2003). The recent result of the national data

shows that the average reliability of parallel classification was

77%, which can be considered a good result. However, the

validity of hospitals in the national reference material was

weakened by the variation of coding practices in different

hospitals (Fagerstrom & Rauhala 2003). The reliability and

usability of the RAFAELA-system has been tested on the

material of eight Finnish hospitals and it has been found a

reliable tool in personnel planning, budgeting and cost

calculation (Rauhala & Fagerstrom 2004).

A lack of nursing resources in the short term existed in

almost all the wards at some point. The question is, in

nursing, is there a daily, operative management, with which

nursing resources can be allocated according to the need for

patient care? In answering this question there is the problem

that nursing care intensity classification systems show the

situation in retrospect and are not able to predict the

situation of the future (Hoffman 1988). In addition, we must

acknowledge other problems of staff management in acute

specialized health care. Three-shift-work sheets planned in

advance and quickly changing situations make successful

operative management difficult to achieve (Mark 2002).

However, these kinds of data from several years offer

material needed for strategic staff management. Data

received from nursing care intensity classification systems

should be used diversely in the decision-making of nursing

management (Van Slyck 2000). Nursing resources that have

been used are registered, but this registry could also be used

to show possible transfer of nurses, temporary or permanent,

to other wards. This would show more clearly the daily

management decisions in nursing care.

The limited scope of the nursing care intensity classification

system must be admitted. It shows the patients’ acute care

needs but not the many other factors which affect the needs

of nursing resources in nursing units (Stepura & Miller

1989). Other factors that affect nursing (such as the planning

of work, shift rotas, substitutes, students, co-operation

between professions, one’s own capacity for work, stress)

have been taken into consideration in the PAONCIL method

and thus an attempt to separate the meeting of patients’ needs

and other factors adding to the work load has been made

(Rainio 1999, Fagerstrom et al. 2002). The PCS is not a



measure of a ward’s nursing care intensity, but of the patient

(Van Slyck 2000). In the management of nursing, diverse

information sources of the wards’ situation and productivity

should be used (Barton 1994) such as treatment periods, bed

days, out-patient visits, DRG-groups and national bench-

marking information.

The data raise the question as to whether the management

of nursing work is sufficiently interested in real information,

rather guided more by subjective, experience based informa-

tion than objective, measurable and verified information, as

Vuori (2001) has suggested. The rigidity of the wards in

adding nursing resources during the second year, even though

data from the first year indicated a clear lack of nurses, shows

that not all the levels of management in the hospital acted

according to the results. Annual and monthly reports on the

situation in nursing should be made. These would show

which way nursing in wards is developing, whether nursing

resources are in optimal use and whether the work is

significantly under or over resourced. This information

should also be used as a part of the hospital’s own budget

planning, because significant changes in nursing resources

mean significant changes in the ward’s costs.

When we look at the costs of nursing, we can say that the

work pace of nurses had, looking at total costs, increased. In

2001 the employer saved more than in 2000. In 2000 the

savings and losses were fairly close to each other, but in 2001

the losses were only half of the savings. Roughly estimated on

the basis of the patient’s nursing care intensity, every ward

would have needed one more nurse to ensure the quality of

care. The nurses also had ineffective working time, which is a

sign that adding to resources does not solve the managerial

problem of the right allocation of resources. If resources are

placed wrongly, the problems of daily staff management

continue.

Vuori (2001) states that the adding of resources does not

guarantee that they are used appropriately. Management

should learn to interpret information and assessment data in

order to make management more effective. The problems of

health care are not solved by changing the administration or

by adding to resources, but by questioning the things that are

taken for granted in the operational models of organizations

and work communities. Administration needs both clinical

and managerial leadership, so that hospitals can reach

successful solutions (Ham 2003).

In the researched wards where the nursing care intensity

per nurse ratio was constantly high, we can predict that the

nurses will soon be exhausted, take sick leave or move to

wards with a lower nursing care intensity level. The nurses

evaluating the PAONCIL method stated that calm periods

(days below the optimal level) are necessary for relaxation

and re-balancing after busy days (Fagerstrom et al. 2002). It

is likely that nursing intensity and necessary nursing resources

are connected to nursing satisfaction and turnover (Mark

2002). The greatest motivation for hospital staff is giving

high quality services to patients (Ham 2003). High nursing

care intensity (days above the optimal level) was described by

the nurses as a feeling of inadequacy when good quality of

care cannot be quaranteed (Fagerstrom et al. 2002). It is not a

good sign for the quality of nursing that nursing resources are

for several years used to the limit. Especially in wards B–D

the significant need for additional resources during 2000 did

not lead to new recruitments except in ward D, where there

was an average addition of 0.9 nurses/day, and the above

optimal level time still increased during the next year. In

addition to the likely decreased quality of care, it is possible

that this has also had an impact on the well-being of nurses

over time. Sick leave reimbursements have increased a small

amount from 2000 to 2001 and there seem to be more

increases in reimbursements amongst wards that have a

higher need for additional nursing resources. However, this

pattern is not true in every ward.

Conclusions

The first goal of this study was to show how feasible the

RAFAELA PCS is in nurse management. On the basis of this

case, it seems that RAFAELA can be used to measure the

nursing care intensity/nurse ratio in different wards and

further receive annual reference data needed in the manage-

ment of nursing.

The second goal was to test whether RAFAELA reflects the

differences between wards. The analysis shows that there are

significant differences between wards that remained almost

unchanged during the two-year period.

The third goal was to analyse how much the employer has

saved or lost in nursing costs and resources. The study shows

that the employer saved more in nursing resources than what

was lost. There were significant differences between wards.

The fourth goal was to research whether active nursing

management could be found in the data. The different

nursing care intensity per nurse levels in different wards did

not improve during the two years, nor did the situation in the

higher care intensity level wards. It is noteworthy that the

general situation of nursing seemed to become more difficult

in 2001 compared to 2000.

According to this research, data from PCS should be used

in staff management, otherwise its implementation in the

organization is not justifiable. The nursing resources could be

better allocated by the management according to patient

needs. Nurses have either too heavy or too light days, and the



quality of care decreases during busy days, when nursing

resources lost from the other wards with light days would

have been needed.

RAFAELA PCS and the methods and registers it is based on

should be further researched and improved to ensure reliab-

ility and validity. Decision-making concerning the whole

hospital requires reliable information about the unit where

additional resources need to be allocated. In health care the

success of management in cost control, productivity and the

effective allocation of staff is evaluated using more and more

sources of information. In the RAFAELA system the produc-

tivity of nursing can be seen as nursing care intensity per

nurse figures and can be directly converted into money and

numbers of nurses. This information supports nursing man-

agers in the fair allocation of resources and in the guaran-

teeing of the quality of care.

Acknowledgements

Sincere thanks to Vasa Central Hospital for the opportunity

to use data from the RAFAELA PCS. Thanks also to Olle

Pursiainen, the IT-manager, for helping with Fig. 1. From

2003 the RAFAELA PCS has been owned by The Association

of Finnish Local and Regional Authorities. The study was

supported by the Medical Research Fund of Vasa Hospital

District.

Contributions

Data collection: PP, AR; data analysis: AR, UI; manuscript

preparation: ALM; translation: JS.

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Case Study From the General Wards of One Central Hospital Rafaela

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