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UNIVERSITY OF TECHNOLOGY, SYDNEY 3

Nursing Workload and Staffing: Impact on Patients and Staff

Professor Christine Duffield

Michael Roche

Professor Linda O’Brien-Pallas

Professor Donna Diers

Chris Aisbett

Kate Aisbett

Professor Caroline Homer

ISBN 978-0-9806239-3-2


4 UNIVERSITY OF TECHNOLOGY, SYDNEY



Roles of Contributors

The roles of contributors during the project were as follows:

Professor Christine Duffield (Centre for Health Services Management – UTS)

o Project Director. Cross-sectional design. Cross-sectional sample definition.

Interpretation of cross-sectional and longitudinal analysis. Report

development.

Michael Roche (Centre for Health Services Management – UTS)

o Longitudinal data collection. Cross-sectional sample definition. Cross-

sectional data collection and entry. Analysis and interpretation of cross-

sectional data. Report development.

Professor Linda O’Brien-Pallas (Nursing Health Services Research Unit – University of Toronto and Adjunct Professor UTS)

o Cross-sectional design and supply of instruments, syntax for cross-sectional

analysis. Analysis and interpretation of cross-sectional data. Interpretation of

the longitudinal data.

Professor Donna Diers (Yale New Haven Health System [USA] and Adjunct Professor UTS)

o Longitudinal study outcomes design. Interpretation of longitudinal data.

Analysis of cross-sectional data and the integration of both methods. Report

development.

Chris Aisbett (Laeta Pty Ltd)

o Collation and editing of longitudinal data. Analysis and interpretation of

longitudinal data. Report development.

Kate Aisbett (Laeta Pty Ltd)

o Analysis and interpretation of longitudinal data. Report development.

Professor Caroline Homer (Centre for Family Health & Midwifery – UTS)

o Cross-sectional design. Report development.


6 UNIVERSITY OF TECHNOLOGY, SYDNEY



Acknowledgements

The investigators wish to acknowledge the commitment of ACT Health to improving

patient safety and the working lives of nurses through funding this study. The ongoing

involvement of and input from senior staff in ACT Health and its two hospitals has been

critical to the success of this project. We would also like to acknowledge the support

and guidance provided by the Senior Nurses associated with this project throughout its

duration: the Chief Nurses, Adjunct Professor Jenny Beutel for her commitment to

ensuring this project was funded and Ms Joy Vickerstaff to whom this Report was

handed; and the Directors of Nursing, Ms Joy Vickerstaff and Ms Sue Hogan who

facilitated access to their hospitals and data collection. The additional assistance and

support of Leonie Johnson, Michelle Cole, and other staff of the Canberra Hospital

Research Centre, and of Sue Minter of Calvary Hospital was also gratefully received.

Without the assistance of all the staff in the Nursing and Midwifery Office, particularly

Sonia Hogan and Heather Austin, in their responses to our numerous requests for

assistance, this project would not have been completed. The team also acknowledges

the extraordinary diligence of Dianne Pelletier who coordinated the cross-sectional data

collection process and acted as the „trouble-shooter‟ and liaison throughout the project.

The research team is indebted also to the generous assistance provided by Dr Barbara

McCloskey in allowing us to use her SAS (analytic software) code for the outcomes

algorithms, Sping Wang and Xiaoqiang Li of The Nursing Health Services Research

Unit (University of Toronto) for the use of their SPSS syntax, Nancy van Doorn of Laeta

Pty for her extensive work in data cleaning and analysis, Christine Catling-Paull for her

comprehensive review of the literature, and Jane Ewing for her preliminary data

analysis. In addition, the assistance of ACT Health and Calvary Information Technology

staff in the extraction of workforce data was indispensable. Finally, the researchers

wish to recognise and acknowledge the support provided by the nursing profession

throughout the Territory and in particular, those nurses who willingly gave of their time

to complete the surveys, tolerated our intrusions and answered our questions.


8 EXECUTIVE SUMMARY



Executive Summary

This study was commissioned by ACT Health to inform future policy decisions on

managing nursing workload in the Territory. The Australian Capital Territory (ACT) is

the smallest of Australia‟s six states and two territories. However it has the highest

population density and is the only state or territory without a sea border. The health

needs of its residents are served by only two public hospitals, The Canberra Hospital

and Calvary Public Hospital, as well as three private hospitals.

Planning and sample definition commenced during late 2006. Cross-sectional data

collection commenced September 2006 and was completed by November 2006.

Longitudinal patient data were collected from the ACT Administrative Data System for

two years (2004-2006) and nursing payroll (workforce) data where possible for the

same years, hospitals and wards.

The study of hospital (N=2) nursing wards (N=16) used longitudinal data held at

Territory levels to associate nursing workload and nursing skill mix (defined as the

percentage of RNs) to patient outcomes from 2004–2006. In-depth cross-sectional data

collected from 16 medical-surgical wards in the two hospitals in 2006 amplified the

findings. In addition, a variety of relationships between the work environment of nurses

and patient outcomes were examined, as were nurses‟ job satisfaction and intention to

leave.

The small sample across only two hospitals means that comparisons with other

studies (for example similar work conducted for NSW Health), must be viewed with

caution. NSW and ACT are different health systems and should not be compared

without careful analysis of admission and case-typing practices. Administrative

divisions such as acute, sub-acute, non-acute, daycase, admitted ED patient, non-

admitted ED and Outpatients are not standardised across health systems. However

where relevant, comparisons have been made.

The focus of this study was on medical and surgical nursing wards/units, the

operational unit where the work of patient care and cure happens, where innovation

can be most readily introduced with real consequences for patients and staff, and

where the relationship between hospital resources and patient outcomes needs to be

studied.


10 EXECUTIVE SUMMARY

This study was designed to:

a) Improve understanding of what constitutes nurses‟ workload in medical and

surgical units across the two hospitals in the Australian Capital Territory.

b) Examine whether patient acuity and length of stay (LOS) have changed over

time, and the impact on nurses‟ workload.

c) Examine the impact of skill mix (the proportion of registered nurses to total

clinical nurse staffing) on patient outcomes as adverse patient circumstances

(casemix controlled in longitudinal data).

d) Determine the impact of the nursing work environment on patient and nurse

outcomes.

This information would assist ACT Health to:

1. Identify and implement innovative models of practice and care where

applicable;

2. Identify how best to meet the health service needs of the community;

3. Identify how to achieve the capacity and capability required to meet high

standards of practice and safe outcomes.

Nursing Workload

Across Australia, the nursing work environment and consequently nursing workload,

has changed considerably over the past few years. This trend is also evidenced in the

ACT data where the ever increasing patient turnover rate is impacting on nursing hours

required to meet workload.

In the longitudinal component, nursing workload on the ward is composed of patient

requirements measured as AR-DRGs, plus movement of patients on and off wards.

Nursing workload is also influenced by the amount of time patients spend on nursing

wards – length of ward (and hospital) stay. Shorter length of stay compresses nursing

work. In the cross-sectional component nursing workload was measured using a

standardised and validated measure, the PRN-80, which estimates the hours of care

required for a patient for the coming 24 hours. Information was collected from the un-

coded medical record by trained data collectors.



Staffing levels have increased overall at Canberra Hospital during the study period.

Most ward staffing is matched to acuity adjusted patient load (workload). In contrast,

there has been an increase in the workload of nurses Calvary Hospitals during the

study period.

Using longitudinal data, the average number of different case types (AR-DRGs) per

ward was calculated. The number ranges from a low of 164 to a high of 459, from a

possible range of 613. The wider the range of DRGs cared for in a ward the greater the

workload as nurses who work on these medical and surgical units must understand the

care requirements, the pharmacology, the treatments, the protocols and preferences of

specialist medical staff for an increasingly various patient assignment.

There is a growing awareness of the impact that the movement of patients to and

from nursing wards has on nursing workload (churn). Churn includes the effect of

admission to Emergency Departments (ED) so increased rates of admission to wards

through ED increases churn. Increased throughput, combined with strategies that result

in the movement of patients as space becomes available on the most appropriate ward

for their diagnosis, also increases churn. This bed movement is in addition to patient

transfer required by the treatment regimen itself – from ward to imaging, back to ward,

and so forth. Each new admission, transfer, or discharge, requires documentation,

orientation, clinical assessment and management review, and other tasks associated

with the patient. Accompanying a patient to another ward or service may take a nurse

away from his/her assignment of patients or tasks for an unknown period of time.

In the longitudinal study patients visited 1.24 and 1.32 wards per episode at the two

hospitals in an average length of stay (LOS) in hospital of 2.9 and 3.2 days

respectively. When attention was restricted to patients who had some contact with the

wards in the study the ward visit figures became 1.64 and 1.84 respectively and the

average LOS figures were 8.9 and 6.3 respectively. Either way, the ward visits were

less than the 2.26 wards per episode found in the NSW Health study. In the cross-

sectional study “patients per bed” was calculated per ward by dividing the number of

patients per day by the number of beds. This calculation does not include bed

movements within the ward. The mean was one patient per bed per day, again less

than the 1.25 found in NSW. Both these results may reflect better bed management

strategies.



Nursing hours per patient day (NHPPD) provided varied considerably on a per day

basis (mean 6.5, range 3.7 – 11.6) and were reasonably normally distributed though

the data, indicating significant variation between and within wards. When patient needs

vary significantly, staffing is more difficult to predict and can result in an increased

workload for nurses because staffing may fail to match patient needs.

The cross-sectional study used the PRN-80 (see Table 13, page 39 for further

explanation), a standardised and validated tool (Chagnon, Audette, Lebrun, & Tilquin,

1978; O'Brien-Pallas et al., 2004) which measures the minutes of care (later translated

into hours) required (both direct and indirect) per patient for the coming 24 hours.

Information was collected from the un-coded medical record by trained data collectors.

By comparing the hours of care required (using the PRN-80) and the hours of staffing

provided taken from the ward roster, on average, approximately one half hour per day

of additional care is required to meet each patient‟s needs. In addition, there was

considerable variation across the sample. The difference between the minimum and

maximum requirements per ward-day ranged from just over 4 hours to 10.7 hours. This

degree of variability in care needs makes it difficult to predict the staffing required, and

the discrepancy between hours needed and available hours may impact on workload,

quality of care and the work environment.

Nurses self-reported an average of 1.3 tasks per nurse per shift delayed and 1.5

tasks per nurse per shift not completed. The tasks not done include a range of care

and comfort measures: talking with patients, pressure area care, oral hygiene and

patient/family teaching, mobilisation and turning patients, adequate documentation and

the taking of vital signs. Just over one-third (34.3%) of nurses reported they were

unable to comfort and talk to their patients on the most recent shift. A small response

rate was seen for night shift so statistical comparisons could not be made, but an

apparently similar rate of tasks delayed was found, with a lower rate of tasks not done.

Similar factors were influential in regard to both tasks delayed and tasks not

completed. The proportion of nurses indicating less time available to deliver care, the

amount of additional time required to complete care this shift, and the proportion of

hours worked by agency staff were common elements. As these factors increased so

did the rate of tasks delayed or not done. Additional predictors were identified in regard

to the rate of tasks not done. These included the proportion of patients admitted from a

care facility and the amount of involuntary overtime reported. An increase in the

proportion of patients admitted from a care facility led to an increase in tasks delayed.



In terms of indirect or additional nursing care activities, nearly half of the

respondents reported that these included delivery or retrieval of patient meal trays

(47%), cleaning (46%) or clerical duties (45%). Over one-third (36%) of nurses order,

co-ordinate or perform ancillary work; 29% arrange discharge referrals and transport,

while 9% transport patients. Starting IVs (35%), undertaking routine phlebotomy (17%)

or ECGs (14%) were also undertaken by nurses.

Nurse Staffing and Skill Mix

Using the longitudinal data, nursing skill mix, defined as the proportion of registered

nurses (RNs) to clinical nurse staffing (Shullanberger, 2000), is highly variable across

the sample wards ranging from 49% to 80% at Canberra Hospital and 57% - 89% at

Calvary Hospital in the final period of analysis. Skillmix was lower in wards with aged or

rehabilitation casemix, higher in specialty surgical wards. Several wards at Canberra

Hospital have had a steady increase in hours worked. At Calvary Hospital all wards

have had an increase in hours worked, although as noted previously this has not

matched increases in workload.

In the cross-sectional data most wards had between 60% and 80% RN staff. Only

twelve ward-days over six different wards employed nurses which were other than RN

and EN categories and the percentage of these “other nurse” hours worked ranged

from 0 – 7.46%, with two outliers at 22.4 and 24.5%.

There were considerable differences in the proportion of full-time to part-time, casual

or agency hours worked. There were two wards which had less than 40% full-time staff.

Part-time staff ranged from 20.3 – 52.6% and casual staff ranged from 1 – 3%. Four

wards in the sample employed no agency staff at all, while the remaining 10 wards

employed between 1 – 8% agency staff. However, there is considerable variation in

these figures when reported on a ward-day basis. The lowest percentage of full-time

hours worked on one ward-day was 10.5% and the highest percentage was 93.3%.

There were ten ward-days which had less than 40% full-time staff and two ward-days

which had more than 80% full-time staff.

There were great variations in the proportion of hours worked per ward-day by

grade. RN L1 staff worked on average 51.6% of the hours with a large range from 21 –

89.9%; RN L2 staff worked on average 16.8% with a range of between 0 – 51%; and

ENs worked 29.9% of hours, also with a large range of 0 – 66%.



Patient Outcomes

Twelve clinical Outcomes Potentially Sensitive to Nursing (OPSN) were examined in

the study. In addition, failure to rescue (death following certain OPSN) was counted in

the longitudinal data. In the cross-sectional study data were collected from un-coded

patient records or the ward reporting system and included falls (with and without injury)

and medication errors (with and without patient consequences), events that cannot be

captured in administrative data.

The statistically significant findings supported the hypothesis that more nursing

hours per patient reduces patient length of stay, but the size of the effect was small. It

was found that if the two hospitals were to increase their RN hours by 10%, only a

minor reduction of 1-2% in patient length of stay would result. However when patient

outcomes as Outcomes Potentially Sensitive to Nursing (OPSN) were examined, it was

found that increasing RN hours by 10% could produce decreases in the adverse event

rates studied from 11% to 45%.

In the cross-sectional study 26 (4.3%) patients in the study were found to have

experienced a fall with or without injury, and some of these patients had experienced

both types of fall. Two patients experienced medication errors without consequences.

Out of the 601 patients studied, 34 (5.7%) experienced time-based medication errors,

lower than found in the NSW study. Falls also were lower in the B1 hospital but higher

in the A hospital than in NSW data. As a result of the low rates of adverse events, no

relationships could be established.

Work Environment

The cross-sectional design provided insight into nurses‟ perceptions of their working

environment, their ability to practice comfortably, and the relationship between nurses‟

perceptions and patient outcomes.

Most nurses (88%) rated the quality of care as excellent or good over the past shift.

When asked to indicate whether the quality of care given over the last 12 months had

changed on their wards, 80% of respondents indicated that it had improved or

remained the same.



Results from the Nursing Work Index-Revised (NWI-R) indicate that on four of the

five measures, that is, nurse autonomy, nurse control over practice, nurse-doctor

relationships and resource adequacy, nurses in ACT scored higher than did nurses in

NSW. Nurse leadership was slightly lower in the ACT data than NSW. Higher levels of

autonomy, control over practice and nurse-doctor relations correlated with lower

discrepancy between nursing demand and supply (hours of care required compared to

those provided). Conversely, a high nursing demand/supply figure (indicating wider

discrepancy between hours of care required and that supplied) related to lower levels

of autonomy, control over practice and nurse-doctor relations.

When asked whether they had experienced a physical or emotional threat or actual

abuse during the last five shifts, 33% of respondents experienced emotional abuse but

up to a maximum of 58% of staff on a ward did. In terms of threat of violence only 21%

experienced this and while there were wards where no staff experienced a threat of

violence, up to a maximum of 67% of staff on a ward did. The results are similar for

physical violence where 15% of staff experienced this in the past five shifts and up to

58% of staff on a ward did so. The source of violence was nearly exclusively patients

and families. Patients and families were responsible for most physical assaults (96.6%)

and threats of assault (95.1%) and emotional abuse (69.7%).

Nurse Outcomes

71.5% nurses were satisfied with their job and even more (79.5%) were satisfied

with the profession. Furthermore 74% do not intend to leave their current job in the next

12 months. Job satisfaction increased with greater satisfaction with nursing, resource

adequacy and total nursing hours provided, while decline in job satisfaction was related

to increases in the number of shifts missed and increased age of the respondent.

Nurses who were satisfied with their job and who perceived they had adequate

resources were more likely to be satisfied with their profession, while those in

temporary employment were less satisfied with nursing. A higher patient turnover also

predicted satisfaction with nursing.

Nurses were more likely to intend to leave their current job if they were required to

re-sequence their work frequently, if there was a higher proportion of agency hours

worked on their ward and if demand for nursing care per day exceeded supply. Nurses

who had worked longer and who were satisfied with their job were less likely to plan to



leave. Nurses indicating they had more time to deliver care per shift were more likely to

leave. Those working on wards with a higher proportion of patients waiting for a care

facility were less likely to intend to leave.

There was considerable variability between the wards. Overall, the study provides

insight into patterns in nursing staffing, the work environment and patient outcomes in

ACT public hospitals. The results suggest that to successfully manage a hospital

system requires an understanding of the nature of the work and a commitment to

matching resources to workload.




Table of Contents.

1. Introduction ........................................................................................ 20

Purpose and Objectives ..................................................................................... 21

Organisation of the Report ................................................................................. 21

Glossary ............................................................................................................. 22

1.1 Literature Review .................................................................................... 27

2. Study Design & Ethics Approval ...................................................... 34

Study Design ...................................................................................................... 34

Ethics Approvals ................................................................................................ 35

3. Samples and Data Collection ............................................................ 36

Longitudinal Component .................................................................................... 36

Cross-sectional Component .............................................................................. 36

3.1 Data Analysis ........................................................................................... 41

Longitudinal Analysis ......................................................................................... 41

Cross-sectional Analysis .................................................................................... 50

4. Findings .............................................................................................. 53

4.1 Longitudinal Findings ............................................................................. 53

Patterns in Skill Mix............................................................................................ 53

Patterns in Staffing Levels ................................................................................. 62

Findings for OPSN other than ALOS ................................................................. 67

Conclusion ......................................................................................................... 73

4.2 Cross-sectional Findings ....................................................................... 75

Patient Characteristics ....................................................................................... 75

Nurse Characteristics ........................................................................................ 76

Ward Characteristics ......................................................................................... 80

Skill Mix Characteristics ..................................................................................... 81

Nursing Workload .............................................................................................. 89

Work Environment ............................................................................................. 93

Quality of Care ................................................................................................... 95

Violence Experienced ...................................................................................... 101

Satisfaction and Intention to Leave.................................................................. 102

Patient Outcomes ............................................................................................ 103

Outcome Predictors ......................................................................................... 105

Nurse Outcomes .............................................................................................. 107

5. Limitations ........................................................................................ 111

6. Summary and Discussion ............................................................... 112

7. References ....................................................................................... 117

8. Appendices....................................................................................... 121



20 INTRODUCTION

1. Introduction

Nurse staffing in Australian hospitals has received greater attention recently with

projections that the current shortage of nurses is unlikely to abate, particularly as the

workforce ages. An overall annual increase in demand for nurses of 2.56% until 2010

has been predicted, with 180,552 Registered Nurses (RNs) being required by that time.

A shortfall of approximately 40,000 is expected (Access Economics, 2004a; Karmel &

Li, 2002). Current workforce predictions indicate that the retirement of large numbers of

nurses in the „baby boomer‟ age bracket and the lower age at which female nurses

retire will exacerbate current shortages (Schofield & Beard, 2005). It is possible that

half the nursing workforce will be retired within 15 years (ARHRC, 2005). Efforts to

recruit more people into the profession without addressing retention will not have a

sustainable impact unless measures are undertaken to understand and address

nursing workload and the quality of the work environment for nurses. These factors

have been shown to have a significant impact on patient outcomes.

Much of the nursing workforce comprises general (although still highly specialised)

medical and surgical nurses. Not only are the majority of hospitalised patients found in

general medical/surgical wards, but also, it is frequently these nurses who move to

more specialised clinical areas such as intensive care, midwifery or mental health

where there are already documented shortages (AHWAC, 2002a, 2002b, 2004; VDHS,

1999). This study was commissioned to examine factors which impact on nurses‟

workload, particularly at the ward/unit level (medical and surgical) but in addition,

examines the relationships between patient outcomes, the nursing work environment,

nursing skill mix and workload. Study at the ward level enables a greater understanding

of the relationships between the factors mentioned above but more importantly, can

provide greater insight for those charged with responsibility for managing staff and

patient care.



Purpose and Objectives

This study examined several questions fundamental to the design and

implementation of optimal models of nurse staffing within ACT, collecting data from two

time perspectives – longitudinal and cross-sectional, in order to:

a) Improve understanding of what constitutes nurses‟ workload in medical and

surgical units across the two public hospitals in the Australian Capital

Territory.

b) Examine whether patient acuity and length of stay (LOS) have changed over

time, and the impact on nurses‟ workload.

c) Examine the impact of skill mix (the proportion of registered nurses) on

patient outcomes as adverse patient circumstances (casemix controlled in

longitudinal data).

d) Determine the impact of the nursing work environment on patient and nurse

outcomes.

This information would provide a basis for ACT Health to:

1. Identify and implement innovative models of practice and care where

applicable;

2. Identify how best to meet the health service needs of the community;

3. Identify how to achieve the capacity and capability required to meet high

standards of practice and safe outcomes.

Organisation of the Report

The longitudinal context provided by two years of ACT administrative data grounds

understanding of data collected at “the coal face” in the cross-sectional design in one

eight week period of time. In the interests of readability, most of the detail about data

acquisition, management and measurement are contained in Appendices.

Throughout the Report, we will move from descriptions of patients and their

experiences to nursing workforce as skill mix, hours of care and back to patient

outcomes. Nursing resources cannot be understood without understanding the context


22 INTRODUCTION

in which nursing is practiced – the work environment, the patients who require care and

the staff providing that care.

To assist the reader, a glossary of terms used in the various methodologies is

presented on the following pages.

Glossary

TABLE 1 DEFINITION OF WARD TYPES

Type Cross-sectional Study Longitudinal Study

Medical Wards designated as Specialty Medical or Medical by the hospital

Wards with a casemix of predominantly Medical AR-DRGs. Calculated per year.

Surgical Wards designated as Specialty Surgical or Surgical by the hospital

Wards with a casemix of predominantly Surgical AR-DRGs. Calculated per year.

General, Mixed Medical-Surgical

Wards designated as Medical-Surgical by the hospital

Wards with no predominant casemix. Calculated per year.

Other N/A Other ward types such as Intensive Care Units, Emergency Departments, and Day Units

Ward type selection in the longitudinal component was made for fairly broad AR-

DRG case-types and overnight stays. Please note the difference in definitions between

the two methods. One of the difficulties in this study was recognising that what a

hospital defined as a medical or surgical ward for example, might well be an historical

label not supported by casemix analysis.



TABLE 2 LONGITUDINAL COMPONENT: OUTCOMES POTENTIALLY SENSITIVE TO NURSING (OPSN) DEFINITIONS*

Item Detail

Central Nervous System (CNS) Complications

Complications such as confusion or delirium. Nurses intervene to create a supportive environment, such as structuring sleep patterns etc.

Deep Vein Thrombosis/Pulmonary Embolism (DVT/PE)

Thromboses (blood clots) are frequently related to periods of immobility. Early and frequent mobilisation of patients is an important activity performed by nurses.

Decubitus Ulcer (Pressure ulcer)

Decubitus ulcers are caused by prolonged pressure on skin areas, usually due to immobility. Mobilisation and positioning of patients are central activities performed by nurses.

Gastrointestinal Bleeding (Ulcer/GIB)

In most cases, gastrointestinal ulcerations and bleeding are stress-related complications in hospital patients. Nursing plays a role in relieving psychological stress and detecting ulcers at an early stage.

Pneumonia Two key risk factors for hospital-acquired pneumonia are prolonged immobility, which leads to inadequate ventilation of parts of the lungs, and inappropriate or failure to perform pulmonary hygienic techniques. Nursing care influences both risk factors.

Sepsis Sepsis, defined as life-threatening and systemic infection, can result when a hospital-acquired infection is left untreated. The two most frequent hospital-acquired infections (UTI and pneumonia) are considered to be nursing sensitive.

Shock/Cardiac Arrest Both pulmonary failure and cardiac arrest are endpoints to a continuous deterioration in a patient‟s status.

Urinary Tract Infection (UTI)

UTI is a frequent complication in hospitalised patients, particularly those with indwelling urinary catheters. Infection can result from inattention to sterile techniques when placing indwelling urinary catheters or from inadequate attention to time consuming toileting programs for incontinent patients.

Failure To Rescue (FTR)

Defined as mortality of patients who experienced a hospital-acquired complication, studies have shown failure to rescue to be related to hospital quality and nursing. The underlying rationale is that excellent care is more likely to prevent patients with complications from dying. Operationally defined here as death following sepsis, shock, GI bleeding or DVT.

Physiologic/Metabolic Derangement

Imbalances in electrolytes and blood sugar levels are very common in hospital patients. Given the central role of nurses in patient monitoring and reporting abnormal lab values to the treating team, slight imbalances can be caught quickly and corrected in a timely manner in well-staffed hospitals.

Pulmonary Failure Both pulmonary failure and cardiac arrest are endpoints to a continuous deterioration in a patient‟s status.

Surgical Wound Infection

Because nurses are responsible for pre-operative preparation of patients, which includes skin cleansing and administration of antibiotics, surgical wound infections could be influenced by the quality of nursing care.

Mortality A number of studies have related mortality to nurse staffing patterns in hospitals.

Length Of Stay (LOS) Nurses play an important role in discharge planning. They can ensure that a patient is not discharged prematurely or kept in the hospital for too long and thereby expose them to hospital acquired complications.

* Adapted from Needleman, et al. (2001)


24 INTRODUCTION

TABLE 3 CROSS-SECTIONAL STUDY: NURSING WORK INDEX - REVISED FACTORS

Factor Possible Score Range*

Sample Items from NWI-R

Autonomy 6-24 Freedom to make important patient care and work decisions

Not being placed in a position of having to do things that are against my nursing judgment

A nurse manager or supervisor who backs up the nursing staff in decision making, even if the conflict is with a physician

Control Over Practice

7-28 Adequate support services allow me to spend time with my patients

Enough time and opportunity to discuss patient care problems with other nurses

Patient care assignments that foster continuity of care

Nurse-Doctor Relations

3-12 Collaboration between nurses and physicians A lot of team work between nurses and physicians Physicians and nurses have good working relationships

Leadership 12-48 A nurse manager or immediate supervisor who is a good manager and leader

Support for new and innovative ideas about patient care

A clear philosophy of nursing that pervades the patient care environment

Resource Adequacy

4-16 Enough registered nurses on staff to provide quality patient care

Enough staff to get work done

* Higher scores indicate the factor was stronger

TABLE 4 CROSS-SECTIONAL STUDY: ENVIRONMENTAL COMPLEXITY SCALE FACTORS

Factor Possible Score Range*

Sample Items from ECS

Re-sequencing of work in response to others

0-10 Clarifying doctor's orders Medications, supplies and narcotic keys missing Completing work of others

Unanticipated changes in patient acuity

0-10 Stat blood work Extra vital signs Greater demand for psychosocial support for patient

Composition and characteristics of the care team

0-10 Students on the unit required supervision and assistance

Students wanted access to charts, equipment and supplies

Scheduled unit staff absent this shift

* Higher scores indicate the factor was stronger



TABLE 5 CROSS-SECTIONAL COMPONENT: OUTCOME DEFINITIONS

Item Definition

Falls with Injury The patient experienced a fall occasioning an injury

Falls without Injury The patient experienced a fall without injury

Falls (any) The patient experienced a fall, with or without injury

(ie „number of patients who experienced any type of fall‟)

Medication Errors with Patient Consequence

The patient experienced a nurse medication error that occasioned adverse consequences

Medication Errors without Patient Consequence

The patient experienced a nurse medication error without adverse consequences

Medication Errors (any) The patient experienced a nurse medication error with or without adverse consequences

Time-based Medication Error Medication delivered more than 30 minutes outside the prescribed time

TABLE 6 CROSS-SECTIONAL COMPONENT: DATA ANALYSIS TIME PERIOD DEFINITIONS

Item Definition

Ward Data for the sample period from a single hospital ward

Sample period = 5 days: Monday-Friday

Ward Day Data for a 24 hour period from a single hospital ward

Shift ECS and related data collected per (self-reported) shift

Shift-period Three equal „shift-periods‟ calculated from ward staffing (roster) data:

0700-1500 (Morning); 1500-2300 (Evening); 2300-0700 (Night)

TABLE 7 CROSS-SECTIONAL COMPONENT: OTHER DEFINITIONS

Item Definition

Staffing hours Data from the ward roster for the 24 hour period, excluding leave and other hours off-ward (see also collection form page 151)

Hours of nursing care required

Hours of nursing care needed per patient for the next 24 hours; data collected by trained data collectors with the validated PRN-80 instrument (see also Table 13 Instruments, page 39)

TABLE 8 CROSS-SECTIONAL COMPONENT: PROPORTION HOURS WORKED GRADE CATEGORIES

Item Definition

RN Registered Nurse: Sum of RN L1 & RN L2 Hours

RN L1 Registered Nurse Level 1 Hours

RN L2 Registered Nurse Level 2 Hours

EN Enrolled Nurse Hours (levels not differentiated in all ward roster data)

AIN Assistant in Nursing Hours


26 INTRODUCTION

TABLE 9 STATISTICAL TERMS

Term Description*

Probability Estimate/Statistical Significance

Significance is the percent chance that a relationship found in the data is random. A probability estimate of 0.05 = 5% chance. Lower values indicate a lower chance of a random relationship.

Correlation Coefficient Correlations measure how variables are related. Values range from 0 (no or random relationship) to 1 (perfect relationship: "The more the x, the more the y, and vice versa.") or -1 (perfect negative relationship: "The more the x, the less the y, and vice versa."). It is a symmetrical value, not providing evidence of which way causation flows.

Regression Regression is used to account for (or predict) the variance in a dependent variable, based on combinations of independent variables.

Multiple regression can establish that a set of independent variables explains a proportion of the variance in a dependent variable.

Logistic regression is a form of regression used when the dependent variable is dichotomous.

Regression Coefficient The average amount the dependent variable increases when the independent variable increases one unit and other independents are held constant. The larger this coefficient the more the dependent variable changes for each unit change in the independent. If all independent variables are measured on the same scale then regression coefficients are directly comparable; but if not then beta (β) weights may be calculated.

Beta (β) Weight The average amount the dependent variable increases when the independent increases one standard deviation and other independent variables are held constant. They display the relative predictive importance of the independent variables. Betas weights reflect the unique contribution of each independent variable, but do not account for the importance of a variable which makes strong joint contributions to the regression model.

R2 Value/Adjusted R2 Value/Pseudo R2 Value

R2 is the percent of the variance in the dependent variable explained uniquely or jointly by the independent variables (i.e. the model overall). A large value indicates that a large fraction of the variation is explained by the independent variables.

Adjusted R2 is a conservative reduction to R2. It adjusts for the effect of a large number of independent variables that may artificially increase R2.

Pseudo R2 provides an approximate measure of the explanatory power of Poisson regression models used in this analysis. Not considered equivalent to R2 or Adjusted R2.

Cronbach's Alpha (α) A commonly used measure of scale reliability. Higher values are better. Values above 0.70 are acceptable in the social sciences.

-2 Log Likelihood Value Measure of goodness of fit. Used to assess the relative fit of each regression model.

* (Garson, 2005; Goldstein, 2003; Sheshkin, 2000)



Literature Review

The current nursing shortage in Australia has been well documented (AHWAC,

2002a, 2002b, 2004). In 2006, estimates of up to 12,270 new nurses were needed to

enter the profession to keep up with health care needs (AHWAC, 2004), and a shortfall

of approximately 40,000 nurses is expected by 2010 (Access Economics, 2004b;

Karmel & Li, 2002). This scenario will likely be detrimental to patient outcomes and

nurse turnover rates as workloads increase, job satisfaction rates decrease and nurses

find alternative employment (Duffield, O'Brien-Pallas, & Aitken, 2004). In light of these

projections it is becoming more important to employ strategies to help retain nursing

staff by addressing issues of work environment, skill mix, workload, job satisfaction,

and the relationship between these and patient outcomes. Without efforts to sustain the

existing nursing workforce, attempts to recruit more nurses will likely be short-lived and

unsuccessful.

Nursing work has changed considerably in recent years and a range of factors have

been identified which impact on nurses‟ workload. These include an increased ageing

population (including both nurses and patients), increased patient acuity, new

diseases, treatments and technologies, and changing employment patterns (AIHW,

2005; Karmel & Li, 2002). Nurse managers have had to become more creative in

staffing and patient allocations to try to maintain standards of care and positive patient

outcomes as skill mix and the workforce profile have changed.

Skill mix

The different categories of health care workers who provide care to patients is

termed „skill mix‟ or „staff mix‟ (McGillis-Hall, 1997). Skillmix is defined as the proportion

of registered nurses to total clinical nurse staffing (Aiken, Sochalski, & Anderson, 1996;

Shullanberger, 2000). It is argued that a lesser qualified skill mix may result in

increased nurse turnover and unproductive time (Orne, Garland, O'Hara, Perfetto, &

Stielau, 1998), and others have tried to clarify roles of unlicensed and untrained

personnel (McKenna, Hasson, & Keeney, 2004). Other large studies have found that a

higher proportion of RNs on medical and surgical wards was associated with better

outcomes in terms of morbidity and mortality (Estabrooks, Midodzi, Cummings, Ricker,

& Giovannetti, 2005; O'Brien-Pallas et al., 2004; Tourangeau et al., 2006). Critical in

these is the proportion of registered nurse hours worked as compared to other

categories of employee – regulated nurses such as enrolled nurses or licensed


28 INTRODUCTION

practical nurses or unregulated workers such as health care assistants, assistants in

nursing.

Work environment

There is increasing emphasis on the work environment of nurses because of its

potential in retaining nurses and ensuring positive patient outcomes. Many years ago in

the United States (USA), a number of hospitals were labelled „Magnet‟ institutions –

“good places for nurses to work”. Nurses in these facilities were deemed central to the

hospital and as a result of this philosophy, had higher job satisfaction and retention

rates (Kramer & Schmalenberg, 1991). These institutions were found to have a 4.6%

lower patient mortality when compared with non-magnet hospitals (Aiken, Smith, &

Lake, 1994). A more recent study also found that attractive organisational

characteristics are key factors in nurse retention. An increased workload and having to

leave basic nursing tasks undone were also found to be fundamental to nurses‟ levels

of job satisfaction and retention rates (Aiken et al., 2001). A collegial working

environment, opportunities for nurse education, a richer skill mix and continuity of care

have also been linked to lower patient mortality levels (Baumann, O'Brien-Pallas et al.,

2001; Estabrooks et al., 2005).

Nurses‟ job satisfaction is affected by the perception of control over their work (Finn,

2001; Laschinger, Finegan, Shamian, & Wilk, 2004; Rafferty, Ball, & Aiken, 2001;

Stamps & Piedmont, 1986; Tillman, Salyer, Corley, & Mark, 1997). The Nursing Work

Index – Revised (NWI-R), used in the ACT study, is a measure of the work

environment. It has 49 items that measure nurse autonomy, control over practice,

nurse-doctor relations, nursing leadership and resource adequacy. The NWI-R was first

developed in the US and has since been refined and used widely including in Australia

(Aiken & Patrician, 2000; Aiken & Sloane, 1997; Aiken et al., 1994; Estabrooks et al.,

2002; Kramer & Hafner, 1989). Also used in this study was the Environmental

Complexity Scale (ECS) (O'Brien-Pallas, Irvine, Peereboom, & Murray, 1997) used

previously in Australia (Duffield et al., 2007). This tool has three domains:

resequencing of work in response to others‟ requests; unanticipated changes in patient

acuity; and characteristics and composition of the caregiver team. Nurses are also

asked whether nursing interventions were left undone or delayed due to lack of time.

Use of both of these tools provides a comprehensive measurement of nursing work

and the factors impacting on it.



Nursing care environments and the organisation of nursing care have been linked to

adverse patient outcomes such as medication errors, increased length of stay and

mortality (American Nurses' Association, 1997; Czaplinski & Diers, 1998; Estabrooks et

al., 2005; Grillo-Peck & Risner, 1995; Needleman, Buerhaus, Mattke, Stewart, &

Zelevinsky, 2002; Tourangeau, 2002; Tourangeau et al., 2006). Recent research

suggests that adverse patient events and nurses‟ emotional exhaustion are directly

affected by the quality of the work environment (Laschinger & Leiter, 2006). Aiken,

Clarke & Sloane (2002) report that understaffing leads to greater nursing turnover

because nurses are being prevented from providing the quality of care that they wish,

compromising patient care. Clarke and Aiken (2006) also argue that nurse productivity

could improve if there were improved work environments.

Workload

In Australia, there are many ways of allocating nursing resources which are not

related to types of patient or ward specialty (except intensive care and high

dependency units) (Duffield, Roche & Merrick, 2006). Some measures used include

nursing hours per patient day (NHPPD) (Western Australia). A „nurse to patient‟ ratio

has been adopted in Victoria which is designed to promote equal workload amongst

nurses (Plummer, 2005). Unruh & Fottler (2006) found this method may underestimate

nursing workload, and Graf et al. (2003) suggest such a method may produce

inflexibility which could exacerbate staffing and quality issues.

Other methods that measure nursing workload are patient dependency or patient

acuity systems. In the early 1980s in Australia, PAIS (Patient Assessment and

Information System) was introduced into Victoria (Hovenga, 1996). The resources

required (hours of nursing) for a given PAIS category had been developed from a

number of work sampling studies and included time for administrative work and indirect

nursing activities (Goodwin & Hawkins, 1990; Hovenga, 1996). These nursing activities

include direct patient care and indirect nursing care such as documentation and within

the PAIS model, patients are classified on a per shift, daily, weekly, monthly, random or

ad hoc basis to reflect the workload at a particular point in time. Software packages,

such as E-care (D. E. Goldstein, 2003) and TrendCare (Trend Care Systems Pty Ltd,

2004), involve nurses using care plans or clinical pathways, determining the time

necessary for each „unit of care‟, and establishing patient requirements from these

parameters.


30 INTRODUCTION

Nursing workload can be impacted by many factors such as the number of case

types (Diagnostic Related Groups [DRGs]) nurses have to care for (Diers & Potter,

1997); the degree of patient turnover and churn (movement of patients between and

within wards) (Duffield et al. 2007); the increased throughput of patients (Unruh &

Fottler, 2006); their length of stay and acuity (Birch, O'Brien-Pallas, Alksnis, Murphy, &

Thomson, 2003); and staff shortages (Buerhaus, 1997). The decreased length of

patient stay in hospital and the concentration of, and increase in nursing work that this

requires, has not been widely studied (Graf et al., 2003).

Diers and Potter (1997) present a case study of an overspent and difficult to

manage ward. It became apparent that a large number of different DRGs (casemix)

contributed to the apparent disorganisation. Some studies argue for similar patient

types to be organised on specialised wards to enhance expert nursing care (Aiken,

Lake, Sochalski, & Sloane, 1997; Czaplinski & Diers, 1998; Diers & Potter, 1997). The

argument is that it is unreasonable for nurses to be expert in all manner of patient

types/specialities, and that by narrowing the demands on their expertise, they would

work more efficiently and improve patient outcomes. Case mix cohorting may help

managers predict nursing care requirements more efficiently, because when patient

needs vary in intensity on a day-to-day basis, nurse staffing requirements are more

difficult to anticipate: patient needs may not be met.

The nursing work environment, and consequently nursing workload, has changed

considerably over the past few years. As a result of technology and efficiency policies

that target length of stay, nurses have a more complex patient load (Baumann,

Giovannetti et al., 2001; Birch et al., 2003). The increased turnover of patients or

„churn‟ intensifies the nursing workload further. Birch (2003) found that after hospital

restructuring in Ontario (Canada) there was an increased number of severity-adjusted

patients using fewer beds cared for by fewer nurses. Patient throughput increased by

12% and inpatient episodes per bed increased by over 25%. Unruh & Fottler (2006)

found that patient turnover (in their sample of up to 205 hospitals) significantly

increased from 1994 to 2001 and that as a consequence, staffing requirements and

workload for nurses may be underestimated. Admission and discharge of patients

means extra documentation, educational, general nursing and organisational duties,

thereby increasing nursing workload. The movement of patients within wards is also a

factor in nursing workload, and one that is harder to quantify. However some wards will

have systems of management whereby it is necessary to move patients from area to

area on a regular basis (eg. from high to low acute areas). Nurses are also called upon



to assist with these when transferring patients between wards, and, depending on

resources, can be required to move the bed themselves. Nursing workload can be

further increased by nurses needing to accompany patients for investigations in other

departments (eg. CT or MRI scans), leaving their allocated patients in the care of a

colleague who already has his/her own patient load.

Another factor impacting on nursing workload is a general shortage of allied health

professionals in Australia (DEWR2006). This includes occupations such as

physiotherapists, occupational therapists, speech pathologists, radiographers and

pathologists. This shortage of staff may cause delays in patient treatment, and an

increased workload as nurses try to incorporate into their day the types of care patients

should ideally receive from these professionals.

Patient Outcomes / Outcomes Potentially Sensitive to Nursing (OPSN)

Nurses are the health professionals that are most directly involved with patients.

They monitor patients‟ progress, assess clinical changes, intervene when appropriate

and are central to communication and coordination among the allied health team.

Patient safety has been defined as „freedom from accident, or, more broadly, avoiding

injuries to patients from the care that is intended to help them‟ (IOM, 1999, 2001).

Ingersoll (1998) defined patient outcomes as the „end result of treatment or care

delivery‟.

Outcomes Potentially Sensitive to Nursing (OPSN) have been the focus of a number

of studies (Buerhaus, 1999; McCloskey & Diers, 2005; Needleman et al., 2002;

Needleman, Buerhaus, Mattke, Stewart, & Zelevinsky, 2001). Needleman et al. (2001)

found that lower levels of RNs were linked to higher rates of urinary tract infections,

pneumonia, shock and cardiac arrest, upper gastrointestinal bleeding, „failure to

rescue‟ (FTR), and length of hospital stay in both medical and surgical patients treated

in hospitals. FTR has been suggested as a better gauge of care quality than

complications alone (Clarke & Aiken, 2003), the term having been introduced by Silber

et al. (1992) to describe how patients are „rescued‟ from events that complicate their

health by nurses and other health care professionals. FTR is operationally defined as

death following adverse events such as sepsis, DVT, GI bleeding, cardiogenic shock

and hospital-acquired pneumonia.


32 INTRODUCTION

The meta-analysis by Kane et al. (2007), established that an increase in RN staffing

was associated with a reduction in patient mortality, adverse events and FTR. This

study found that for surgical patients, an increase of one full-time RN a day was

associated with a reduction in the relative risk of FTR, and nosocomial bloodstream

infections. Similarly, in intensive care facilities, a similar increase in staffing consistently

decreased rates of cardiopulmonary resuscitation, unplanned extubation, pulmonary

failure and nosocomial pneumonia.

In the USA, the Nursing Care Report card (1997) was developed to monitor nursing

care in acute care settings. It was based on data collected by state agencies in 1992

and 1994 from 502 hospitals in California, Massachusetts, and New York. The purpose

of the study was to quantify nurse staffing, patient incidents, and lengths of stay at the

hospitals, as well as the relationship between these variables. Upon evaluation, the

American Nurses Association (1997) found that preventable conditions, such as

pressure ulcers, pneumonia, post-operative infections and urinary tract infections were

inversely related to RN skill mix and nurse staffing. Similar results were found by

Kovner & Gergen (1998) and more recently, Cho et al. (2003). The Institute of Medicine

(2004) suggested that lower levels of nursing staff (especially RNs) are related to

increases in length of stay, hospital acquired infections and the incidence of pressure

ulcers. Tourangeau (2006) found that by increasing the percentage of RNs by 10%,

there were six fewer deaths for every 1000 discharged patients. In New Zealand an

increase in the percentage of RNs together with a decreased number of nursing hours

per patient per day increased negative patient outcomes (McCloskey & Diers, 2005).

Recently there has been a subtle change in language from OPSN to „nursing (or

„nurse‟) sensitive outcomes‟. This originated in the USA and is now seen to be the

accepted term (Kane et al., 2007; Person et al., 2004).

Accurate information about safe and optimal ward staffing catering to different

patient types is only possible on a ward-level shift-by-shift basis. Study at this level

gives a clearer understanding of the ward environment and its effect upon nursing

practice and patient outcomes. To date, few published studies have been based at the

ward level (Boyle, 2004; Diers, Bozzo, Blatt, & Roussel, 1998; Diers & Potter, 1997).



Context

The ACT is the smallest of Australia's six states and two territories, but has the

highest population density and is the only state or territory without a sea border. At 30

June 2006, the Australian Capital Territory (ACT) had an estimated resident population

of 334,200 persons, with the majority residing in Canberra and nearby surrounds. The

Canberra-Queanbeyan Statistical District had a population of 381,400 persons at June

2006. This is 1.8% of Australia's total population making it the eighth largest major

population centre in Australia, larger than the capital cities of Hobart and Darwin

(Australian Bureau of Statistics, 2007 -a, 2007 -b).

Public in-patient hospital services in the ACT are provided at The Canberra Hospital

and Calvary Public Hospital. In-patient hospital services for private patients in the ACT

are provided by Calvary Private Hospital, John James Memorial Hospital and the

National Capital Private Hospital. According to the Australian Institute of Health and

Welfare (AIHW, 2007), there were 72,136 public hospital separations in the ACT during

2005–06, 1.6% of the nearly 4.5 million public hospital separations nationally.


34 STUDY DESIGN & ETHICS APPROVAL

2. Study Design & Ethics Approval

Study Design

The study was designed to include both longitudinal data extracted from

administrative data systems for the two year period and cross-sectional data collected

within this time frame for medical and surgical wards in ACT hospitals.

The longitudinal component of the study included:

Patient data extracted from the ACT Administrative Data

System for two years (2004-2006)

Nursing payroll (workforce) data where possible for the same

years and hospitals.

These data allow the determination of the relationship of nursing resources as paid

hours worked, to patient outcomes as Outcomes Potentially Sensitive to Nursing

(OPSN) (Needleman et al., 2001) controlling for casemix as AR-DRGs and hospital

type. The nursing payroll data allow specification of nursing resources by skill mix, in

the context of patient load as case type, patient volume, ward type (medical-surgical or

other).

The key aspects of the data collected from the cross-sectional sample of hospital

wards are:

Ward organisation/environmental characteristics

Nursing workload and environmental complexity

Nurse outcomes as intent to stay/leave present job or the

profession

Patient characteristics

Patient outcomes as adverse events that cannot be captured in

administrative data (falls with and without injury and medication

errors with and without consequences).

The use of two compatible methodologies provides a powerful design in which the

known inadequacies of administrative data can be balanced by the cross-sectional data

collection and the known issues of labour intensive but small sample data collection



can be informed by the use of large, longitudinal datasets (Jiang, Stocks, & Wong,

2006).

A conceptual model based in General Systems Theory guided the study. The model

is presented in Appendix 1. Both a process and an outcome approach were taken in

the study.

Ethics Approvals

Ethics approval was sought and gained from the Human Research Ethics

Committee, University of Technology, Sydney, from ACT Health and Community Care

Human Research Ethics Committee, and from Calvary Health Care ACT Human

Research Ethics Committee. Approval from all committees included cross-sectional

and longitudinal components of the study. Participants were assured that no individual

or ward would be identified in any report or publication derived from the study, although

it is not possible to disguise the two participating hospitals completely. Where data

were analysed and reported at ward level, wards were deideintified using alphanumeric

codes.


36 SAMPLES & DATA COLLECTION

3. Samples and Data Collection

Longitudinal Component

The data sources for this study were owned by the two ACT hospitals involved; The

Canberra Hospital and Calvary Public Hospital. Data on patients were held by ACT

Health as part of its mandatory hospital morbidity collection and patient level ward

history data. Data pertaining to the nursing workforce, specifically nurse rostering and

payroll data were held by the two individual hospitals.

Data of the two types were received from both hospitals for the period Aug/Sep

2004 to Oct/Dec 2006, inclusive. Data were available for a total of 398 ward months.

Details of the patient (Table 10) and nursing (Table 11) data sample are presented

below (see also Table 31, page 62).

TABLE 10 LONGITUDINAL COMPONENT PATIENT DATA

Hospital Separations ALOS on Sample

WARD (hrs)

ALOS in HOSP (hrs)

Total Patient

Hours

Ward per Episode (churn)

82 28407 253.46 340.76 4,744,347 1.34

83 12031 192.80 274.03 1,469,489 1.42

TABLE 11 LONGITUDINAL COMPONENT NURSE DATA

Hospital No.

Nursing Shifts

RN Hrs EN Hrs AIN Hrs Total

Nursing Hours

82 262,980 581,136 1,146,393 182 1,727,529

83 45,939 89,986 245,256 0 335,242

Cross-sectional Component

Sixteen medical-surgical hospital wards consented to participate, 12 from The

Canberra Hospital where 158 nurses participated, and four at Calvary Hospital where

42 nurses participated in the study (see Table 12). No data were collected from

obstetric, paediatric or psychiatric wards, nor from ED or outpatient areas or theatre.



Data collection commenced on the 20 September 2006 at The Canberra Hospital

and 14 November at Calvary Hospital, and was completed by 12 October and 30

November 2006 respectively. Five experienced nurses were seconded from the

hospitals under study, and were trained to undertake data collection with support from

UTS staff. No eligible wards declined the invitation to participate. Each ward had one

week of data collection randomly assigned within the sampling period allocated for

each hospital.

Orientation sessions were held with each ward in the week before data collection

and nurses‟ consent obtained. Staff unable to attend and casual or agency staff were

given an information sheet, consent form and copy of the survey to complete and

return to a marked box at the nurses‟ station or by reply-paid post. Nurses were given a

study identification (ID) number. All nurses on the 16 nursing wards selected were

invited to participate.

The Nurse Survey captured information on nurse demographics, the work

environment and organisational attributes. At the end of each shift, nurses were asked

to complete the Environmental Complexity Scale which acquired information on ward

factors that influence nurses‟ ability to provide the required care for patients, in addition

to details of nursing interventions delayed or not done and indirect care activities. The

data collector completed the PRN-80 form which measured patient acuity daily for each

patient on the ward. This instrument lists nursing interventions that nurses complete

during a 24 hour period. This instrument provided the total minutes of care (later

converted to hours) required for that patient for the coming 24 hour period.

The data collector or the Clinical Nurse Consultant (CNC) completed the Daily Unit

Staffing Profile and Unit and Hospital Profile, providing roster data and information on

the ward. Table 13 (page 39) lists the instruments used in the cross-sectional part of

the study along with their psychometric properties and where appropriate, inter-rater

reliability (see also Appendix 7).

Table 12 outlines the details of cross-sectional data collection, and the number of

responses for each instrument. Two wards were not able to provide complete roster

data for the sample period, and one ward did not provide a unit profile. These wards

were omitted from description or analyses requiring those data. However, in order to

provide as complete a report as possible, data were included where available. The

number of wards used for each analysis or description is indicated.


38 SAMPLES & DATA COLLECTION

TABLE 12 CROSS-SECTIONAL COMPONENT DATA COLLECTION

Instrument* Collection Frequency Response

Nurse Survey: Revised Nurse Work Index Scale (NWI-R); Nurse Demographics & Work Environment

Once per nurse

200 nurses

(71% of all consenting nurses)

(158 [75.2%] Canberra Hospital

42 [58.3%] Calvary Hospital)

Environmental Complexity Scale (ECS); Nursing interventions delayed or not done, and indirect care activities

Once per nurse per shift 612 shifts

Ward Staffing Form Once per ward-day

14 wards,

67 ward-days,

1292 shift-periods

Ward Adverse Events Profile Once per ward 16 wards

Unit & Hospital Profile Once per ward 15 wards

Patient Data Form Once per patient 601 patients

Workload Measurement (PRN 80) Once per patient-day 1768 patient-days

* See also Table 13, and Appendix 7

NU

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39

TABLE 13 INSTRUMENTS

Instrument Details Present study statistics Source

Revised Nurse Work Index Scale (NWI-R)

Identifies organisational attributes leading to positive patient, nurse and institutional outcomes. The four sub-scales of the NWI-R and their reliability are: nursing unit-nurse autonomy (Cronbach‟s alpha = 0.85), nurse control (0.91), nurse physician relations (0.84) and organisational support (0.84), with overall (aggregated) scale reliability of 0.96 (Aiken & Patrician, 2000). Units with higher subscale scores demonstrate higher patient satisfaction, lower mortality rates, lower nurse emotional exhaustion, and lower incidences of needlestick injuries (Aiken et al., 1997).

Cronbach‟s alpha:

Autonomy (0.63);

Control over practice (0.69);

Nurse-doctor relationships (0.67); Leadership (0.80);

Resource adequacy (0.71).

Nurse survey, administered once to each nurse in the sampled units

Nurse Demographics & Work Environment

Measures nurses‟ perceptions about their work environment and the quality of care on the unit. It also measures demographics, job satisfaction and intent to leave. This instrument (adapted from Aiken et al., 2001; O'Brien-Pallas, Doran et al., 2001) allowed us to examine links between nurse staffing, workload and types of nursing activities.

Ward Staffing Form

Used to record nurse staffing, and skill mix on each unit every shift each day during the sampling period. Key variables include: patient census, number/mix of staff working, number of agency/casual staff, nurse absenteeism, number of staff floated to/from the unit, number of staff on orientation, and nurse patient ratios.

Ward rosters retrieved by data collectors

Ward Adverse Events Profile

Number of medications given 30 minutes outside prescribed time. Adverse events reporting system on the unit

Unit & Hospital Profile

Information on hospital/unit size, use of clinical pathways and standard nursing care plans, presence of an educator, and hours of cleaning/clerical/auxiliary support available to the unit.

Ward CNC by interview

Patient

Chara

cterist

ics

&

Outc

om

es

Patient Data Form

The specific medical conditions creating the demand for nursing care and the outcomes of that care. Key variables include primary and secondary diagnoses and the medical condition most responsible for hospital stay. Since AR-DRGs are not assigned until after hospital medical records coding and patient discharge, patient records were matched to HIE data after the longitudinal data were acquired.

Patient record accessed by data collectors; supplemented by HIE data.

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Instrument Details Present study statistics Source

Nurs

ing W

ork

load

PRN Workload Measurement

(PRN 80)

Lists 214 indicators or tasks nurses complete for patients during a 24-hour period. Each indicator has a standard point value reflecting time involved completing tasks for patients; each point represents 5 minutes, and a higher total point value indicates greater amounts of nursing care required. PRN methodology has been tested extensively with several iterations since its development in 1972, and its content validity has been established by nurse experts. Chagnon et al. (1978) established the construct and predictive validity of the PRN. Recent work (O'Brien-Pallas et al., 2004) found no significant differences in workload estimates between the PRN-80 and other established systems (Grasp and Medicus), providing further support for its reliability and validity.

Inter-rater reliability: 87.8% Patient record accessed by data collectors

Environmental Complexity Scale (ECS)

Measures “tensions” nurses experience in providing care to patients to a standard outlined in nursing care plans. It taps three domains: unanticipated delays in response to others leading to re-sequencing of work; unanticipated delays due to changes in patient acuity; characteristics and composition of the caregiver team (O'Brien-Pallas et al., 1997). O‟Brien-Pallas et al. (2002), found Cronbach‟s alpha for each subscale of: 0.80 for unanticipated delays and re-sequencing of work; 0.85 for changes in patient acuity; and 0.92 for composition and characteristics of the care-giving team.

This instrument also collects information per nurse-shift on the quality of care, nursing interventions delayed or not done due to time pressures, and indirect care activities.

Cronbach‟s alpha: Re-sequencing of work (0.68); Unanticipated changes in patient acuity (0.80); Composition and characteristics of the care team (0.61).

Nurses on sampled wards, once per nurse per shift

See also Appendix 7: Instruments



Data Analysis

Longitudinal Analysis

The aim of this research was to study the relationship between nursing inputs and

patient needs (e.g. nursing workload) with a focus on outcome measures as a means

of assessing the adequacy of care. The data were longitudinal, allowing assessment of

variation in the relationship over time and hence an assessment of the relative

adequacy of nurse staffing levels at various times during the study period. It related to

two public institutions and a number of ward areas in each, allowing a degree of

generalisation to a range of circumstances arising on a ward.

The methods used in the research employ controlling for workload (through AR-

DRG casemix and activity variables) and then reviewing the impacts of staffing level.

That is, it considers the impact of changes in staffing and skill-mix relative to a “fixed”

workload. However it also offers a method for determining what staffing has been more

or less successful for a given workload from a range of workloads encountered during

the study period.

Data Preparation

Two types of patient data were requested from ACT Health. The first were coded

morbidity records at patient episode of care level. These data, known as admitted

patient care data, were provided in the format shown in Appendix 2. These gave data

elements such as hospital of treatment, start and end dates and times for the episode

of treatment, basic demographic information on the patient, along with diseases and

procedures as coded under the Australian version of the International Classification of

Diseases (ICD-10-AM) 5th Ed. and the Australian Classification of Health Interventions

(ACHI) 5th Ed. respectively. In addition, information was available on mode of

separation/type of ending of episode. The data also uniquely identified each episode of

care without identifying the patient.

The second type of patient data, termed ward history data, was provided in the

format shown in Appendix 3. These identified ward area, start and end times and a

unique morbidity data identifier of every patient having contact with the ward (and its

staff). It should be noted that short absences from the ward do not generate new ward

episode data, however prolonged absences such as visits to theatre and recovery, do.


42 FINDINGS

All records in the admitted patient care data were linked to the ward episode data to

provide a detailed ward history of the patient.

Data on nurse rostering and payroll for particular wards were provided by the two

study hospitals. These came from the computerised nurse rostering systems

(PROACT) in two formats, both reflecting the actual assignment of nurses to ward

areas rather than the planned assignment. The roster data included information on the

skill level of each nurse on a shift as well as their start and finish times. The nursing

data and patient data were then linked by ward to provide a detailed patient and nurse

profile for the ward. Although the ward identifiers used in the nursing data were not a

direct match to the ward identifiers used in the ward history collection, links could be

made between the two. These links were either made or confirmed by the staff of the

hospitals, project staff in the field or information systems staff in ACT Health. The links

settled on are in Appendix 4. There was an inconsistency in the data as originally

matched, which was resolved by combining two ward areas (ward codes 1AF & 1AI in

The Canberra Hospital).

The roster data reflected the shifts of nurses working on a ward during a given pay

period. However, for both the staffing and patient data, the focus of the study was the

wards and the events occurring there. Therefore the data were reorganised to be a

sequence of events of specified nature occurring at a specified time on the ward, for

example, the commencement of a shift by a RN qualified staff member or the transfer

to the ward of a patient in a particular AR-DRG with a particular number of hours

already spent in hospital. These reorganised data are referred to technically as

transaction records, but we treated and referred to them as Time Series. Time series

data allowed the construction of measures that could be used to assess changes in

workload. This included cumulative patient hours spent on the ward, and patient hours

spent in hospital before admission to the ward, or after discharge from the ward.

Similarly, for nursing data, measures included a cumulative count of nurses being

rostered on and off the ward, as well as the number of hours worked by the nurses.

Patient data covered a wider range of wards (n = 76), compared to the nursing data

(n =15). All wards from the nursing data were matched to corresponding patient data.

Data from wards 1AF and 1AI (Hospital 82) were combined and treated as a single

ward. In total a full nursing and patient profile was able to be provided for 14 wards

areas listed below.



TABLE 14 HOSPITALS AND WARDS PROFILED

Hospital Code Roster Ward WARD

Start Date

WARD

End Date

82 1AA 09/09/2004 21/02/2007*

82 1AB 09/09/2004 21/02/2007*

82 1AD 09/09/2004 21/02/2007*

82 1AF & 1AI 09/09/2004 18/10/2006

82 1AG 09/09/2004 18/10/2006

82 1AH 09/09/2004 18/10/2006

82 1AK 09/09/2004 18/10/2006

82 1AL 09/09/2004 21/02/2007*

82 1AM 09/09/2004 21/02/2007*

82 1AO 09/09/2004 21/02/2007*

83 2AC 26/08/2004 7/03/2007*

83 2AE 26/08/2004 7/03/2007*

83 2AJ 26/08/2004 7/03/2007*

83 2AN 26/08/2004 7/03/2007*

*cut off at 31/12/2006

Matched nurse and patient data relating to a ward covered approximately 2.5 years.

The exact periods are shown in Table 14 above. It was found in the patient records that

the majority of data with separation date after the 31/12/2006 were not yet coded,

therefore the cut off point for both nursing and patient data became 31/12/2006.

Data Considerations and Controlling for Workload

Patients commonly make contact with more than one ward area during a hospital

episode and indeed often have multiple hospital stays during a 2.5 year period. These

multiple contacts result in repeated measures on the same patient. In our time series

analysis we have ignored the presence of multiple hospital stays (in common with most

large dataset studies) and have used the patient‟s AR-DRG and prior hospital stay (in

hours) to reduce the interdependence of their consecutive ward episodes. However the

dependency that remains cannot be ignored over short periods. Therefore the study

elements were chosen to be 28 day (roster period) segments of the time series of each

study ward. The patient and workforce data for these roster periods (ward months)

were then linked and records not overlapping the study the period discarded. The final

data were a full patient and nursing profile (by ward month) for each of the 14 wards.

The data used in this study have a limitation that potentially affects the strength of

effects found. It is that the adverse events data (captured in the morbidity record) is at


44 FINDINGS

hospital episode level and does not attribute an event time or place. Therefore such

occurrences were attributed to a ward area in proportion to exposure. We felt biases

could arise through the transfer of “injured” patients from one ward area (for example a

short stay ward) to another ward area where they recovered. Therefore we controlled

for ward workloads during the contact period and placed the staffing in the role of

experimental variable.

The controlling approach used was based on clusters methodology. There were two

matchings of ward month used. The first, the load cluster, was based on the profile of

the ward months measured through:

Total patient hours for each AR-DRG

Total admissions to ward for each AR-DRG

Total hours in hospital before admission to ward for each AR-DRG

Total patient hours (a redundant variable used for consolidation)

Total ward separations

The second clustering was by assess cluster which matched ward months on a

profile of:

Total admissions to ward for each AR-DRG

Total hours in hospital before admission to ward for each AR-DRG

These methods produce relatively similar “clusters” of wards by the clinical

characteristics embedded in AR-DRGs and are therefore a form of “risk adjustment.”

Both these matchings ignore the size of the wards; they only use the patterns in the

profile variables. Other statistical controlling techniques, such as linear regression and

casemix index methodology, were used within clusters to strengthen the analyses

reported below.

The Outcome Measures

A recent development in the nursing literature has been the adoption of statistical

measures referred to as Outcomes Potentially Sensitive to Nursing (OPSN). The

OPSN algorithms were originally developed by Needleman and Buerhaus (2001). Dr

Barbara McCloskey developed the cross walks from the American ICD-9 to the

Australian/NZ ICD-10 for use with New Zealand data (McCloskey and Diers, 2005).

The OPSN definitions can be found in Appendix 5. Mapping tables from the National



Centre for Classification in Health were used to find the comparable outcome codes.

“NZ other exclusions” were used and Version 3.1 AN-DRGs were mapped to AR-DRG

Version 5.1 on the basis of the Grouper logic (Laeta Pty Ltd is a Commonwealth

Certified Grouper Developer).

Workforce (nursing hours by skill level) was then correlated with outcomes

potentially sensitive to nursing (OPSN) whilst controlling for caseload (patient hours on

wards by case-type and other features). The method used to control for caseload was

the combination of DRG casemix and matching through clustering of ward months with

like patient profiles discussed under “Data Considerations” above.

Interpretation of the results of OPSN analyses requires familiarity with the data and

methods used. Therefore we draw an extract from our earlier report to NSW Health to

explain the standard approach (see Duffield et al. 2007, pp.43-44).

The episodes of care were compared with the criteria found in Appendix 5, defining

Outcomes Potentially Sensitive to Nursing (OPSN) that are reasonably well supported

by administrative collections such as the ACT Health admitted patient care data. The

work by Needleman and Buerhaus (2001; 2002) and McCloskey and Diers (2005) has

led to the development of the following measurable concepts.

TABLE 15 OUTCOMES POTENTIALLY SENSITIVE TO NURSING

Code OPSN

1 Urinary Tract Infection

2 Decubitus

3 Pneumonia

4 Deep Vein Thrombosis/Pulmonary Embolism

5 Ulcer/Gastro-Intestinal Bleeding

6 Central Nervous System Complications

7 Sepsis

8 Shock/Cardiac Arrest

9 Surgical Wound Infection

10 Pulmonary Failure

11 Physiological/Metabolic Derangement

12 Failure to Rescue*

* Deaths following sepsis, pneumonia, GI bleeding, or shock

All definitions are subject to the following filter (exclusion rules) on records, and

these apply to all comparator sets and records counted to form denominators in rates:


46 FINDINGS

MDC = 14,15,19 or 20 (maternity, newborn, mental illness, substance abuse)

Paediatrics (i.e. age <18)

LOS < 1 day

LOS > 90 days

Error DRGs (i.e. DRG = 961Z, 962Z, 963Z)

Each OPSN category is supported by a list of ICD-10 diagnosis codes (some also

include ACHI surgery codes) for inclusion of cases, and a set of exclusion rules that

apply to both the codes selected for the presence of codes and those in scope of the

concept (denominator). For example Category 1, UTI, is defined as either diagnoses

N39.0 or T83.5 or as a secondary diagnosis (but not as a primary diagnosis) and the

case is not grouped to any of MDC 11 through to MDC 15 inclusive nor to MDC19 or

MDC 20 (mental illness and substance abuse), and nor is the rubric of the principal

diagnosis A40, or A41. Another simple OPSN is Category 9, surgical wound infection,

where either of the diagnosis codes T79.3 or T81.4 appears as a secondary diagnosis,

but neither as a principal diagnosis gives membership of the category. See Appendix 4

for detailed definition of category membership. The denominators used to form the

rates for either of these indicators are the count of cases restricted to the same set of

MDCs and with the principal diagnosis being other than one excluded by the OPSNs

definition. In practice, the both the Numerator and Denominator counts are restricted to

being either of medical or surgical DRGs and a medical and a surgical version of the

OPSN is produced. Failure to rescue (FTR) is death following an adverse event of

sepsis, pneumonia, GI bleeding, or shock (Silber et al., 1995; Silber et al., 1992),

Therefore the denominator is the count of these particular adverse events.

OPSN have been investigated in a number of studies (Beurhaus, 1999; McCloskey

& Diers, 2005; Needlemen et al., 2001, 2002). They were also investigated in the

recent NSW report by our team.

The analysis of OPSN is complicated in these data, and in general, because the

measures were initially intended to be applied at hospital level to quite similar hospitals,

or the same hospital over a number of time periods. However we bring the analysis to

bear on the units of our study, ward months. One of the most immediate consequences

of this shift is that the casemix seen on a ward will affect the rate of adverse outcomes

in an unbalanced way. Therefore we used the load cluster to match ward months.



Another technical complication arises in the analysis of OPSN because the rates of

these events in a typical ward over a 28 day period are numerically low, so that the

counts of events do not suit Analysis of Variance based on the Normal Distribution. The

Statistical literature contains a number of relevant examples of analyses of counts data

based on Generalised Linear Modelling – with Poisson distribution. In particular SPSS

Version 15.0 has implemented the approach so that it could be applied to our OPSN

data. We needed to replace the OPSN values by their nearest integer value because

the Poisson method expects count data.

OPSN analyses were performed using Generalised Linear Modelling – with Poisson

distribution. A range of different models were tested using the following factors:

Cluster

Cluster, NH:PH

Cluster, RN:PH, EN:PH

Cluster, RN:NH

Cluster, RN:NH, NH:PH

Cluster, RN:NH, RN:PH

Cluster, RN:NH, RN:PH, EN:PH

Where Cluster = group which the ward month falls into dependent upon the number of hours of care by each AR-DRG etc

NH = total nursing hours

PH = total patient hours

RN: total hours worked by Registered Nurses

EN: total hours worked by Enrolled Nurses

The best model for each individual OPSN was selected dependent upon the

significance of the Omnibus test, and Model Effects Type III Chi-Square results

(produced by SPSS Version 15 (SPSS Inc., 2006)). Once the best model was chosen,

the direction of the parameter estimates was noted. This indicated whether the

parameter was having a positive or negative effect on the incidence of OPSN.

Review of the SPSS output made it clear that the effect of rounding the OPSN may

affect findings, so a subsidiary testing process was put in place. This secondary

approach was guided by the standard method for testing the difference of proportions

and by the Gauss Markov Theorem. We only applied it to testing for RN Proportion

Effect.


48 FINDINGS

We start by taking the underlying rate for an OPSN in a ward month to be that of its

load cluster under the null hypothesis that only Cluster has an effect. This is estimated

by summing the OPSN across the cluster, summing the patient hours on ward across

the cluster and then dividing the former by the latter. We then predict the number of

OPSN for each ward month by multiplying its estimated underlying rate by its patient

hours on ward. In keeping with the standard tests of proportions we then divide each

ward month‟s OPSN number by its predicted value.

It is at this point we bring Gauss Markov and the underlying Poisson distribution to

bear and weighted each ward month ratio by the square root of its predicted value. If

RN proportion has no effect, each weighted ratio (GME) is an unbiased, unit variance

predictor of unity. Under the null hypotheses there will be no regression of GME on RN

proportion. Under the alternative there will be and negative slope will be associated

with better outcomes. The actual testing process included a modification, which was to

conduct the regression while controlling for cluster effects. The latter could be induced

by the differing RN proportion across Cluster, and hence needed to be controlled for.

An important methodological point here is that while this second approach does not

take full advantage of the Poisson error distribution, use is made of Gauss Markov.

Further, under the Poisson analysis our model for the parameter b is not identified: the

absolute size of the anti-logged cluster effects is completely confounded with the

absolute value of b. We also found it necessary to adopt some sample statistics for the

cluster effects when the largest attributed OPSN count was less than 0.5 for a whole

Cluster. We conducted the follow up test described above to strengthen our findings

and report these results along with the formal method results.

Poisson analysis allows the assessment of the statistical significance of a factor and

the direction of its effect, but not a readily interpretable measure of its size. This gap in

understanding needs to be filled using other methods. The follow-up testing approach

assists in this but is biased by the weighting applied to form GME. In addition the

clusters have different average proportions of RN hours say. However use of the

General Linear Model with fixed effects of Cluster, Intercept set to zero and weighted

least squares (using the expected OPSN number as weight variable) offers an

approximate approach consistent with the Gauss Markov based approach. This follows

from the fact that the unweighted ratios are unbiased estimators of 1 with variance

equal to the inverse of the variance of the observed value.



The regression slope for an experimental variable in this new type of analysis needs

interpretation which we now offer. If b is the regression parameter for RN hours as a

proportion of nursing hours (for example), then we see the effect of increasing the RN

hours proportion by 10% as changing the rate of the OPSN by b times 10%. So if b

were -3 then a 10% increase in the RN hours proportion would reduce the rate of the

OPSN to 70% of its current value.

In this report we extend our investigations to include length of hospital stay (LOS) as

an OPSN variable. LOS is responsive to the quality of nursing care (as well as other

factors) and therefore ward months associated with patients who have longer than

expected stays may also be those where the quality of nursing care is lower.

One of the obvious factors affecting LOS is the patient‟s illness and medical

intervention. These are not nursing dependent. Therefore LOS as an OPSN needs to

be controlled for the patient‟s AR-DRG V5.1. The standard approach for doing this is to

form casemix indices, where the LOS performance of a particular ward is compared

with that to be expected if it had the same average LOS for each AR-DRG as seen in

the whole dataset. Another method for dealing with these factors is by matching ward

months (through load clusters) before considering the effects of nurse staffing and skill

mix. To be particularly careful, we combined these approaches and a further linear

regression approach to adjust for prior exposure to risk.

ALOS as an OPSN Methodology

As discussed above, each ward month had been assigned to a load cluster and an

assess cluster. Taking each assess cluster at a time, casemix adjusted indices for both

the time spent in hospital before encountering a ward month and time spent in hospital

after contact with the ward month were calculated. The use of casemix adjustment

within assess cluster was to make sure LOS precursors and outcomes were being

compared like with like.

The next step was conducted load cluster by load cluster, thereby controlling for

workload on the ward at the time of the assess cluster patient contacts. The within load

cluster processing was the conduct of linear regression involving the logarithms of

indices calculated in the previous step. These indices were each ward month‟s index

for after contact hours of stay (After Index) and its index of before contact hours of stay


50 FINDINGS

(Before Index). The regression predicted the logarithm of After Index based on the

logarithm of Before Index.

After the regressions had been run for each load cluster, it was possible to calculate

the difference between each ward month‟s observed logarithm of After Index and its

predicted value. These residuals are referred to as performances. The anti-logarithm of

a performance provides a measure of the care hours after ward contact as a proportion

of the care after contact expected in a ward in the same assess cluster, in the same

load cluster, with the same casemix and the same patient pre-contact history.

The methodology for assessing the effects of nursing hours per patient hour, and

proportion of RN nursing care hours could thus be based on the correlations and

regressions of performance on the experimental variables. It was safe to assume that

the statistical dependence between the ward months‟ performance statistics could be

ignored as there were many raw data points and 398 ward months.

Cross-sectional Analysis

Cross-sectional data were entered into a Microsoft Access (Microsoft Corporation,

2003) database and extracted to SPSS versions 14 and 15 (SPSS Inc., 2005, 2006) for

analysis. Where data were missing at the patient or nurse level, they were imputed as

the ward mean calculated from the non-missing values on that ward. Where more than

10% of data were missing at the patient or nurse level, that variable was not used in

regression analyses. Complete staffing data were not available on two wards. These

wards were consequently excluded from analyses that used staffing data.

Subscale scores and alpha reliabilities for the instruments used were generated

using syntax provided from the Canadian study (O'Brien-Pallas et al., 2004).

Correlation analysis (Pearson‟s r or Kendall‟s tau b [τ], depending on the nature of the

data (Sheshkin, 2000) was used to explore relationships between variables at the

individual and ward level. Data collected at the patient and nurse level were

aggregated to ward level for some analyses, using mean values, rates or proportions.

Some patient level data were converted to percentage of patients per ward, for

example, adverse patient outcomes such as falls and medication errors.

In similar studies, multilevel modelling (MLM) has been used for analysis of

hierarchical or clustered data (Duffield et al., 2007; H. Goldstein, 2003). That approach



is considered appropriate where some variables are measured at the individual level

(patient or nurse) and others measured at the ward level. Data are therefore not

aggregated, but rather retained at the measurement level. However, the number of

wards with complete data in this study (14) does not provide sufficient statistical power

to undertake this type of analysis. Data were therefore used at the most appropriate

level of aggregation for each analysis.

Some data from the Environmental Complexity Scale (ECS) in the cross-sectional

component were further analysed at the „shift-period‟ level (see Table 6, page 25). In

this case, hours of nursing care data were apportioned to three conventional time

periods: morning (0700-1500); evening (1500-2300 hours) and night (2300-0700

hours), using the individual nurses‟ shift start and end times.

For all regression modelling explanatory variables were added in sequence to the

statistical models. The order of entry of variables into the statistical modelling process

was consistent with the theoretical framework described in Appendix 1. In order to

address potential multicollinearity, a univariate regression analysis on each individual

explanatory variable identified all significant predictors, and a factor analysis was

conducted. This identified 17 variable groupings. The significant univariate predictors

were then identified within the different groups. All predictor variables for each outcome

variable were put into a stepwise regression model, whereby the properties of each

model were compared to the previous one using the -2 Log Likelihood value. The

output for that model was then considered in terms of its position among the 17

components to ensure that any two predictor variables did not fall into the same group.

In order to compare the relative contributions of the independent variables to the

models, beta (β) weights were calculated. In the case of linear models, the adjusted R2

value was also calculated to provide an estimate of overall model fit (see also

Glossary, page 22).

Linear regression models for tasks delayed and not done were developed with data

at the ward-day level. This level of data provides outcome variables that are an

aggregate of responses for that ward for that day. Analysis at this level of data for

these outcomes is more meaningful as it accounts for the overall picture of the ward for

a given day, and the impact of workload and other variables for that period.

Analyses for the nurse outcome variables job satisfaction, satisfaction with nursing,

and intention to leave the current job, were conducted with these variables measured


52 FINDINGS

at the nurse level. Data collected at shift level (Environmental Complexity Scale) were

aggregated to nurse level to permit matching with nurse data. However, not all data

could be matched, leaving a reduced dataset of 149 cases. As these outcomes are

dichotomous, logistic regression models were developed.

In summary, longitudinal data were examined for changes in the relationship

between the amount and type of nursing resources and OPSNs across the two year

period, at a ward level. Cross-sectional data were analysed for relationships between

variables, and models were developed to determine the variables that significantly

impact on outcomes. Comparison with similar research in NSW was made where data

were available, either as overall figures or by hospital grouping. Where possible in both

components of the study, estimates of the strength of each model and of the relative

contribution of each variable were calculated.



4. Findings

Longitudinal Findings

Descriptive

Patterns in Skill Mix

TABLE 16 CANBERRA AND CALVARY TIME PERIODS

Canberra Calvary

Period Date Start Date End Date Start Date End

1 9/9/04 8/3/05 26/8/04 25/2/05

2 9/3/05 8/9/05 26/2/05 25/8/05

3 9/9/05 8/3/06 26/8/05 25/2/06

4 9/3/06 8/9/06 26/2/06 25/8/06

5 9/9/06 21/2/07 26/8/06 25/2/07

6 - - 25/2/07 7/3/07

Note that calculations were adjusted for the final periods which were shorter than 6 months. Also three wards in Canberra have a final period shorter than the other wards, ending on 18/10/06 instead of 21/2/07. This has been noted under relevant tables („Ward 1AH‟, „Ward 1AK‟ and „Ward 1AG‟).

Table 17 to Table 26 show the RN and EN hours for each ward from Canberra

Hospital included in the study. Table 27 to Table 30 show results for Calvary Hospital.

Notes on each ward are below each ward table. A summary of how wards compare

can be found in text following Table 26 for Canberra and Table 30 for Calvary. Wards

are described by type as indicated (see Longitudinal Analysis, page 41 and Table 31

page 62). Three time series (1, 3, 5) cross the Christmas/January period which may

impact on staffing and patient levels.


54 FINDINGS

Canberra Hospital

TABLE 17 NURSE SKILL MIX FOR CANBERRA WARD 1AB ‘MEDICAL TYPE’ FROM 09/09/2004 TO 21/2/2007

6 Month Period 1 2 3 4 5

Hours Worked

EN 13733 13963 13629 12784 12653

RN 12237 13718 14372 14382 12041

No. of Personal Shifts

EN 1787 1687 1655 1566 1553

RN 1593 1709 1821 1810 1489

Ratio Hours

EN 53% 50% 49% 47% 51%

RN 47% 50% 51% 53% 49%

Ratio Shifts

EN 53% 50% 48% 46% 51%

RN 47% 50% 52% 54% 49%

Total Hours

EN/RN 25969 27680 28001 27166 24694

Table 17 shows 51% EN and 49% RN hours worked over the given time period in

Ward 1AB. There is a small increase in RN and total hours worked between periods 2

and 3.

TABLE 18 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AL 'MEDICAL TYPE' FROM 09/09/2004 TO 21/02/2007


Hours Worked

EN 9076 10090 8507 8843 8029

RN 11243 13530 14696 15494 13619


EN 1191 1315 1085 1109 996

RN 1505 1736 1891 1970 1741

Ratio Hours

EN 45% 43% 37% 36% 37%

RN 55% 57% 63% 64% 63%

Ratio Shifts

EN 44% 43% 36% 36% 36%

RN 56% 57% 64% 64% 64%

Total Hours

EN/RN 20318 23620 23203 24337 21648

Note that AIN worked one shift (6 hours) in Period 5.

Table 18 shows a sustained, gradual increase in the proportion of RN hours worked

in the „Medical Type‟ Ward 1AL throughout the whole period (from 55% to 63%),

levelling off in the last three periods. The largest increase in the proportion of RN hours

worked occurred between period 2 and period 3 (7%).



TABLE 19 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AD 'MEDICAL TYPE' FROM 09/09/2004 TO 21/02/2007


Hours Worked

EN 9703 10785 10910 12333 11903

RN 36485 40957 46656 49310 42856


EN 1240 1378 1348 1584 1468

RN 5010 5406 6232 6688 5778

Ratio Hours

EN 21% 21% 19% 20% 22%

RN 79% 79% 81% 80% 78%

Ratio Shifts

EN 20% 20% 18% 19% 20%

RN 80% 80% 82% 81% 80%

Total Hours

EN/RN 46187 51742 57566 61642 54759

Table 19 shows that there is a far greater proportion of RN hours worked on ward

1AD than both wards 1AL and 1AB above. The proportion remains steady around 22%

to 78% for EN to RN hours across the whole study period.

TABLE 20 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AO 'MEDICAL TYPE' FROM 09/09/2004 TO 21/02/2007


Hours Worked

EN 15771 20859 19867 20521 19167

RN 27682 36381 36874 38159 34188


EN 1994 2638 2494 2586 2404

RN 3688 4726 4700 4924 4356

Ratio Hours

EN 36% 36% 35% 35% 36%

RN 64% 64% 65% 65% 64%

Ratio Shifts

EN 35% 36% 35% 34% 35%

RN 65% 64% 65% 66% 64%

Total Hours

EN/RN 43453 57240 56741 58679 53354

Note that AIN worked 14 shifts (91 hours) in Period 5 (9/9/06 – 21/2/07)

Table 20 shows a steady proportion of 36% EN and 64% RN ratio hours in the ward

1AO over the study period. This is more than 1AB and 1AL but less than 1AD.


56 FINDINGS

TABLE 21 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AH ‘SURGICAL TYPE’ FROM 09/09/2004 TO 21/02/2007


Hours Worked

EN 5395 6317 4532 8226 1777

RN 14960 19591 14854 20159 4975


EN 696 744 536 992 216

RN 2061 2496 1885 2563 654

Ratio Hours

EN 27% 24% 23% 29% 26%

RN 73% 76% 77% 71% 74%

Ratio Shifts

EN 25% 23% 22% 28% 25%

RN 75% 77% 78% 72% 75%

Total Hours

EN/RN 20355 25908 19386 28384 6752

Note that period 5 ends earlier than most other Canberra wards (21/2/07).

Table 21 above shows a consistent 26% to 74% ratio between EN and RN staff

hours worked in the „Surgical Type‟ ward 1AH throughout the study period.

TABLE 22 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AM 'MEDICAL TYPE' FROM 09/09/2004 TO 21/02/2007


Hours Worked

EN 16074 20279 18058 16972 14829

RN 23891 29913 35625 41318 39911


EN 2100 2582 2240 2144 1802

RN 3184 3814 4460 5192 4786

Ratio Hours

EN 40% 40% 34% 29% 27%

RN 60% 60% 66% 71% 72%

Ratio Shifts

EN 40% 40% 33% 29% 27%

RN 60% 60% 67% 71% 72%

Total Hours

EN/RN 39964 50192 53683 58290 54740

Note that AIN worked 24 shifts (330 hours) in Period 5.

Table 22 shows a consistent increase in the proportion of RN to EN hours worked in

the ward 1AM across the study period (from 60% RN 40% EN, to 72% RN and 27%

EN) with the total number of nursing hours also increasing by 37% since the start of the

period. Although there is an increase in hours, the number of hours worked by EN rises

at first (period 2) and then steadily declines to be lower than the start of the study

period (14829 compared to 16074).



TABLE 23 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AA 'MEDICAL TYPE' FROM 09/09/2004 TO 21/02/2007


Hours Worked

EN 8783 11065 11098 10468 7783

RN 27757 28961 30818 31695 31965


EN 1058 1328 1368 1240 948

RN 3460 3688 3860 3968 3986

Ratio Hours

EN 24% 28% 26% 25% 20%

RN 76% 72% 74% 75% 80%

Ratio Shifts

EN 23% 26% 26% 24% 19%

RN 77% 74% 74% 76% 81%

Total Hours

EN/RN 36539 40026 41916 42162 39748

Table 23 shows a fair bit of instability in skill mix for ward 1AA, but a distinctly higher

RN ratio (80% RN, 20% EN) in the final period.

TABLE 24 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AK 'MEDICAL-SURGICAL TYPE' FROM 09/09/2004 TO

18/10/2006


Hours Worked

EN 8429 9064 9535 8960 2439

RN 16626 20983 22449 20286 5123


EN 1025 1111 1096 1011 284

RN 2126 2657 2639 2303 590

Ratio Hours

EN 34% 30% 30% 31% 32%

RN 66% 70% 70% 69% 68%

Ratio Shifts

EN 33% 29% 29% 31% 32%

RN 67% 71% 71% 69% 68%

Total Hours

EN/RN 25055 30047 31984 29247 7562


Table 24 shows an increase in the proportion of RN hours worked between period 2

and 3 for ward 1AK, accompanied by an increase in the number of total nurse hours

between period 1 and 4 (increase of 4192 hours, or 16.7%). Note that period 5 is only

one month long. Overall the proportion of EN to RN hours remains steady at about

32% EN to 68% RN.


58 FINDINGS

TABLE 25 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AG 'MEDICAL-SURGICAL TYPE' FROM 09/09/2004 TO

18/10/2006


Hours Worked

EN 10042 9999 10860 9696 2259

RN 21935 25528 26491 23879 6309


EN 1329 1262 1358 1243 289

RN 2810 3201 3256 2940 778

Ratio Hours

EN 31% 28% 29% 29% 26%

RN 69% 72% 71% 71% 74%

Ratio Shifts

EN 32% 28% 29% 30% 27%

RN 68% 72% 71% 70% 73%

Total Hours

EN/RN 31977 35528 37351 33574 8568


Table 25 shows a steady ratio between EN and RN hours of 28.6% to 71.4% in this

„Medical-Surgical Type‟ Ward. Note that period 5 is only one month long.

TABLE 26 NURSE SKILL MIX FOR CANBERRA HOSPITAL WARD 1AF ‘MEDICAL-SURGICAL TYPE’ AND WARD 1AI ‘SURGICAL TYPE’ FROM 09/09/2004 TO 21/02/2007*


Hours Worked

EN 25270 29477 35137 21667 6531

RN 37206 50788 57379 40254 12696


EN 3238 3678 4396 2714 815

RN 4820 6510 7277 5131 1606

Ratio Hours

EN 40% 37% 38% 35% 34%

RN 60% 63% 62% 65% 66%

Ratio Shifts

EN 40% 36% 38% 35% 34%

RN 60% 64% 62% 65% 66%

Total Hours

EN/RN 62476 80264 92516 61921 19227

* Note that these data were combined from 2 wards in order to retain reasonable stability in the time series, so should be viewed with caution.

Table 26 shows a statistically significant increase in the proportion of RN hours

worked over the period of the study, but little change in the total number of nursing

hours. RN hours increase over this period, while EN hours decline.



Summary of Canberra Wards EN to RN Ratios

„Medical Type‟ ward 1AB maintained a steady 50% EN to 50% RN ratio over

the study period and ward 1AL demonstrated an increase from 55% to 63%

over the study period.

„Medical type‟ ward 1AD had the highest proportion of RN hours from the

wards studied (20% to 80% comparing EN to RN). Ward 1AA was the highest

of the remaining wards with a 25% to 75% ratio.

Most of the remaining wards held a ratio between 26% to 74% and 40% to

60% of EN to RN hours.

A number of wards showed increases in total nursing hours, of up to 39%.

Calvary Hospital

TABLE 27 NURSE SKILL MIX FOR CALVARY HOSPITAL WARD 2AJ 'SURGICAL TYPE' FROM 26/08/2004 TO 7/03/2007

6 Month Period 1 2 3 4 5 6

Hours Worked

EN 1962 2131 4679 4436 4298 474

RN 15964 20568 20127 17641 17115 4027


EN 262 288 633 598 580 63

RN 2151 2750 2703 2385 2283 545

Ratio Hours

EN 11% 9% 19% 20% 20% 11%

RN 89% 91% 81% 80% 80% 89%

Ratio Shifts

EN 11% 9% 19% 20% 20% 10%

RN 89% 91% 81% 80% 80% 90%

Total Hours

EN/RN 17927 22699 24805 22077 21413 4501

Note that period 6 ends earlier than most other time frames.

Table 27 shows a clear decrease in the ratio of RN hours between period 2 and

period 3 (from 90% to 80%) in the „Surgical Type‟ ward 2AJ. This ratio remains

consistent until the end of period 5 (20% to 80%). Total hours during this time increase

with additional EN and RN hours worked between period 2 and 3 and decrease slightly

in 4 and 5. The final total remains higher than the starting amount.


60 FINDINGS

TABLE 28 NURSE SKILL MIX FOR CALVARY HOSPITAL WARD 2AE 'SURGICAL TYPE' FROM 26/08/2004 TO 7/03/2007


Hours Worked

EN 1890 1913 3261 3669 3730 559

RN 7705 8326 13476 12575 13474 2143


EN 257 258 442 518 516 76

RN 1028 1127 1826 1694 1810 290

Ratio Hours

EN 20% 19% 19% 23% 22% 21%

RN 80% 81% 81% 77% 78% 79%

Ratio Shifts

EN 20% 19% 19% 23% 22% 21%

RN 80% 81% 81% 77% 78% 79%

Total Hours

EN/RN 9595 10240 16737 16244 17204 2703

Table 28 shows a steady ratio between EN and RN hours worked of 20% to 80%

over the study period for the „Surgical Type‟ ward 2AE. The greatest difference occurs

in period 4 with an increase in EN hours worked of 4% proportionally. Total numbers

increased by 100% over the same time, with RN and EN numbers increasing in the

same proportion.

TABLE 29 NURSE SKILL MIX FOR CALVARY HOSPITAL WARD 2AN 'MEDICAL TYPE' FROM 26/08/2004 TO 7/03/2007


Hours Worked

EN 2886 3662 3066 5918 7116 868

RN 3584 5988 4950 4747 6789 1132


EN 385 487 409 796 953 116

RN 480 801 662 637 908 151

Ratio Hours

EN 45% 38% 38% 55% 51% 43%

RN 55% 62% 62% 45% 49% 57%

Ratio Shifts

EN 45% 38% 38% 56% 51% 43%

RN 55% 62% 62% 44% 49% 57%

Total Hours

EN/RN 6470 9650 8016 10666 13905 2000

Table 29 shows a variable pattern for EN to RN work hour ratios for the „Medical

Type‟ ward 2AN within the study period. Total work hours increase by over 100%

during the study period but not at the same rate for EN and RN. The ratio moves from

close to 45% EN to 55% RN, to almost 40% EN to 62% RN then 55% EN 45% RN in



period 4, back toward 50% EN and RN in period 5 and 43% EN to 57% RN at the end

of the period.

TABLE 30 NURSE SKILL MIX FOR CALVARY HOSPITAL WARD 2AC 'MEDICAL TYPE' FROM 26/08/2004 TO 7/03/2007


Hours Worked

EN 7758 7306 7126 10374 8185 1614

RN 13796 15599 16307 20852 18092 2724


EN 1028 981 960 1403 1102 216

RN 1838 2083 2174 2802 2413 364

Ratio Hours

EN 36% 32% 30% 33% 31% 37%

RN 64% 68% 70% 67% 69% 63%

Ratio Shifts

EN 36% 32% 31% 33% 31% 37%

RN 64% 68% 69% 67% 69% 63%

Total Hours

EN/RN 21553 22905 23433 31226 26278 4339

Table 30 shows a slight increase in the proportion of RN hours worked over the time

of the study from 64% to 69% for ward 2AC. Most of the increase in total hours over

the period is due to an increase in RN hours.

Calvary Summary

Both the „Medical Type‟ wards 2AC and 2AN have the lowest ratio of RN to EN

hours and are the most variable over the study period, both showing a steady

increase in total hours over the period.

„Surgical Type‟ wards 2AE and 2AJ have the highest proportion of RN hours

(20% to 80% EN to RN).

All wards showed an increase in total hours over time, with the nursing skill

mix ratio remaining fairly steady.


62 FINDINGS

Patterns in Staffing Levels

Staffing level is expected to change with patient acuity, including features related to

age and length of stay. We would also expect the actual care hours delivered on the

ward during a ward month to be the major determinant of staffing numbers. Table 31

below gives basic utilisation data on the wards studied.

TABLE 31 WARD STATISTICS

Hospital Ward Ward Type Separations

Avg LOS

on ward

(days)

Avg LOS in

hospital (days)

Avg Age

(Yrs)

82 1AA Medical 2368 5.5 8.1 59

82 1AB Medical 1607 11.8 13.9 84

82 1AD Medical 2712 7.6 9.9 58

82 1AF &

1AI

Medical-Surgical & Surgical

7087 6.7 8.4 48

82 1AG Medical-Surgical 2779 6.9 9.2 54

82 1AH Surgical 2532 5.4 8.6 57

82 1AK Medical-Surgical 3078 5.9 8.4 60

82 1AL Medical 821 19.7 22.4 67

82 1AM Medical 2332 6.7 10 59

82 1AO Medical 3091 6 9.6 64

83 2AC Medical 3073 7.2 10.4 67

83 2AE Surgical 2719 4.4 5.7 59

83 2AJ Surgical 5328 3.4 4.7 54

83 2AN Medical 911 11 16.7 75

Figure 1 below illustrates the relationship between staffing numbers and patient load

in the wards from Canberra Hospital. The data are shown for consecutive roster

periods. Further, the methodology for investigating ALOS as a OPSN has been used to



assess amount of nursing required to achieve the average level of ALOS outcome

given the patient load and casemix. This level is plotted as “Typical Nurse Hrs”

indicating that it is the level that leads to the average risk for patients of this type. There

is no supposition that typical means appropriate, however the plot allows assessment

of variation from the empirical norm established by the software. The accuracy of this

assignment would be improved by the addition of further ward month data to the

method‟s learning (reference) set.

Figure 1 shows staffing levels were similar to typical hours over most of the period.

Staffing shows a general match to patient load and acuity adjusted patient load

(workload). This observation is based on the typical plot which is well matched to the

actual. There has been no significant change in the workload of nurses in these wards

in Canberra Hospital during the study period.

FIGURE 1 CANBERRA HOSPITAL STAFFING AND PATIENTS

Figure 2 shows the same measures for Calvary Public Hospital. The typical plot is

increasing over time with respect to the actual nursing provided. This means the

nurses‟ workload has increased over the period in this hospital.


64 FINDINGS

FIGURE 2 CALVARY PUBLIC HOSPITAL STAFFING AND PATIENTS

Acuity, measured as the ratio of typical nursing hours to patient hours, has remained

static in Calvary Public Hospital. In the Canberra Hospital there there has been a

statistically significant decline in acuity over the study period, although it would take 10

years of the current trend to halve the current level of acuity.

We now look at the study wards in turn. The first feature we look at is the complexity

of their caseloads as measured by the number of different AR-DRGs seen during the

period. Note that the figure for Ward 1AF & 1AI should be disregarded as it is an

artefact of our need to combine the two areas in order to retain reasonable stability in

the time series. The other data show that the wards see a wide range of casemix and

hence complexity in matching care to care requirements. It also illustrates the need for

casemix adjustment (of the type we have employed) in the comparative analysis of

wards and even ward months of the same ward.



TABLE 32 AR-DRGS CARED FOR OVER THE STUDY PERIOD

Hospital Ward No DRGs seen

(out of possible 613)

82 1AA 357

82 1AB 214

82 1AD 302

82 1AF/1AI 459

82 1AG 351

82 1AH 387

82 1AK 395

82 1AL 164

82 1AM 291

82 1AO 249

83 2AC 296

83 2AE 336

83 2AJ 404

83 2AN 188

We have devoted Appendix 6 to plots for each study ward. The plots show the same

measures as used in Figure 1 and Figure 2, and so allow demonstration of the changes

in acuity adjusted workload, patient load and nurse staffing level. We note that there

are significant differences in (acuity adjusted) staff to patient ratios between wards.

We accept that part of the explanation of ward level variation in acuity adjusted

staffing is the result of use the AR-DRG system to classify patients not in an acute

phase of their illness. Therefore the absolute level of agreement between Typical

Nurse Hrs and Actual Nurse Hrs will be affected by the presence of sub-acute and/or

non-acute patients on some wards, for example aged care units.

If there were a question of whether nursing availability drives the patient load or the

patient load drives the nursing allocation, then the charts in Appendix 6 would indicate

that both apply at different times. Sometimes the staffing falls away and then patient

numbers decline (nursing leads), sometimes changes happen together, and other

times the patient numbers lead. What we do see however is a strong relationship

between all three series plotted in each chart.

The combined wards 1AF and Ward 1AI show a decline in activity over the period

with a peak and then large step down in patient hours over ward months 10 and 11.

Acuity adjusted staffing estimate “Typical” fits the actual staffing quite well, and any


66 FINDINGS

large deviation is towards better actual staffing. There has been no real change in

acuity adjusted patient hours per nursing hour.

Ward 1AL has very stable series but so is the difference between typical and actual

nursing, with the actual nursing only about 60% of the former. This ward exemplifies

the issue of clinical acuity as an influence on AR-DRG assessed nursing requirement.

The „Medical Type‟ patient load includes some less acute patients than AR-DRG is

designed to classify. We may conclude however that there has been no real change in

acuity adjusted patient hours per nursing hour.

Ward 1AD has a high staff to patient ratio but one which is fully supported in acuity

adjusted terms. The trend is towards reduced workload for the nurses.

Ward 1AB is a little less stable than Ward 1AL, but is also a lower acuity type ward.

The patient load and staffing series track quite well, however there are quite dramatic

up-changes in acuity adjusted patient load (as reflected in the Typical series) which are

not matched by changes in staffing. This means the nurses on this ward face very

variable workloads, but no clear trend over time.

Ward 1AO shows the interdependence of the patient and nursing series very clearly.

However no clear trend over time (between Typical and Actual Nursing Hrs) emerges.

Ward 1AH shows a large variation in patient hours around ward months 18 and 22,

with concurrent changes (although of lesser magnitude) in staffing hours at the same

time. There is no trend in regard to workload.

Ward 1AM shows a growth of activity during the period. There is a trend for reduced

workload for its nurses.

Ward 1AA shows a disconnect between its acuity adjusted nursing measure and its

actual staffing level. No trend emerges.

Ward 1AK also displays a disparity between the actual staffing level and acuity

adjusted nursing hours, although the two measures are more closely matched from

ward month 11.



Ward 1AG shows a close match between actual staffing and the acuity adjusted

measure, except for two ward months (14 and 15).

Ward 2AJ shows considerable variation in patient hours that are not clearly reflected

in staffing numbers. The acuity adjusted measure and actual staffing track each other

quite well with no clear trend. The low staffing levels in this ward mean that variations,

such as that observed at ward month 17 must place a great deal of strain on the

nurses.

Ward 2AE would appear very difficult to manage and it is clear that reduction in

staffing to meet reduced patient numbers is more easily achieved than staffing up to

meet added patient load. This leads to some very distinct staffing shortfalls, for

example ward month 24. However no time trend emerges. From the nurses‟ point of

view, this would mean unpredictable patient assignments.

Ward 2AN shows seasonal effects (including closure1) which mask a major step up

in activity. The staffing level lags behind the increase in patient care requirements

leading to a massive increase in workload.

Ward 2AC also shows the effect of a slow-down around ward month 18 and

evidence of increased activity in the later months, but its staffing tracks the activity

change well. There is no trend in nursing workload.

Findings on Outcomes Potentially Sensitive to Nursing (OPSN)

Findings for OPSN other than ALOS

Our approach to this technically difficult area has been described in detail in the

Longitudinal Analysis section, page 41. We first set about using a counts data

approach found in the literature and our findings from these analyses are summarized

as Poisson or Approach 1 (A1). We then adopted a less rigorous approach that

accommodated the fact that our OPSN data were not actual count data and could be

non-integers, and in particular between 0 and 1. We report the findings from this

approach under the label GM Ratios or Approach 3 (A3). Finally we adopted a

1 Note that closure and slow-down were only strongly evident in the Calvary data.


68 FINDINGS

weighted least squares approach aimed at finding approximations for the effects of

interest measured in a way that could be interpreted in Nurse Staffing terms. These are

presented under the label Local Rate Reduction Effects or Approach 2 (A2). The

inclusion of the word local is to reinforce the understanding that the estimated effect

only applies to “practical” levels of change in the staffing variable. For example a slope

estimate that applies for RN hours as a proportion of total nursing hours would not

make sense for a change between no RNs to all RNs on a ward, but would make

sense for a 5% decrease in RN share.

Table 33 shows the statistical significance of factors in the various models tested for

each OPSN, for the three types of analyses used. These findings are presented to

show the degree of consistency in results between the analytic methods and hence the

amount of weight that can be attached the estimated values presented in Table 34. It

must be borne in mind that the different analyses are estimating different things even if

an experimental factor bears the same name. We can be most confident if a factor

comes up as significant in each analysis of a model for OPSN, but must expect

contradictions.

TABLE 33 TEST FOR SIGNIFICANT MODELS OF STAFFING ON OPSN

OPSN Model

11

Model

22

Model

33

Model

44

Model

55

Model

66

Model

77

Method

A18

CNS A1 A1 A2 A1 A2 A1 A2 A3 None None None Yes

Decubitus A1 None A2 A1 A2 A3 A2 A2 A1 Yes

DVT A1 None A2 None A2 A2 None Yes

FTR A1 None A1 A2 A1 A2 A3 None None None No

GI Bleed A1 None A1 A2 A1 A2 A3 None None None No

PM Derangement A1 None None None None None None Yes

Pneumonia A1 None A2 None A2 A2 None Yes

Pulm Failure A1 None A2 A2 A3 A2 A2 None No

Sepsis A1 None A2 None A1 A2 A2 None Yes

Shock A1 None A2 None A2 A2 None No

UTI A1 None A1 A2 A1 A2 A3 None None None Yes

Wound Infection A1 None A2 A2 A3 None None None No

1 Load Cluster

2 Nursing Hours per Patient Hour – Adjusted for Model 1

3 RN:PH, EN:PH – Adjusted for Model 1

4 RN Proportion – Adjusted for Model 1

5 RN:NH, NH:PH – Adjusted for Model 4

6 RN:NH, RN:PH – Adjusted for Model 4

7 RN:NH, RN:PH, EN:PH – Adjusted for Model 3

8 Estimates all clusters



The final column in Table 33 indicates whether the Poisson model used data from all

available clusters (Yes) or ignored sections of the data because no OPSN values

greater than 0.5 were recorded. The cells that are marked up in Table 33 correspond to

the best model selected on analysis of deviance criteria and appreciation of the

superiority of Approach 1. With the exception of DVT and PM Derangement, the

selected models include RN hours as a proportion of total nursing hours, i.e. the skill

mix variable. We note that Nursing Hours which was always a candidate in A1 and A3

is never selected (in the best choice of model) on its own; there is always a skill mix

component.

TABLE 34 PARAMETER ESTIMATES FOR OPSN

OPSN Model RN

Proprtion

RN

Hours

EN

Hours

Nursing

Hours

A1

Finding

CNS 4 -4.513 N/A N/A N/A Yes

Decubitus 4 -1.89 N/A N/A N/A Yes

DVT 3 N/A NS* 2.754 N/A No

FTR 4 -2.679 N/A N/A N/A Yes

GI Bleed 4 -3.707 N/A N/A N/A Yes

PM Derangement 1 N/A N/A N/A N/A Yes

Pneumonia 5 -1.114 N/A N/A 0.749 No

Pulm Failure 6 NS* 1.263 N/A N/A No

Sepsis 5 -1.467 N/A N/A 0.618 Yes

Shock 6 NS 1.951 N/A N/A No

UTI 4 -3.408 N/A N/A N/A Yes

Wound Infection 4 2.546 N/A N/A N/A No * Note that the entry NS in Table 34 means the parameter estimate was not significantly different from zero even though the inclusion of the effect in the model was supported. This occurs in DVT and Shock and suggests that these parameter estimates are not useful.

It is clear from Table 34 that parameter estimates based on models not supported

by Poisson (Approach 1) should be treated with scepticism. The alternative Approach 2

is biased by tendencies for people subject to different risks being nursed at different

intensities and skill mix. With this in mind, we have chosen to ignore the parameter

estimates for DVT, Pulmonary Failure, Shock and Wound Infection.


70 FINDINGS

TABLE 35 LOCAL RATE REDUCTION EFFECTS OF INCREASING RN SHARE OF NURSING TIME TO BE 10% MORE OF

NURSING TIME

OPSN Current ACT

Mean Rate for 84 hr Stay

Current ACT Standard

Deviation of Rate for 84 hr

stay

New ACT Mean Rate for

84 hr Stay

New Rate as Percent of

Current Rate

CNS 0.63% 0.44% 0.35% 55%

Decubitus 0.50% 0.18% 0.40% 81%

DVT 0.47% 0.18% N/A N/A

FTR 0.23% 0.13% 0.17% 73%

GI Bleed 0.17% 0.09% 0.11% 63%

PM Derangement

2.30% 0.85% N/A N/A

Pneumonia 0.47% 0.16% 0.42% 89%

Pulm Failure 0.18% 0.07% N/A N/A

Sepsis 0.48% 0.24% 0.41% 85%

Shock 0.06% 0.04% N/A N/A

UTI 1.05% 0.59% 0.70% 66%

Wound Infection

0.21% 0.14% N/A N/A

Table 35 demonstrates that increasing the skill level of the ward‟s nurses improves

patient outcomes across a broad range of measures. The choice of an 84 hour base in

this table is to make the rates relate to the average patient stay in hospital. Thus the

figures relate to episodes of care as well as hours of care. An appropriate response to

an unacceptably high level of an OPSN is to increase the skill mix rather than to

increase the nursing hours per patient day.

ALOS as an OPSN

The analysis was conducted using software that tested and partitioned the data into

a “successful outcome” group of ward months and a “not successful outcome” group.

The successful group (partition) of the ward months was made up of all those ward

months for which the (casemix controlled) total hospital bed-days of patients the



software recognised as less than the expected bed-days after adjusting for prior bed-

days in each AR-DRG. The not successful partition were the remaining ward months.

This analysis suggested an association between increased nursing hours and

decreased LOS, although it was not statistically significant. It is possible that a clear

result will be obtained when there is a larger data set of ward months with which to

work. In particular, the setting of meaningful performance thresholds in the current data

lead to problems of unreliable sample numbers.

Findings on Workforce and Skill Mix Stability

The tables at the start of this section illustrate the variances between wards over

time in staffing factors. Since time period considered in the tables (6 months) is quite

long, any time trend in the RN proportion would be systemic rather than random in

nature. The regressions of RN proportion against time found significant improvements

in skill mix for ward 1AM and ward 1AF but no other trend was significant.

The second group of tables and graphs in this section (and Appendix 6) show

changes in staff to patient ratios over time. They are based on ward months which are

quite a long period in line management terms. When we consider the very short term

fluctuations in nurse to patient ratios we find the effects of shifts generating highly

variable data. Figure 3 below is typical for Canberra Hospital. It takes a sample of

nursing event times (e.g. start of shift) for ward 1AO and shows the ratio between the

nurses and patients on the ward after the event is completed. The sample was chosen

based on the simultaneous staff change and patient movement, with the aim of seeing

what the staff to patient ratio looks like at these busy change points. We see that the

extreme variation is more towards the case of many nurses per patient rather than in

the other direction. This may be expected with overlapping shifts and handover time.

We also see that even over short time periods, the wards are staffed above a positive

minimum level. The data from Calvary Hospital was also highly variable but did not

exhibit the lower threshold found in the Canberra Hospital wards. Another feature of the

Calvary wards is that they do not show such a distinction in the nurse to patient ratio

between shifts.


72 FINDINGS

FIGURE 3 STAFFING AT WARD EVENT TIMES

L6A

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

Jun-

04

Aug

-04

Oct-0

4

Dec

-04

Feb-0

5

Apr

-05

Jun-

05

Aug

-05

Oct-0

5

Dec

-05

Feb-0

6

Apr

-06

Jun-

06

Aug

-06

Oct-0

6

Dec

-06

Feb-0

7

Apr

-07

Nu

rse:P

ati

en

t R

ati

o

L6A

There is a possibility that generally satisfactory nurse to patient ratios may be

calculated over a long time period masking periods of low staffing. So we look at the

ward months again, but this time using the output from the Nursing Model Software.

We set this up by looking at the suggested levels of RN staffing.

Firstly the Model does not determine adequate levels of nursing; it only assesses

what levels are most likely to achieve the threshold (average) standard of LOS

outcome and indicates the change (up or down) in staffing required to match these

levels. The average outcomes may be quite substandard, so if the Model (with this

threshold set) indicates that RN staff may be removed from the ward there is no reason

to do so. However if the model indicates that RN staff need to be added to the ward,

then there are genuine reasons to be concerned.

Figure 4 demonstrates the (cumulative) distribution of ward months and of patient

hours against the RN staffing adjustment (as RN hours per patient hour) that would

bring LOS outcome expectations in line with the average. A negative adjustment

means that the ward month would have been expected to perform above average while

a positive adjustment means that the ward month would tend to have a poor

performance.



FIGURE 4 PERFORMING TO AVERAGE LOS

RN Workforce Adjustment Spread

0

1000000

2000000

3000000

4000000

5000000

6000000

-0.3

0

-0.2

8

-0.2

6

-0.2

4

-0.2

2

-0.2

0

-0.1

8

-0.1

6

-0.1

4

-0.1

2

-0.1

0

-0.0

8

-0.0

6

-0.0

4

-0.0

2

0.0

0

0.0

2

0.0

4

0.0

6

0.0

8

0.1

0

0.1

2

0.1

4

0.1

6

0.1

8

0.2

0

RN worforce adjustment

Cu

mla

tiveT

ota

l P

ati

en

t H

ou

rs

0

50

100

150

200

250

300

350

400

Cu

mla

tive W

ard

Mo

nth

s

Total Patient Hours

Ward months

We see that in both the ward months and patient hours distributions the median

adjustment is near zero, however more patient hours are found in the region with better

staffing than average. The OPSN work shows that increasing the proportion of RNs by

10% gives good gains and we note that 8% of patient care hours and 11% of ward

months are delivered in ward months where the Modelled RN staffing adjustment

exceeds 10%. Looking at the better staffed ward months, we see that about the same

proportions of patient hours and ward months fall into the range with adjustments

below -0.16. So it is possible to improve outcomes within available resources.

The modelled adjustments were analysed at ward month level to find any time

trends. Wards 1AL and 1AM had decreasing adjustment (improved RN nursing) over

time. No other patterns emerged at ward level or in data with all wards combined.

Conclusion

The findings of this research do not include evidence of a hospital systems failure. In

particular there is no evidence of the feed-forward loop resulting from adverse

extended LOS that one would expect in a system in which adequate corrective nursing

action cannot be delivered. We can conclude that in ACT this adverse effect is never

allowed to run out of control for long. We see that in the relative stability (after casemix


74 FINDINGS

adjustment) of skill mix and staffing levels over the medium to longer term (though not

at ward level).

We note that there has been an increase in acuity adjusted workload in both

hospitals. This increase is more evident at Calvary Public Hospital where nursing

workloads are approaching unsustainable levels for an environment where the

provision of quality patient care is important.

A positive but weak association between adverse LOS and low nursing numbers

was shown by running the Nurse Staffing Model software. The effect does not seem

large. This may be the result of the masking brought about by the fact nursing levels

are never allowed to remain critically low for extended periods and when the levels are

low the nurses compensate by giving more of their time. Findings from the cross-

sectional study support a ”safety valve” model.

A relationship between better OPSN outcomes and higher skill mix was found. It is

strong enough to encourage the further skilling of the ACT nursing workforce. This is

particularly the case because the OPSN are only indicator values that are likely to

markedly understate the true rate of avoidable adverse events, and because our

analysis was limited by the data on adverse events. We would expect the true gains to

be much higher. This hypothesis should be confirmed by analysing data in which the

time and place of the adverse events were recorded. Then time periods shorter than

ward months could be used as the basis of staffing and skill mix evaluation, removing

the masking referred to above.



Cross-sectional Findings

Patient Characteristics

The tables below describe the patient sample and characteristics for both patients

and nurses in the cross-sectional study. Data were obtained on 601 different patients

and 1768 patient-days using the PRN-80 tool (Table 36). Table 37 outlines the patient

characteristics obtained from the patient record in the cross-sectional study.

TABLE 36 DATA COLLECTION RESPONSE – PATIENT DATA

Patient Data Total

Patient Data Form 601 (patients)

PRN-80 1768 (patient-days)

In this sample of patients (n=601) 88.9% had a caregiver at home. The majority

(96.9%) were under the care of a GP (LMO); 16.1% were referred for homecare; 16.8%

were waiting for a care facility (this may impact the average length of stay for the ward);

1.4% had been admitted for respite care. Only 24.7% of patient admissions were

planned with 13.4% admitted from a care facility. Pre-admission clinics had been

attended by 13.7% of patients. Finally, 14.3% of patients had been hospitalised for the

same condition in the last three months.

TABLE 37 PATIENT CHARACTERISTICS

Frequency Percent

Patient has a caregiver at home 1571 88.9

Patient has a GP (LMO) 1714 96.9

Patient attended pre-admission clinic 243 13.7

Referral for homecare 284 16.1

Planned admission 437 24.7

Patient hospitalised, same condition, past 3 months 252 14.3

Patient admitted for respite care 24 1.4

Patient waiting for a care facility 297 16.8

Patient admitted from a care facility 237 13.4

N=1768 (Patients)


76 FINDINGS

Nurse Characteristics

As indicated earlier 200 nurses responded to the nurse survey. The staff

classifications for which self-reported data were collected included registered nurses

level 1 and 2 (RNL1 & RNL2), enrolled nurses (ENs) endorsed enrolled nurses (EENs),

trainee enrolled nurses (TENs) and assistants in nursing (AINs). In addition nurses in

charge of the wards/units, clinical nurse consultants (CNCs), were asked to participate.

Definitions of Nurse Categories

RNL1 means a registered nurse who delivers nursing and/or midwifery care to

patients/clients in any practice setting and is provided with, or has access to, guidance

from more experienced nurses or midwives and, who provides support and direction to

enrolled nurses and nursing and midwifery students. RNL2 is a registered nurse who

has demonstrated competence in advanced nursing or midwifery practice, provides

guidance to RNL1, enrolled nurses, and nursing and midwifery students in the delivery

of nursing and/or midwifery care; and acts as Team Leader in the absence of the

Clinical Nurse Consultant. An EN is an enrolled nurse who completes one year of

training within the Vocational Education and Training (VET) sector. The VET sector

consists principally of government-funded Technical and Further Education (TAFE)

institutes (McKenna et al. 2000). An EEN is an enrolled nurse who has completed a 6-

month post-enrolment medication administration certificate. An AIN assists in the

performance of nursing duties and other duties incidental and related to the provision of

nursing care services. The AIN is under the direct or indirect supervision of a RN. A

Clinical Nurse Consultant (CNC) is responsible for the quality of clinical nursing care

provided in a ward or clinical unit or to a specified group of patients (ACT Health / ANF,

2007).

When the profile of respondents (n = 200) in the cross-sectional sample is

compared to AIHW data (Table 38) the sample had 12% fewer registered nurses and

10% more enrolled nurses; 13% fewer part time nurses and 7% more full time nurses;

and 4% more male nurses than ACT data (AIHW, 2006).



TABLE 38 NURSE SURVEY RESPONSE COMPARISON WITH AIHW DATA – GRADE, GENDER, EMPLOYMENT STATUS

ACT Average* Cross-sectional Data

Registered nurses (RN) 82.9% 70.5%

Enrolled nurses (EN) 17.1% 27.0%

Female 93.8% 89.5%

Male 6.2% 10.5%

Full time 51.9% 58.5%

Part time 48.1% 35.0% * (AIHW, 2006)

The characteristics of the respondents to the Nurse Survey in regard to employment

grade, status, permanency of position and their highest qualification are described in

the following tables. Table 39 indicates that the cross-sectional sample consisted of

nearly 71% registered nurses (levels 1 and 2) and 27% enrolled nurses (including

EENs) with the remaining 2.5% distributed over CNCs (1.5%) and AINs (1%). More

than half (58.5%) were employed full-time (n = 117), while approximately one-third

(35%) were part-time. Casual and agency staff accounted for the remaining 6.5% of

respondents, either RNs or ENs. Table 40 shows that the majority (89%) of the 200

respondents were permanent employees (i.e. not employed on temporary contracts).

TABLE 39 NURSE GRADE & EMPLOYMENT STATUS (SELF-REPORTED)

AIN EN EEN RNL1 RNL2 CNC Total N Total %

Full time 1 29 3 78 3 3 117 58.5

Part time 1 17 2 47 3 0 70 35.0

Casual 0 3 0 9 0 0 12 6.0

Agency 0 0 0 1 0 0 1 0.5

Total N 2 49 5 135 6 3 200 100%

Total % 1.0 24.5 2.5 67.5 3.0 1.5

TABLE 40 PERMANENT AND TEMPORARY NURSING STAFF (SELF-REPORTED)

Frequency Percent

Permanent 178 89.0

Temporary 22 11.0

Total 200 100%

Asked for their highest nursing education qualification (Table 41), most RN

respondents (49.5%) reported holding a degree or diploma, 23.5% an EN certificate,

13% an RN hospital certificate. Very few reported having a post-registration


78 FINDINGS

qualification ranging from post basic certificates (2.5%) to 6.5% with postgraduate

qualifications (i.e. graduate certificate, graduate diploma or a master level degree). In

addition, more than half the respondents (n = 119, 59.5%) report that they hold a non-

nursing qualification (see Table 42).

TABLE 41 HIGHEST NURSING QUALIFICATION (SELF-REPORTED)

Frequency Percent

EN Certificate 47 23.5

EEN Certificate 7 3.5

RN Hospital Certificate 26 13.0

Post Basic Certificate 5 2.5

RN Diploma 10 5.0

RN Degree 89 44.5

Graduate Certificate 7 3.5

Graduate Diploma 3 1.5

Master of Nursing 3 1.5

No Qualification 3 1.5

Total 200 100%

TABLE 42 HIGHEST NON-NURSING QUALIFICATION (SELF-REPORTED)

Frequency Percent

TAFE Certificate 43 21.5

Diploma 20 10.0

Degree 25 12.5

Graduate Certificate 10 5.0

Graduate Diploma 3 1.5

Masters Degree 0 0.0

PhD 1 0.5

Other 17 8.5

No Qualification 81 40.5

Total 200 100

In terms of age the youngest respondent was 20 while the oldest was 67 years. The

mean age of 39.2 years is less than the reported average age of employed nurses for

the ACT (45.3 years) (AIHW, 2006). Forty-five percent (45%) of the 200 nurses have

children living at home, with only 7% having other dependents living with them (Table

44). On average respondents reported having worked as a nurse for almost 12 years,

had worked at the present hospital for almost five years, and had worked on the current

ward for almost three years (Table 43).



TABLE 43 NURSES’ AGE & EXPERIENCE

Mean SD Min Max

Age 39.2 11.26 20 67.0

Years worked as a nurse 11.9 10.80 0 45.0

Years worked as a nurse at present hospital 4.7 6.37 0 33.0

Years worked as a nurse on current unit 2.7 4.15 0 19.3

Years worked as a casual nurse 0.5 1.78 0 16.5

N = 200 (Nurses)

TABLE 44 CHILDREN & OTHER DEPENDENTS

Frequency Percent

Children living with you 90 45

Other dependents living with you 14 7

N = 200 (Nurses)

On average respondents worked 32.4 hours per week (range 0 – 50) at the current

hospital, 6 hours on another ward in the same hospital, and 2.1 hours in another job

(self-reported, see Table 45).

TABLE 45 NURSES’ HOURS WORKED – AVERAGE PER WEEK OVER THE PAST YEAR – SELF-REPORT

Mean SD Min Max

Hours worked per week in this hospital 32.4 11.9 0 50

Hours worked per week in other jobs 2.1 7.2 0 45

Hours worked per week on other wards in this hospital 6.1 12.9 0 42

N=200 (Nurses)

Nearly 11 percent (10.8%) of respondents reported having missed a morning or

afternoon tea break in the current shift and almost 7% reported that they had missed

lunch (Table 46).

TABLE 46 NURSES WHO MISSED BREAKS DURING THE CURRENT SHIFT

Frequency Percent

Morning or afternoon tea 66 10.8

Lunch 42 6.9

N = 612 (Shifts)


80 FINDINGS

The median number of shifts missed per respondent over the over the past year was

5, and the median number of occasions missed was 3. Approximately 13% of nurses

reported missing no work during this period. The most common reason for missing

work was physical illness (Table 47).

TABLE 47 REASON FOR MISSING WORK

Frequency Percent

Physical illness 132 66.0

Other 41 20.5

Injury (work related) 10 5.0

Mental health day 9 4.5

FACS leave 6 3.0

Unable to get requested day off 2 1.0

N=200 (Nurses)

Ward Characteristics

In terms of allied health (Table 48) two-thirds of the wards had a dedicated social

worker, physiotherapist (60%), occupational therapist (33%), dietician (20%) or speech

therapist (20%). 80% of wards had access to a dietician and a speech therapist, two-

thirds had access to an occupational therapist and 40% had access to a

physiotherapist. As for ancillary staff, 60% had a dedicated clerical assistant and 40% a

dedicated ward assistant. 60% had access to a ward assistant and 40% access to a

clerical assistant. There was a mean of 6.5 hours of housekeeping support per ward

(range 4 – 8, data not shown).

TABLE 48 WARD CHARACTERISTICS: ALLIED HEALTH & ANCILLARY SUPPORT

Access Dedicated

N % N %

Physiotherapist 6 40.0% 9 60.0%

Occupational Therapist 10 66.7% 5 33.3%

Social Worker 5 33.3% 10 66.7%

Dietician 12 80.0% 3 20.0%

Speech Therapist 12 80.0% 3 20.0%

Ward Assistant 9 60.0% 6 40.0%

Clerical Assistant 6 40.0% 9 60.0%

N=15(Wards) (one ward did not complete a ward profile, see also Sample definition, page 36)



Table 49 shows the level of nursing support at ward level. While all wards (100%)

had technical or medical support, 93.3% also had support from specialist nurses, 60%

had access to a nurse educator and 53.3% had critical pathways or clinical guidelines.

TABLE 49 WARD CHARACTERISTICS: NURSING SUPPORT

N Percent

Specialist Nursing (diabetes, wound, stomal, chemo, podiatry) 14 93.3%

Technical or Medical (MET, I/V, Path, ECG, Pain) 15 100.0%

Nurse Educator (access) 9 60.0%

Critical Pathways or Clinical Guidelines 8 53.3%

N=15 (Wards) (one ward did not complete a ward profile, see also Sample definition, page 36)

There was an average of 24.5 (range 16-34) beds per ward. The average number of

patients seen each day per ward during the sample period was 22.9 (range 15.8-34)

(Table 50).

TABLE 50 WARD CHARACTERISTICS

Mean SD Min Max

Beds 24.5 5.18 16.0 34.0

Patients on ward (mean per day) 22.9 5.05 15.8 34.0

N=15 (Wards) (one ward did not complete a ward profile, see also Sample definition, page 36)

Skill Mix Characteristics

Nursing hours worked

A skill mix profile was derived from nursing hours worked and was calculated from

the complete record of ward nursing roster data. These data were aggregated into

“proportion of hours worked” by employment status (full-time, part-time, casual and

agency); by grade categories (RN level 1, RN level 2, EN [including EENs as it was not

possible to differentiate on all ward rosters], and AIN). These profiles include hours

worked on the ward only and excluded CNCs and CNEs. Table 51 below outlines these

staffing data.


82 FINDINGS

TABLE 51 CROSS-SECTIONAL STAFFING DATA

Staffing data Ward-shifts* Ward-days§

Daily ward staffing profile 1292 67 * Data for a 24 hour period from a single hospital ward § Data for a shift-period from a single hospital ward

See also Table 6, page 25

Figure 5 summarises the percentage of RN, EN, and other nurse hours worked per

ward, and hence provides an overview of the skill mix across the sample. As

mentioned, staffing data were not complete for two wards. Analyses were restricted to

reporting on a per ward and ward-day basis due to the small number of wards (i.e. 14

wards; see also sample details, page 36).

The skill mix ranged from 44% RN and 55% EN staff, to 82% RN and 18% EN staff.

Most wards had between 60% and 80% RN staff (Figure 5). There were three wards

with a mix of fewer than 60% RN staff and three wards with greater than 80% RN

staffing.

FIGURE 5 PERCENTAGE OF NURSE HOURS WORKED PER WARD, BY GRADE, ORDERED BY RN%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ward

RN EN Other

Table 52 indicates that the cross-sectional staffing profile was within 10% of

longitudinal data on all but ward 1AF, suggesting that the sample was generally

representative of staffing for each ward (see also Table 58, page 89).



TABLE 52 COMPARISON OF LONGITUDINAL & CROSS-SECTIONAL STAFFING PROFILE,, BY WARD

RN% EN%

Ward Code Cross-sectional Longitudinal Cross-sectional Longitudinal

1AA 80.2 75.4 15.2 24.6

1AB 44.1 49.9 55.9 50.1

1AD 81.1 79.5 18.9 20.5

1AF 77.7§ 62.3 22.3 37.7

1AG 72.1 70.7 25.0 29.4

1AH 70.2 74.1 28.8 25.9

1AI 59.3 62.3 40.7 37.7

1AK 66.6 68.7 32.6 31.3

1AM 72.2 66.3 27.8 33.7

1AO 62.7 64.3 37.3 35.7

2AC 70.5 67.5 28.7 32.5

2AE 76.2 79.4 23.8 20.6

2AJ 82.0 84.1 18.1 16.0

2AN 44.8 53.8 45.0 46.2

* Cross-sectional data recorded „other‟ nursing staff as follows: 1AA:4.6%; 1AG:2.9%; 1AH:1.1%; 1AK:4.0%; 2AC: 0.8%; 2AN:10.2%. These data were not collected in the longitudinal component. § Staffing data for Ward 1AF was atypical when compared to longitudinal data.

On a ward-day basis (Figure 6) there were 39 (58%) ward-days that had between

60-80% RN hours worked and one that had 100% RN hours. Fifteen ward-days had

fewer than 60% of hours worked by RNs and 13 had 80% or more. Twenty ward-days

had greater than 35% EN hours worked. Only twelve ward-days over six different

wards employed nurses which were other than RN and EN categories and the

percentage of these “other nurse” hours worked ranged from 0 – 7.46%, with two

outliers at 22.4 and 24.5%.


84 FINDINGS

FIGURE 6 PERCENTAGE OF NURSE HOURS WORKED PER WARD-DAY, BY GRADE, ORDERED BY RN%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ward-Day

RN EN Other

As indicated earlier part-time nurses were under-represented in this sample when

compared to Territory figures (48.1% part-time). This may be an artefact of the data

collection process. Most data collection was undertaken in a six to eight hour period

between 0700hrs and 1800hrs which might have provided less opportunity to engage

those nurses working fewer days or outside these hours. However, there is no effect on

staffing data as they were obtained from the ward roster. In addition to the data from

the ward roster, the Nurse Survey asked respondents to report on for example,

employment status and hours worked. Figure 7 and Figure 8 present ward level and

ward-day level information on employment status.

The percentage of full-time, part-time, casual and agency hours worked per ward for

the 14 wards surveyed are shown below (Figure 7). The lowest percentage of full-time

hours worked on one ward was 39.4% and the highest percentage was 75.38%. There

were two wards which had less than 40% full-time staff. Part-time staff ranged from

20.3 – 52.6% and casual staff ranged from 1 – 3% in five wards with a maximum of

26.1%. Four wards in the sample employed no agency staff at all, while the remaining

10 wards employed between 1 – 8% agency staff. However, there is considerable

variation in these figures when reported on a ward-day basis.



FIGURE 7 PERCENTAGE OF NURSE HOURS WORKED PER WARD, BY EMPLOYMENT STATUS, ORDERED BY FT%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ward

Full time Part time Casual Agency

Table 53 and Table 54 show the proportion of hours worked per ward and ward-day

(see Glossary, page 22) respectively by employment status. The mean for each

category of staff per ward and per ward-day is comparable although the range and

consequently the SD are larger in the ward-day data. For example per ward, full-time

staff comprised 53.8% (SD = 11.22%), part-time staff comprised one-third (SD =

11.75%), casual staff 9.9% (SD = 8.39%) and agency staff comprised 2.9% (SD =

2.81%) of the ward staffing (Table 53). Table 54 shows that the range in the proportion

of full-time (10.5 – 93.3%) and part-time (0 – 79%) hours was considerably greater at

the ward-day level.

TABLE 53 PROPORTION OF HOURS WORKED PER WARD BY EMPLOYMENT STATUS

Mean SD Min Max

Full-time 53.8 11.22 39.4 75.4

Part-time 33.3 11.75 20.3 52.6

Casual 9.9 8.39 1.1 26.1

Agency 2.9 2.81 0 8.1

N=14 (Wards) (two wards did not provide staffing data, see also Sample definition, page 36)

Table 54 shows that 54% of the hours worked per ward were by full-time nurses and

a third (33.3%) of the hours were worked by part-time nursing staff. The remaining 12%

were casual and agency hours. These „employment status‟ categorisations were not


86 FINDINGS

available in the longitudinal data, and they therefore provided a more detailed

understanding than would be available using administrative data alone.

TABLE 54 PROPORTION OF HOURS WORKED PER WARD-DAY BY EMPLOYMENT STATUS

Mean SD Min Max

Full-time 54.6 14.62 10.5 93.3

Part-time 33.1 15.81 0 79.0

Casual 9.6 9.95 0 38.0

Agency 2.8 4.45 0 22.9

N=67 (Ward-Days)

Percentages of full-time, part-time, casual and agency hours worked per ward-day

are shown below (Figure 8). The lowest percentage of full-time hours worked on one

ward-day was 10.5% and the highest percentage was 93.3%. There were ten ward-

days which had less than 40% full-time staff and two ward-days which had more than

80% full-time staff. One ward-day had 71.7% full-time nurses, no part-time or agency

staff at all and instead filled this gap with 28.3% casual staff. Apart from this particular

ward-day, the remaining ward-days had part-time staff ranging from 6.7 – 79% of their

staff. Twenty-three ward-days reported no casual staff, while the remaining 44 (66%)

ward-days had between 3.3 – 38% casual staff. On 43 ward-days in the sample no

agency staff were employed at all, while for the remaining 24 ward-days between 3.3 –

22.9% agency staff were employed. This analysis indicates that there is considerable

variation in staffing across many wards each day.



FIGURE 8 PERCENTAGE OF NURSE HOURS WORKED PER WARD-DAY, BY EMPLOYMENT STATUS, ORDERED BY FT%

Table 55 below shows great variation in the proportion of hours worked per ward-

day by grade. RN L1 staff worked on average 51.6% of the hours with a large range

from 21 – 89.9%; RN L2 staff worked on average 16.8% with a range of between 0 –

51%; and ENs worked 29.9% of hours, also with a large range of 0 – 66%.

TABLE 55 PROPORTION OF HOURS WORKED PER WARD-DAY, BY GRADE

Mean SD Min Max

RN L1* 51.6 12.88 21.0 89.9

RN L2* 16.8 11.16 0.0 51.1

EN 30.1 13.67 0.0 66.0

Other 1.5 4.38 0.0 24.5

N=67 (Ward-Days) * See Glossary, page 22

When the same data are presented per ward (Table 56) the means are comparable

but as expected, the standard deviations and the range for each grade are smaller. The

maximum percentage hours worked per ward for ENs is 55.9% and 74.5% for RNL1s.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ward - Day Full time Part time Casual Agency


88 FINDINGS

TABLE 56 PROPORTION OF HOURS WORKED PER WARD, BY GRADE

Mean SD Min Max

RN L1 51.7 10.66 35.3 74.5

RN L2 16.9 10.26 0.0 33.5

EN 30.0 11.39 15.2 55.9

Other 1.5 2.87 0.0 10.2

N=14 (Wards) (two wards did not provide staffing data, see also Sample definition, page 36)

Staffing data were also examined for skill mix across three equal „shift-periods‟ (see

Table 6, page 25), referred to as morning (0700-1500), evening (1500-2300) and night

(2300-0700) „shift-periods‟. Raw staffing data were apportioned to these periods to

provide an indication of the relative availability of these staffing categories during the

day, evening or night (see also Table 6, page 25). Similar proportions of all categories

were found on morning and evening shift-periods, while the night period showed an

increased presence of ENs, significantly fewer RNL1 hours, and slightly fewer RNL2

hours (Table 57).

TABLE 57 PROPORTION OF HOURS WORKED PER SHIFT-PERIOD BY GRADE

% Hours Evening Morning Night

RNL1 54.0% 54.0% 41.8%

RNL2 17.6% 16.6% 15.9%

EN 27.5% 27.4% 41.1%

Other 1.0% 1.9% 1.2%

N=1292 (Shift-periods)

A comparison of cross-sectional and longitudinal staffing data indicated that there

were similar mean proportions of staffing hours per ward (eg RN = 68.5% and 68.4%

respectively), although with slightly greater variation in the cross-sectional data (eg RN

SD = 12.20 and 9.72 respectively, see Table 58).



EQUATION 1 PATIENTS PER BED

NumberofPatientsOnWardPerDayPatientsPerBed=

NumberofBedsPerDay

EQUATION 2 NURSING HOURS PER PATIENT DAY

NursingHoursWorkedOnWardNHPPD=

NumberofPatients

TABLE 58 COMPARISON OF LONGITUDINAL & CROSS-SECTIONAL STAFFING PROFILE, BY GRADE

N* Mean SD Min Max

Cross-sectional

RN 14 68.5 12.20 44.1 82.0

EN 14 30.0 11.39 15.2 55.9

Other 14 1.7 2.93 0.0 10.2

Longitudinal

RN 14 68.4 9.72 49.9 84.1

EN 14 31.6 9.72 16.0 50.1

Other 14 0.0 0.00 0.0 0.0

* Two wards did not provide cross-sectional staffing data, see also Sample definition, page 36

Nursing Workload

The movement of patients through the ward is referred to as patient “churn”. Each

new admission, transfer, or discharge, requires documentation, orientation, clinical

assessment and management review, and other tasks associated with the patient. In

order to provide an indication of the amount of churn per ward, “Patients per bed” was

calculated per ward by dividing the number of patients per day by the number of beds

(Equation 1). This calculation does not include bed movements within the ward. While

the mean was one patient per

bed per day there was

considerable variation between

wards, with the range from less

than one patient (0.7) per bed per day and the maximum 1.2 (Table 59). When

examined on a day by day basis, rather than averaged across the ward sample period,

the maximum rose to 1.4, with a larger range (0.5 – 1.4).

TABLE 59 PATIENTS PER BED

N Mean SD Min Max

Patients per bed by ward 14* 1.0 0.14 0.7 1.2

Patients per bed by ward-day 67 1.0 0.15 0.5 1.4

* Two wards did not provide cross-sectional staffing data, see also Sample definition, page 36

Nursing hours per patient day & hours of care required per patient day

Nursing hours per patient day

(NHPPD, Equation 2) provided varied

considerably on a per day basis (mean


90 FINDINGS

6.5, range 3.7 – 11.6) and were reasonably normally distributed though the data

indicate significant variation between and within wards (Table 61, page 91).

The PRN-80 determines the minutes of care (later transferred into hours) required

by patients for 24 hours using data from the medical record (see Table 7, page 25 &

Table 13, page 39). An average of the hours of care required per patient per day

(determined by the PRN-80) was calculated. Across the sample of 67 ward-days there

was considerable variability (Figure 9). The average requirement per ward-day (using

PRN-80) was 7.02 hours. The difference between the minimum and maximum

requirements per ward-day (range) was considerable; from just over 4 hours to nearly

11 hours (10.7 hours). This degree of variability in care needs makes it difficult to

predict the staffing required, and the discrepancy between hours needed and available

hours may impact on workload, quality of care and the work environment. At the

patient-day level there was also great variability over 24 hours (mean 7 hours 5 mins;

range 1 hour 4 mins – 21 hours 11 mins) indicating great variation between individual

patient care requirements per day (Table 60).

FIGURE 9 HOURS OF CARE REQUIRED

Mean hours of nursing care required for 24 hours (ward-day)

10.008.006.004.00

Fre

qu

en

cy

12

10

8

6

4

2

0

Histogram

Mean =7.02Std. Dev. =1.486N =67

Mean hours of nursing care required

for 24 hours (ward)

10.009.008.007.006.005.004.00

Fre

qu

en

cy

5

4

3

2

1

0

Histogram

Mean =7.09Std. Dev. =1.389N =14



TABLE 60 HOURS OF CARE REQUIRED

N Mean SD Min Max

Patient-Day 1768 7.1 3.14 1.1 21.2

Ward-Day 67 7.0 1.49 4.3 10.7

Ward 14* 7.1 1.39 4.5 9.0

* Two wards did not provide staffing data, see also Sample definition, page 36

When the hours of care required (measured using the PRN-80) are compared to

those provided (Table 61), on average, approximately one half hour per day of

additional care is required to meet each patient‟s needs (see Table 13 for explanation

of the use of this tool). There was considerable variation per ward day over the entire

sample period, as displayed in Figure 10.

TABLE 61 NURSING HOURS PER PATIENT DAY; NURSING CARE REQUIRED; NURSING DEMAND/SUPPLY

Mean SD Min Max Percentiles

25 50 75

Hours of nursing care required per patient day

7.0 1.49 4.3 10.7 5.8 7.0 8.0

Nursing hours per patient day 6.5 1.64 3.7 11.6 5.3 6.4 7.4

Nursing demand/supply 112.8 28.22 56.9 171.2 91.4 114.1 127.3

N=67 (Ward-Days)

FIGURE 10 NURSING HOURS PER PATIENT DAY & NURSING CARE REQUIRED (ENTIRE SAMPLE PERIOD – 67 DAYS)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00


Nursing hours per patient day

Ward Day


92 FINDINGS

EQUATION 3 NURSING DEMAND/SUPPLY FIGURE

HoursofCareRequiredperDay%NursingDemand/Supply= ×100

NursingHoursPerPatientDay

An additional factor, referred to here as the nursing demand/supply figure

(Equation 3), is calculated by dividing the required hours of care (derived from the

PRN-80) by the hours of care provided (O'Brien-Pallas et al., 2004). Nursing

demand/supply figures over 100 indicate that more care is required by patients than is

provided. Table

61 indicates that

only a quarter of

the ward days

sampled are in balance for nursing resources: That is, the supply of nursing hours

equals or is less than that of the hours of nursing care required by patients on only 25%

of days in the sample (i.e. nursing demand/supply = 91.4 at the 25th percentile). For the

remaining 75% of days there is an imbalance – nursing hours required exceed those

provided.

These data were further analysed by hospital peer group, using ward mean data

(Table 62), and were compared to similar data from NSW. This showed that, in the

ACT, both the hours of nursing care required per patient day and nursing hours per

patient day provided were higher in the A group hospital. However, there was a larger

imbalance between care required and provided in the B1 hospital (A=110.8, B1=119.0).

When compared with NSW figures, more hours of nursing care per patient day were

required in A group, and fewer than NSW in B1. Nursing hours per patient day were

substantially higher than NSW figures in A group (ACT=7.1, NSW=5.3), and slightly

lower in B1 group (ACT=5.0, NSW=5.2). There was a lower imbalance between care

required and provided in both groups in the ACT.

TABLE 62 COMPARISON OF ACT & NSW NHPPD & CARE REQUIRED FIGURES, MEAN PER WARD, BY HOSPITAL PEER

GROUP

ACT* NSW§

Group Mean Min Max Mean Min Max


A 7.6 5.5 9.0 6.0 4.0 10.0

B1 5.8 4.5 7.3 6.9 5.0 8.1

Nursing hours per patient day provided

A 7.1 5.5 9.8 5.3 3.8 7.7

B1 5.0 4.6 5.4 5.2 4.2 5.9

Nursing demand/supply A 110.8 73.5 136.1 115.9 63.5 169.6

B1 119.0 95.8 142.9 133.5 117.8 147.6 * N=14 wards (10 in A group hospital, 4 in B1 group hospital)

(two wards did not provide staffing data, see also Sample definition, page 36) § N=65 wards (49 in A group hospitals, 16 in B1 group hospitals)



Work Environment

A range of factors in the work environment were measured. Results from the

subscales of the Nursing Work Index – Revised (NWI-R) and the Environmental

Complexity Scale (ECS) were compared with prior research, and also included in

regression models on patient and nurse outcomes.

Nursing Work Index – Revised

Compared to the findings in the NSW study (Duffield et al., 2007) nurse autonomy,

nurse-doctor relationships, resource adequacy and nurse control over practice were

higher in the ACT data than the NSW study, while slightly lower for nurse leadership

(Table 63) (see Glossary, page 22 for definitions).

TABLE 63 NURSING WORK INDEX - REVISED

NSW 2004/5 ACT Health

Mean SD Mean SD Min Max

Nurse autonomy 16.7 3.18 17.3 3.23 7 24

Nurse control over practice 17.5 3.91 18.3 4.05 8 28

Nurse-doctor relations 8.4 1.75 8.8 1.80 3 12

Nurse leadership 32.8 5.88 32.6 6.09 15 46

Resource adequacy 9.0 2.73 9.6 2.77 4 16

N= 200 (Nurse Respondents)

Associations were found between some factors of the NWI-R and the nursing

demand/supply level (Table 64). A high nursing demand/supply figure (indicating wider

discrepancy between hours of care required and that supplied) related to lower levels

of autonomy, control over practice and nurse-doctor relations.

TABLE 64 NURSING WORK INDEX REVISED & NURSING DEMAND/SUPPLY

Kendall's τ Nursing Demand/Supply

Nurse autonomy (mean) -0.219(*)

Nurse control over practice (mean) -0.174(*)

Nurse-doctor relations (mean) -0.298(**)

Nurse leadership (mean) -0.130

Resource adequacy (mean) -0.147

* Correlation is significant at the 0.05 level (2-tailed)

** Correlation is significant at the 0.01 level (2-tailed)


94 FINDINGS

Environmental Complexity Scale

This instrument was completed by nurses per shift2. Compared to results in the

NSW study (Duffield et al., 2007), nurses in ACT scored slightly higher for

unanticipated changes in patient acuity (6.4, SD = 1.02), and identically in the other two

sub-scales.

TABLE 65 ENVIRONMENTAL COMPLEXITY SCALE

NSW 2004/5§ ACT*

Mean SD Mean SD Min Max

Re-sequencing of work in response to others

5.9 0.88 5.9 0.79 4.3 9.3

Unanticipated changes in patient acuity

6.3 1.04 6.4 1.02 4.6 10

Composition and characteristics of the care team

6.4 1.06 6.4 1.28 0 10

* N = 612 (Shifts) § N = 6839 (Shifts)

Items one and two on the Environment Complexity Scale referred specifically to the

impact of students on the ward. In both instances, the majority of responses indicated

that students were not present on that shift (Table 66 & Table 67). When students were

present on the ward, over half of respondents suggested that their workload increased.

TABLE 66 ECS ITEM 1: STUDENTS ON THE UNIT REQUIRED SUPERVISION AND ASSISTANCE

Students required supervision/assistance Frequency Percent

Increased 102 16.7

Decreased 15 2.5

Same 55 9.0

N/A 440 71.9

N = 612 (Shifts)

2 Note that the term „shift‟ indicates the shift as reported by the respondent. It is not the same as

a „shift-period‟ derived from roster data (see Table 6, page 25).



TABLE 67 ECS ITEM 2: STUDENTS WANTED ACCESS TO CHARTS, EQUIPMENT AND SUPPLIES

Students wanted access to charts, etc Frequency Percent

Increased 97 15.8

Decreased 5 0.8

Same 57 9.3

N/A 453 74.0

N = 612 (Shifts)

Quality of Care

Nurses were asked on the Environmental Complexity Scale “How would you

describe the quality of your nursing care delivered during this shift?” The response

choices were “excellent”, “good”, “fair” and “poor”. They were also asked on the Nurse

Survey for their view of the changes in quality of care over the past 12 months (see

Appendix 7, Instruments).

Table 68 indicates the quality of care reported per shift. Of the 612 responses 88%

of nurses rated the quality of care as excellent or good while 12% reported it as fair or

poor over the past shift.

When asked to indicate whether the quality of care given over the last 12 months

had changed on their wards, 80% of respondents indicated that it had improved or

remained the same, and 20% believed that it had deteriorated (Table 69).

TABLE 68 QUALITY OF CARE PER SHIFT

Frequency Percentage

Excellent/good 537 87.7

Fair/poor 75 12.3

Total 612 100.0

TABLE 69 QUALITY OF CARE OVER THE PAST YEAR


Improved 38 19.0

Remained same 122 61.0

Deteriorated 40 20.0

Total 200 100.0


96 FINDINGS

Tasks delayed or left undone

The Environmental Complexity Scale allowed measurement of nurses‟ perceptions

of tasks delayed or left undone. Respondents were asked “Which of the following tasks

were necessary but left undone because you lacked the time to complete them?” and

to “Check all that apply” from the list provided. Rates were calculated for each nurse

per shift across the cross-sectional sample (Table 70). On average each nurse was

delaying 1.3 tasks per shift and not completing 1.5 tasks per shift. A small response

rate was seen for night shift so statistical comparisons could not be made, but an

apparently similar rate of tasks delayed was found, with a lower rate of tasks not done.

TABLE 70 TASKS DELAYED OR NOT DONE PER NURSE PER SHIFT

Shift N Mean SD Min Max

Tasks Delayed

Morning 379 1.4 1.32 0 4

Evening 205 1.2 1.20 0 4

Night 28 1.2 1.34 0 4

All Shifts 612 1.3 1.29 0 4

Tasks Not Done

Morning 379 1.7 1.87 0 8

Evening 205 1.5 1.76 0 8

Night 28 0.5 1.00 0 4

All Shifts 612 1.5 1.82 0 8

When compared by hospital peer group using ward means, a higher rate of tasks

delayed was found in the A group hospital, while a higher rate of tasks not done was

found in the B1 group hospital. These data were also compared by peer group with

NSW data (Table 71). In regard to tasks delayed, ACT had a slightly higher rate in A

group, and a lower rate in B1 group. Tasks not done were lower than NSW in both

hospital groups.

TABLE 71 COMPARISON OF ACT & NSW TASKS DELAYED OR NOT DONE, MEAN PER WARD, BY HOSPITAL PEER

GROUP

ACT* NSW§


Tasks delayed A 1.4 1.0 1.8 1.3 0.3 2.3

B1 1.2 1.0 1.4 1.5 1.1 2.3

Tasks not done A 1.5 0.9 2.2 1.6 0.1 3.4

B1 1.6 1.3 2.1 2.0 1.1 3.2

* N=14 wards (10 in A group hospital, 4 in B1 group hospital) § N=65 wards (49 in A group hospitals, 16 in B1 group hospitals)



Detailed analysis of these data (Table 72) show that over the 612 shifts for which

data were collected, routine vital signs, medications or dressings were reported as not

done on 49 occasions (8%) and were delayed 165 times (27%). In addition, routine

mobilisation or turns in bed were not done on 42 occasions (6.9%) and delayed 229

times (37.4%); delay in administering PRN (as needed) pain medication occurred 141

times (23%) and delayed response to patient bells occurred 282 times (46.1%).

Necessary tasks left undone included routine teaching for patients and families

which occurred 80 times (13.1%) and nurses acknowledged omitting preparing the

patient and family for discharge on 71 occasions (11.6%). Comforting and talking to

patients was not done 210 times (34.3%) and adequate documentation of nursing care

was omitted 77 times (12.6%). Pressure area care was left undone 117 times (19.1%)

and oral hygiene 128 times (20.9%). Most categories had similar or lower rates

compared to recent NSW research (Duffield et al., 2007).

TABLE 72 TASKS NOT DONE OR DELAYED DUE TO TIME PRESSURES

NSW

2004/5

Tasks

Not Done

Tasks

Delayed

% Freq % Freq %

Comforting/talking with patients 39.5 210 34.3 -- --

Nursing care plan not done -- 151 24.7 -- --

Oral hygiene 19.3 128 20.9 -- --

Pressure area care 24.0 117 19.1 -- --

Routine teaching for patients and families 16.3 80 13.1 -- --

Adequately documenting nursing care 15.0 77 12.6 -- --

Prepare patient and family for discharge 11.0 71 11.6 -- --

Routine vital signs, medication 7.3 49 8.0 165 27.0

Routine mobilisation 8.2 42 6.9 229 37.4

Other 1.9 22 3.6 -- --

Delay in responding to patient bell 50.6 -- -- 282 46.1

Delay in administering PRN pain medications 21.5 -- -- 141 23.0

N=612 (Shifts)

When asked to specify “other” tasks delayed or left undone, 22 were cited. Analysis

of these data (Table 73) shows that respondents reported a lack of time to complete

patient hygiene tasks i.e. showering was thought of as necessary but left undone on

five occasions (22.7%), dressings on three occasions (13.6%). Lack of time to

complete and maintain fluid balance charts was mentioned separately by two

respondents (9.1%), as was patient/family support, time to complete wound charts and


98 FINDINGS

assessment/discharge activities were cited as being other tasks necessary but left

undone. Finally, on one reported occasion a patient‟s enema was left undone.

TABLE 73 OTHER TASKS NECESSARY BUT NOT DONE

Frequency Percent

Showering 5 22.7

Other 4 18.2

Dressings 3 13.6

Patient/Family support 2 9.1

Assessment/Discharge 2 9.1

Fluid balance 2 9.1

Wound charts 2 9.1

Monitoring 1 4.5

Patient enema 1 4.5

Total 22 100%

Time available to deliver care

Respondents were asked “Please rate the time available to deliver care on this shift

compared to the last five shifts you have worked”, with the choice of less, the same, or

more time than usual. Table 74 shows that 54.4% of respondents had about the same

amount of time as usual to deliver care on the current shift compared to the last five

shifts worked. 22.7% reported they had more time than usually available to deliver care

on their most recent shift, with 22.9% indicating they had less time than usual (Table

74).

TABLE 74 TIME AVAILABLE TO DELIVER CARE PER SHIFT

Response Frequency Percent

Less time than usual 140 22.9

About the same amount of time as usual 333 54.4

More time than usual 139 22.7

Total 612 100.0

How much more time needed to deliver care

Nurses were asked, “Approximately how much more time do you need to give the

type of care stated in the nursing care plan or your assessment of patients‟ needs

today?” Respondents were asked to tick only one response.



Table 75 shows that 26.3% of nurse respondents stated they needed no more time

that shift to provide the type of care stated in the nursing care plan, 33.8% reported that

up to 30 minutes more time was needed, and nearly 40% of respondents felt that more

than 30 additional minutes were necessary to deliver care, 11% of whom felt they

needed more than 60 minutes to do so. The additional time required may be offset by

the use of support worker roles.

TABLE 75 HOW MUCH MORE TIME NEEDED

Response Frequency Percent

No more time needed 161 26.3

< 15 minutes 52 8.5

15-30 minutes 155 25.3

31-45 minutes 114 18.6

46-60 minutes 62 10.1

> 60 minutes 68 11.1

Total 612 100.0

An examination of these data by hospital peer group (Table 76) showed that there

was more time required by nurses to complete their care per shift in the B1 hospital.

Compared to NSW data, slightly more time was required in both groups.

TABLE 76 COMPARISON OF ACT & NSW TIME NEEDED PER SHIFT, MEAN PER WARD, BY HOSPITAL PEER GROUP

ACT* NSW§


Tasks not done A 26.6 18.6 34.3 25.7 9.8 37.8

B1 28.1 20.3 39.6 27.8 19.0 37.5


Indirect or Additional Care Activities

Nurses were asked, “Which of the following tasks did you perform during this shift”.

The response was to “Check all [boxes] that apply”. Across the entire sample of 612

shifts, a total of 1694 indirect care activities were completed. The average proportion of

nurses required to undertake these tasks per ward-day is shown in Table 77.

This table indicates that, by ward-day, 46% of nurses were required to deliver or

retrieve patient meal trays, 34% order, co-ordinate or perform ancillary work, 42%


100 FINDINGS

undertake cleaning and 43% clerical duties. 30% arrange discharge referrals and

transport, while 9% transport patients. 38% of respondents state they are required to

start IVs while performing ECGs was reported by 14% and routine phlebotomy by 16%.

TABLE 77 PROPORTION OF NURSES UNDERTAKING INDIRECT CARE ACTIVITIES, PER WARD-DAY

Mean SD

Deliver/retrieve patient meal trays 45.5 27.40

Order/coordinate/perform ancillary work 34.4 24.48

Start IVs 38.4 25.41

Arrange discharge referrals and transport 29.7 23.69

Undertake ECGs 14.3 19.31

Undertake routine phlebotomy 15.9 21.69

Transport patients 9.3 15.23

Undertake cleaning duties 42.2 22.96

Undertake clerical duties 43.2 23.46

N=67 (Ward-Days)

Table 78 shows the proportion of the above tasks undertaken per shift. The majority

were completed during the morning shift (64.1%), and fewer during the evening shift

(31.5%). Relatively few were undertaken overnight (4.4%) with the exception of routine

phlebotomies, of which nearly 10% occurred between at night. When these data are

matched to the skill mix category of respondents to the nurse survey, approximately

75% of these tasks were reported by RNL1, with 20% by ENs or AINs (data not

shown).

TABLE 78 INDIRECT CARE ACTIVITIES BY SHIFT

Morning Evening Night

N % N % N %

Deliver/retrieve patient meal trays 163 56.6 111 38.5 14 4.9

Order/coordinate/perform ancillary work 146 66.1 68 30.8 7 3.2

Start IVs 139 65.0 68 31.8 7 3.3

Arrange discharge referrals / transport 138 78.4 36 20.5 2 1.1

Undertake ECGs 55 66.3 23 27.7 5 6.0

Undertake routine phlebotomy 61 59.2 32 31.1 10 9.7

Transport patients 39 70.9 15 27.3 1 1.8

Undertake cleaning duties 174 61.9 94 33.5 13 4.6

Undertake clerical duties 171 62.6 86 31.5 16 5.9

Total 1086 64.1 533 31.5 75 4.4

N=612 (Shifs)



Violence Experienced

Nurses were also asked about their experience of violence: “In the last 5 shifts you

worked, have you experienced any of the following while carrying out your

responsibilities as a nurse”. The response was “yes” or “no” to physical assault, threat

of assault, and emotional abuse (Table 79). Emotional abuse was experienced by 33%

of respondents but by up to a maximum of 58% of staff on one ward. In terms of threat

of violence 21% experienced this and while there were wards where no staff

experienced a threat of violence, up to a maximum of 67% of staff on one ward did.

The results are similar for physical violence, where 15% of staff experienced this in the

past five shifts and up to 58% of staff on a ward did so.

TABLE 79 NURSES EXPERIENCING VIOLENCE IN THE LAST 5 SHIFTS

Entire Sample* Average Per Ward§

Frequency Percentage Min% Max%

Physical violence 30 15.0 0.0 58.3

Threat of violence 41 20.5 0.0 66.7

Emotional abuse 66 33.0 5.3 58.3 * Proportion of nurses experiencing violence in entire sample (N=200 Nurses) § Proportion of nurses experiencing violence per ward (N=16 Wards)

Respondents were also given the opportunity to choose the source of violence from

a list provided. Nurses indicated that patients and families were responsible for most

physical assaults (96.6%) and threats of assault (95.1%). The majority of emotional

abuse was also from patients and their families (69.7%) but was also reported from co-

workers. These figures are similar to NSW data (Table 80).


102 FINDINGS

TABLE 80 SOURCE OF VIOLENCE TOWARDS NURSES (COMPARISON WITH NSW [DUFFIELD ET AL., (2007)])

Ph

ysic

al

Vio

len

ce

%

Th

rea

t of

Vio

len

ce

%

Em

otio

na

l

Ab

use

%

NS

W

AC

T

NS

W

AC

T

NS

W

AC

T

Patient 87.4 90.0 75.5 87.8 40.2 30.3

Patient + family/visitor 7.1 3.3 10.5 4.9 16.1 15.2

Family/visitor 2.5 3.3 8.6 2.4 14.1 24.2

Nursing co-worker 0.6 3.3 1.9 2.4 15.1 9.1

Patient + nursing co-worker 0.6 0.0 0.2 0.0 4.0 6.1

Other 0.0 0.0 0.6 2.4 1.7 3.0

Physician 0.0 0.0 0.2 0.0 0.9 1.5

Patient + physician 0.0 0.0 0.0 0.0 0.7 1.5

Patient + family/visitor + physician + nursing co-worker

0.0 0.0 0.0 0.0 1.5 1.5

Patient + physician + nursing co-worker 0.0 0.0 0.0 0.0 0.0 3.0

Family/visitor + nursing co-worker 0.0 0.0 0.0 0.0 0.9 1.5

Physician + nursing co-worker 0.0 0.0 0.0 0.0 0.3 3.0

Patient + family/visitor + nursing co-worker

1.8 0.0 2.1 0.0 3.6 0.0

Family/visitor + physician 0.0 0.0 0.2 0.0 0.0 0.0

Patient + family/visitor + physician 0.0 0.0 0.0 0.0 0.9 0.0

Number of nurses* 326 30 474 41 881 66

* ACT N = 200 (Nurses); NSW N = 2278 (Nurses)

Top 3 categories indicated in bold

Satisfaction and Intention to Leave

Most nurses (71.5%) were satisfied with their current job (Table 81), although more

were satisfied with nursing as a profession (79.5%). Almost three-quarters of

respondents (74%) were not intending to leave their current job (Table 82).



TABLE 81 NURSES' SATISFACTION WITH CURRENT JOB & NURSING AS A PROFESSION

Frequency Percent

Satisfied with current job 143 71.5

Dissatisfied with current job 57 28.5

Satisfied with nursing as a profession 159 79.5

Dissatisfied with nursing as a profession 41 20.5

Total 200 100

N=200 (Nurses)

TABLE 82 NURSES PLANNING TO LEAVE THEIR CURRENT JOB

Frequency Percent

Do not intend to leave current job in the next 12 months 148 74

Intend to leave current job in the next 12 months 52 26

Total 200 100

Patient Outcomes

As described previously, patient outcomes in the cross-sectional data were collected

from both the patient record and ward-level reporting mechanisms. The patient

outcomes here were falls with and without consequences and medication errors with

and without consequences. These data were aggregated to the ward level in order to

conduct correlation analyses and regression models. The dependent variables (patient

outcomes) were in all cases calculated as “percentage of patients who experienced

(the event) per ward”. Regression models, either linear or logistic, were conducted and

Beta (β) weights calculated where possible to indicate relativities between the factors.

Adverse Events

Adverse events were collected from the patient record or ward reporting system.

Twenty six (4.3%) patients in the study were found to have experienced a fall with or

without injury (Table 83), and some of these patients had experienced both types of

fall. Two patients experienced medication errors without consequences. These adverse

event rates were very low compared to other studies and may be indicative of the short

sample period per ward, data collection issues, or unknown factors.


104 FINDINGS

TABLE 83 PATIENTS EXPERIENCING ADVERSE EVENTS

Frequency Percent

Medication errors without patient consequences* 2 0.3

Falls with injury 13 2.2

Falls without injury 14 2.3

Falls (any – either with or without injury)§ 26 4.3

N=601 (Patients) * No patients recorded medication errors with adverse consequence § Some patients experienced both types of fall. See Glossary, page 22

These data were also calculated as the percentage of patients per ward who

experienced these adverse events, by hospital peer group (Table 84). This showed a

higher proportion of patients in the A group hospital experienced any type of fall, a fall

with injury or medication error without consequences, and a higher proportion in the B1

hospital experienced falls without injury. Compared to NSW data, a lower proportion of

patients in the ACT experienced medication errors without consequence in both

groups, and falls with or without injury in the B1 group. In the A group, a greater

proportion of patients in the ACT experienced falls.

TABLE 84 COMPARISON OF ACT & NSW PATIENT OUTCOMES, MEAN % OF PATIENTS PER WARD, BY HOSPITAL PEER

GROUP

ACT* NSW§


Medication errors without patient consequences*

A 0.5 0.0 2.6 14.0 0.0 52.0

B1 0.0 0.0 0.0 21.0 4.0 64.0

Falls with injury A 3.3 0.0 11.5 1.0 0.0 6.0

B1 0.4 0.0 1.8 1.0 0.0 3.0

Falls without injury A 1.5 0.0 7.7 1.0 0.0 7.0

B1 2.6 0.0 6.3 3.0 0.0 14.0

Falls (any – either with or without injury)§

A 4.4 0.0 15.4 2.0 0.0 11.0

B1 3.1 0.0 6.3 4.0 0.0 14.0


Although statistically significant correlations were found at the ward and ward-day

level between these adverse events and a number of other variables, examination of

scatter plots showed that this was an effect of the low rates, with the majority of data

points clustered about zero and a few outliers influencing the results. Therefore, no

relationships could be established.



In addition to these data, late administration of medication (more than 30 minutes,

see definitions Table 5, page 25) was recorded per patient-day. These time-based

medication errors were recorded on 40 of the 1758 patient-days (Table 85). On most of

these 40 patient-days between 1 and 4 errors were recorded, and on one patient day

between 5-14 errors were recorded. It is possible these errors could be recorded in the

aforementioned patient data collection also, so that only a summary of frequency is

presented here. Out of the 601 patients studied, 34 (5.7%) experienced this type of

error (data not shown).

TABLE 85 TIME-BASED MEDICATION ERRORS


1-4 errors per patient-day 39 2.2

5-14 errors per patient-day 1 0.1

N=1758 (Patient-Days)

Outcome Predictors

Tasks Not Done & Tasks Delayed per Ward-Day

Linear regression models for tasks delayed and not done were developed with data

at the ward-day level. Analysis at this level of data for these outcomes is more

meaningful as it examines the overall picture of the ward for a given day.

Similar factors were influential in regard to both outcome variables (Table 86 &

Table 87). The proportion of nurses indicating less time available to deliver care, the

amount of additional time required to complete care this shift, and the proportion of

hours worked by agency staff were common elements. As these factors increased so

did the rate of tasks delayed or not done. Additional predictors were identified in regard

to the rate of tasks not done (Table 86). These included the proportion of patients

admitted from a care facility and the amount of involuntary overtime reported. Both

models explained over 30% of the variance.


106 FINDINGS

TABLE 86 LINEAR REGRESSION ON TASKS NOT DONE

Direction β Weight

Amount more time needed this shift Positive (+) 0.440

Proportion of patients admitted from a care facility Positive (+) 0.347

Proportion of hours worked by agency staff Positive (+) 0.305

Average weekly overtime worked - involuntary paid Positive (+) 0.232

Proportion of nurses indicating less time available to deliver care Positive (+) 0.182

Adjusted R2 = 0.315

N= 67 (Ward-Days)

p≤0.05

TABLE 87 LINEAR REGRESSION ON TASKS DELAYED

Direction β Weight

Proportion of nurses indicating less time available to deliver care Positive (+) 0.469

Amount more time needed this shift Positive (+) 0.236

Proportion of hours worked by agency staff Positive (+) 0.183

Adjusted R2 = 0.367

N= 67 (Ward-Days)

p≤0.05

Correlation of factors shown to be significant predictors of tasks delayed or not done

were generally consistent with these regression models, although two items did not

show a statistically significant correlation (Table 88).

TABLE 88 CORRELATION OF FACTORS IN LINEAR REGRESSION MODELS ON TASKS NOT DONE & TASKS DELAYED

Kendall's τ

Tasks

not

done

Tasks

delayed

Additional time needed this shift 0.260(**) 0.361(**)

Proportion of patients admitted from a care facility 0.189(*) -0.077

Proportion of hours worked by agency staff 0.064 0.095

Average weekly overtime worked - involuntary paid 0.031 0.029

Proportion of nurses indicating less time available to deliver care 0.156 0.244(**)


**Correlation is significant at the 0.01 level (2-tailed)

„The amount of additional time needed this shift‟ was highly correlated with two

outcome variables; tasks not done (τ =0.260) and tasks delayed (τ =0.361). As tasks

not done or delayed increased, the amount of additional time reported as needed this

shift also increased. Likewise an increase in the proportion of nurses indicating less

time available to deliver care indicated an increase in tasks delayed (τ =0.244). Also an



increase in the proportion of patients admitted from a care facility led to an increase in

tasks delayed (τ =0.189).

Nurse Outcomes

Analyses were conducted for the nurse outcome variables - job satisfaction,

satisfaction with nursing, and intention to leave the current job. These variables were

measured at the nurse level. Analysis at this level is appropriate to examine the

influence of workload and other variables on individual nurse outcomes.

Job Satisfaction

Nurses who were satisfied with their profession, had adequate resources to do their

job, and who worked on wards with a higher overall amount of nursing hours were

more likely to be satisfied with their current job. Older nurses, and those nurses

missing a higher number of shifts, were less likely to be satisfied with their job (Table

89).

TABLE 89 LOGISTIC REGRESSION ON JOB SATISFACTION

Direction β Weight

Number shifts missed work Negative (-) -0.558

Satisfaction with nursing Positive (+) 0.382

Resource adequacy Positive (+) 0.367

Total nursing hours provided on the ward Positive (+) 0.335

Age Negative (-) -0.228

Pseudo R2=0.400

N=149 (Nurses)

p≤0.05

Satisfaction with Nursing

Nurses who were satisfied with their job and who had adequate resources were

more likely to be satisfied with their profession, while those in temporary employment

were less satisfied with nursing. A higher patient turnover also predicted satisfaction

with nursing (Table 90).


108 FINDINGS

TABLE 90 LOGISTIC REGRESSION ON SATISFACTION WITH NURSING

Direction β Weight

Job satisfaction Positive (+) 0.536

Temporary employment status Negative (-) -0.402

Resource adequacy Positive (+) 0.145

Patients per bed Positive (+) 0.099

Pseudo R2= 0.325

N=149 (Nurses)

p≤0.05

Intention to Leave Current Job

Nurses were more likely to intend to leave their current job if they were required to

resequence their work frequently, if there was a higher proportion of agency hours

worked on their ward and if demand for nursing care per day exceeded supply. Nurses

who had worked longer and who were satisfied with their job were less likely to plan to

leave. Nurses indicating they had more time to deliver care per shift were more likely to

leave; a finding worth further study. Those working on wards with a higher proportion of

patients waiting for a care facility, were less likely to intend to leave (Table 91).

TABLE 91 LOGISTIC REGRESSION ON INTENT TO LEAVE CURRENT JOB

Direction β Weight

Nursing demand/supply Positive (+) 0.392

Proportion of patients waiting for a care facility Negative (-) -0.390

Years worked as a nurse Negative (-) -0.321

Job satisfaction Negative (-) -0.267

Proportion of hours worked by agency Positive (+) 0.246

Resequencing of work in response to others Positive (+) 0.232

More time available to deliver care Positive (+) 0.216

Pseudo R2=0.339

N=149 (Nurses)

p≤0.05

Correlation between the factors identified in the logistic regression analysis and the

individual outcome variables showed similar relationships. Some variables, such as the

proportion of hours worked by agency staff, displayed relationships with the outcomes

even though they were not statistically significant in the regression models (Table 92).



TABLE 92 CORRELATION OF FACTORS IDENTIFIED IN LOGISTIC REGRESSION ON INDIVIDUAL NURSE OUTCOMES

Kendall's τ Job

satisfaction

Satisfaction

with

nursing

Intent

to

leave

current

job

Job satisfaction 1.000 .357(**) -.195(*)

Satisfaction with nursing .357(**) 1.000 -0.155

Intent to leave current job -.195(*) -0.155 1.000

Number shifts missed work -.141(*) 0.049 -0.048

Resource adequacy .272(**) .181(**) -.145(*)

Proportion of hours worked by agency 0.042 -.158(*) .253(**)

Time available to deliver care 0.000 -0.047 0.090

Resequencing of work in response to others 0.002 -0.020 0.132

Temporary employment 0.148 -.252(**) 0.092

Years worked as a nurse -0.098 0.012 -.153(*)

Total nursing hours .232(**) .161(*) -0.047

Nursing demand/supply -0.132 0.049 0.072

Patients per bed .150(*) .197(**) -0.134

Proportion of patients waiting for a care facility -0.012 .140(*) -.244(**)


**Correlation is significant at the 0.01 level (2-tailed)

As expected, job satisfaction was positively correlated with satisfaction with nursing

and total nursing hours as found in the regression model. In addition, increases in

resource adequacy were positively correlated with job satisfaction, while the number of

shifts missed this week was negatively correlated with job satisfaction and were not

included in the regression model. Increases in significant variables with a positive τ-

value are likely to result in improved job satisfaction. However, the number of patients

per bed was positively correlated with job satisfaction (τ=.150, p≤.05) reflecting earlier

findings that nurses are happier and more satisfied when they are busier.

Highly significant correlations between satisfaction with nursing and its predictor

variables as in the regression model (Table 90) were as expected. In addition to these,

satisfaction with nursing was positively correlated with total nursing hours and the

proportion of patients waiting for a care facility. The proportion of hours worked by

agency staff was negatively correlated with satisfaction with nursing.

In regard to intention to leave current job, although resource adequacy was not a

significant predictor in the regression model, it is significantly correlated with Intention


110 FINDINGS

to leave (τ =-.145, p≤.05) and indicates that as resource adequacy improves the

intention to leave the current job declines.



5. Limitations

Any study using standard administrative data is limited to what is in the data. In this

instance, the administrative data mined in the longitudinal study were supplemented by

the cross-sectional data collection to provide information on variables that are simply

not part of standard data collection. These were particularly those variables concerned

with the quality of the working environment and the nursing outcomes.

In previous studies it has been shown that there is wide variation in a range of the

variables captured in both the longitudinal and dross-sectional data. This potential

variation should be considered when applying these findings outside the sampled

hospitals.

The longitudinal data were essentially the entire population of patients for the period

studied and the entire record of nurses working for the periods available. Still, the data

cover only about two years. Similarly, the cross-sectional data include all eligible

nursing units after maternity, newborn, pediatric and psychiatric units were excluded.

There are several limitations in regard to the longitudinal analysis:

Limited amount of usable data

Lack of large learning (reference) set for threshold contrast method

Lack of direct link between ward and adverse event

Potential seasonal effects for data time span

As discussed earlier, the time and place of OPSN could not be determined in the

data so attribution to the nursing unit is a limitation.

Instrument reliability and validity have been reported. A high proportion of

consenting nursing staff responded to the surveys overall (71%), but it is not known

whether important responders declined to participate. The sampling period for the

cross-sectional study was only one week per nursing unit and although it appeared to

be similar to longitudinal data in terms of skill mix, it is not known how representative

that week might have been in regard to patient type and the remainder of the the unit‟s

life. This short sample period, unknown data collection issues or other factors may

have been related to the very low number of patient adverse events collected.


112 SUMMARY AND DISCUSSION

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6. Summary and Discussion

Synopsis of Objectives, Design and Measures

This study used a combination of longitudinal and cross-sectional data collection to

examine nursing workload (and changes therein), patient acuity and length of stay, skill

mix and the working environment and their relationships with patient outcomes in two

hospitals in the Australian Capital Territory. The unit of analysis was the nursing ward,

the business and operational unit of the hospital. The project was designed to provide

information to assist policy development in the ACT toward innovations in care delivery.

In particular it was to determine approaches to staffing which would provide for the

health needs of the population, achieve high standards of care and enhance patient

outcomes. The focus was on medical/surgical nursing units where the majority of

nurses work.

Patient data were obtained on all discharges from the two hospitals for two financial

years, 2005 and 2006 (approximately 185,000 hospital morbidity records of which

40,538 contributed to the final analysis). Nursing payroll data were obtained for roughly

the same period. Payroll data allowed tracking nurses to the wards on which patients

were nursed. Eventually 398 ward months of data were used in the analysis. Casemix

control to the ward level provided risk adjustment. Twelve Outcomes Potentially

Sensitive to Nursing (OPSN) – adverse events coded in administrative data – were

studied along with length of stay as an outcome.

Cross-sectional data which involved original data collection at ward level, took place

over a three week period at Canberra Hospital and two weeks at Calvary Hospital

toward the end of the longitudinal data collection. Surveys of nurses collected data on

job satisfaction, perception of the ward working environment (including environmental

complexity), and perception of the extent to which work was accomplished fully and on

time. Additional data collection at ward level obtained data on staffing and patient

outcomes as falls and medication errors.

Statistical treatment was designed to determine patterns of nursing resources and

their relationship to patient outcomes, and in the case of cross-sectional data, nurse

outcomes such as job satisfaction. Where appropriate, comparisons to a similar study

conducted in New South Wales (NSW) were made.



Discussion of Results

Sixteen medical/surgical nursing units were included in the sample. Relevant

nursing data were available for 15 of these units, two of which were collapsed into one

for analysis for statistical reasons leaving 14 units as the sample for the longitudinal

analysis. There were 16 units in the cross-sectional study, but two did not provide

complete roster data.

Over time, nursing workload as measured by nursing hours per patient hour

increased, especially in one of the hospitals; the ratio of nurse hours on ward to patient

hours on ward decreased. Skill mix measured as the percentage of RN hours worked

was quite variable ranging from 50% to 80% at one hospital and 54% to 84% at the

other. Skillmix was lower in wards with aged or rehabilitation casemix, higher in

specialty surgical wards. This is not an unexpected finding but it raises questions about

the conventional wisdom that decrees a lesser skilled workforce for aged or infirm

patients, many of whom may actually be more frail than surgical specialty patients.

Patient movements can contribute to nursing workload. The findings here indicate

the number of wards per patient episode over the two years (average length of stay =

4.0) were on average 1.24 and 1.32 at the two hospitals, considerably lower than the

NSW result of 2.26. In addition the number of patients per bed per day was on average

one, compared to 1.25 in NSW. This may reflect better bed management strategies.

In terms of the nursing hours required and provided, there was an average

difference of 0.5 hours per patient day, less than in NSW data. Of interest is that in the

ACT, both the hours of nursing care required per patient day and nursing hours per

patient day provided were higher in the A group hospital than in NSW. The reverse is

true of the B group hospital, where hours of nursing care required per patient day and

nursing hours per patient day provided were less than in NSW.

Nursing workload in ACT is influenced by the number of different AR-DRGs per

nursing unit. There is a wide degree of variability ranging from 164 – 459 DRGs per

ward, from a possible range of 613. It cannot be expected that nurses are equally

skilled or comfortable caring for a wide range of patient types, each with its treatments,

procedures, protocols, medications and physician teams. Smaller hospitals, such as

found in ACT, cannot create the number of specialty units found in larger hospitals, a

fact that managers need to appreciate. The nursing workload will always feel heavier in



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wards with a large number of different AR-DRGs. Still, the role of casemix in staffing

has been little identified nor studied.

As we have found in previous research, there was considerable variation in nursing

unit staffing and skill mix over only a two year period, variation that was neither

seasonal nor predictable. There should be no expectation that every nursing unit has

the same ratio of nursing hours per patient day nor the same skill mix for different

mixes of cases. However, such variation itself increases nursing workload and may

contribute to job dissatisfaction. Indeed, the cross-sectional results showed that

adequacy of nursing resources was one of the stronger predictors of nursing job

satisfaction. Decisions about how to titrate nursing resources to patient types should be

made consciously, not simply allowed to vary with the ability of the nurse manager to

advocate for resources or the constraints imposed by a tight labour market. Indeed, our

analysis suggested that parity in nursing staffing could be achieved with modest

increases in resources.

Analysis also showed that increased skill mix was associated with decreased length

of stay, although the relationship was not strong in this sample. It has been observed

that physicians admit patients to hospital but nurses get them out. Yet skill mix has

rarely been considered in itself an efficiency investment.

When patient outcomes as Outcomes Potentially Sensitive to Nursing (OPSN) were

examined, it was found that increasing RN hours by 10% could produce decreases in

the adverse event rates studied from 11% to 45%. While we did not attempt cost

analyses in this study, it is known that adverse outcomes such as hospital-acquired

decubiti, infections etc. increase length of stay and cost. It should be in hospitals‟

interest to invest in the resource(nurses) to lower such rates, not only for financial

reasons but more importantly, to minimise harm to patients.

The cross-sectional data amplified these findings. Comparisons were made where

appropriate with our New South Wales study. This is a new area of inquiry, however,

so the NSW findings cannot be taken as the “gold standard” – they are simply

descriptive of the situation as the data revealed it in the prior study. It was not possible

to determine the impact that medicaton endorsed ENs might have on medication

errors.



The nursing work environment in ACT was rated as somewhat better by the ACT

nurses than NSW nurses rated theirs, and, largely because there were only two

hospitals in the ACT study, we did not find the enormous variations in nursing units that

we had found in NSW. Still, with a sample of only 16 units, there was a striking amount

of variation in nearly every measure.

Nursing supply/demand analysis showed that only 25% of the units were in

“balance”, with the rest showing a deficit of nursing for patient requirements. When

nurses reported numbers of tasks delayed or not done, these figures were related to a

perception of resource adequacy – staffing, support services etc. That is, where there

were adequate resources, fewer tasks were reported undone or delayed.

It was interesting to note, as it had been in NSW, that nurses on wards with larger

proportions of patients from care facilities and wards with a higher proportion of agency

staff and overtime reported more work undone at the end of shift. These are wards that

are stressed; the necessity for involving agency staff is a signal to managers that

something is not right on the ward with respect to staffing. The finding about patients

from care facilities might signal a systemic problem of coordination of care across

institutions or perhaps an issue of quality of facility care.

A higher proportion of nurses in ACT reported experiencing a threat of violence or

physical violence than did nurses in NSW but less emotional abuse. The perpetrators

were most often patients or families. This is an under-appreciated aspect of nursing

workload.

In terms of nurse outcomes, 71.5% of nurses were satisfied with their current job

and this was related to having adequate resources to do their job and a higher overall

amount of nursing hours. More than three quarters (79.5%) were satisfied with nursing

and again this was related to having adequate resources to do the job. While workload

is an important factor in job satisfaction and satisfaction with nursing, there is evidence

that nurses were more satisfied when they were busier (measured as higher patient

turnover per bed). In terms of workforce planning, 74% of nurses had no intention of

leaving their current job in the next 12 months and as resource adequacy improves, the

intention to leave the current job declines.

Overall, the study of ACT hospitals reveals hitherto unknown patterns in nursing

staffing, the work environment and patient outcomes. The study suggests that to



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successfully manage a hospital system requires an understanding of the nature of the

work and a commitment to matching resources to workload. The workload/staffing

software used in this study was developed from the NSW study and its test here shows

interesting possibilities.



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8. Appendices

Appendix 1 Theoretical Foundations

Appendix 2 Format for Admitted Patient Care Data

Appendix 3 Format for Ward Episode Data

Appendix 4 Matching Wards

Appendix 5 OPSN Analysis

Appendix 6 Staffing of the Study Wards

Appendix 7 Instruments for Cross-sectional Component


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Appendix 1

Theoretical Foundations

(Linda O’Brien-Pallas)

A theoretical framework guides this study. At the meso and micro level, the Patient

Care System Model (Figure 11) developed by O‟Brien-Pallas and colleagues (2001;

2001; 2004) is used to guide the analysis of the relationship among the variables

studied at the nursing subunit level and the hospital level.

FIGURE 11 PATIENT CARE DELIVERY MODEL

The framework considers aspects of patient, nurse, hospital and unit specific inputs

(resources), which influence throughputs within the complexity of the environment.

These independent variables combine to influence nurse patient and system outcomes.

Consistent with General Systems Theory (GST) the patient, nurse and system outputs

Patient Characteristics

• Demographics

•Medical diagnoses

• Admission type

• Pre-operative clinic

Nurse Characteristics

• Demographics

• Professional status

• Employment status

• Education

• Experience

System Characteristics

• Geographic location

• Hospital size

• Unit size, type, patient mix

• Occupancy

System Behaviours

• Workload

• Nurse-to-patient ratios

• Proportion of RN worked hours

• Continuity of care/shift change

• Unit instability

• Overtime

• Use of agency & relief staff

• # of units nurse works on

• Non-nursing tasks

Patient Outcomes

• Medical consequences, including mortality status

• Resource intensity weight

Nurse Outcomes

• Autonomy & control

• Job satisfaction

• Relationships with MDs

• Violence at work

System Outcomes

• Length of stay

• Cost per resource intensity weight

• Quality of patient care

• Quality of nursing care

• Interventions delayed

• Interventions not done

• Absenteeism

• Intent to leave

Patient Care Delivery System

INTERMEDIATE OUTPUTS

• Worked hours

• Utilization

Environmental Complexity Factors

• Resequencing of work in response to others

• Unanticipated delays due to changes in patient acuity

• Characteristics & composition of caregiving team

IINNPPUUTTSS

Feedback

TTHHRROOUUGGHHPPUUTTSS

OOUUTTPPUUTTSS

Perceived Work Environment

Interventions

Patient Care Delivery Model (O‟Brien-Pallas et al., 2004)



serve as dependent variables to the system as a whole (O'Brien-Pallas et al, 2001) but

can also serve as independent variables for other analysis of the system.

The underlying assumptions of the GST are as follows: GST is a general science of

wholeness, concerned with the problems of organisation and dynamic interactions

manifested in the difference of the behaviour of the parts when isolates (Falco & Lobo,

1990; Freeman, 2005; Putt, 1978). GST believes an organisation must be “open” and

continually change, adapt and interact to meet the challenges posed by both the

internal and external environment, in order to meet the needs of their clients and

stakeholders (Shortell et al, 1991; Daft, 1995; Freeman, 2005). An open system

interacts with the environment, taking input from the environment, subjects it to some

form of transformation process and then produces an output (Nadler & Tushman,

1980).

The holistic view that GST provides, allows a comprehensive and specific view of

the system or individual under investigation, never as the mechanistic accumulation of

parts in segregated causal relationships (Laszlo, 1975). A system is characterised by a

number of constraining but interacting factors, each fulfilling a function not

accomplished by the others which connect through communication and feedback

mechanisms (Fabb, Chao, & Chan, 1997). Basic concepts of GST are those of: 1)

nonsummativity, 2) input, throughput and output, 3) entropy, 4) equifinality/ multifinality,

5) equilibrium, 6) feedback and 7) control (Fabb et al., 1997; Freeman, 2005; Putt,

1978).

GST concepts can be represented in the following propositions:

1. A system is a set of interacting and interrelated parts. A system is more than

a sum of its parts; its characteristics derive from the association among the

parts and from the system‟s connection with the environment (Fabb et al.,

1997; Freeman, 2005). In this study unit characteristics including patient

characteristics, staff characteristics, system characteristics and behaviours

influence throughput including environmental complexity, interventions and

perceived work environment. These in turn influence intermediate outcomes

including workload and staff utilisation and these in turn influence patient,

nurse and system outputs.

2. Open systems have permeable boundaries that continually engage in the

input, throughput and output of matter, energy and information (Fabb et al.,


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1997; Freeman, 2005). In this study the system is conceptualised as

interconnecting parts including nursing, patient and system variables and

their relationship workload and staff utilisation recognising these in turn

influence patient, nurse and system outputs.

3. Systems are capable of negative entropy, that is, systems can survive and

grow rather than decay and die, if they are able to work out mutually

beneficial relationships with their environment (negentropic) (Fabb et al.,

1997). The process of entropy is universal, existing in both closed and open

systems (Putt, 1978). In this study the system will be explored to identify

factors that influence workload and patient nurse and system outputs.

Through this study areas for improvement within work systems will be

identified and positive change maybe recommended.

4. When acting on a system of interrelated parts, the effects cannot be gauged

on knowledge of inputs alone but must include the entire system. The overall

pattern must be considered, in order to determine the results of specific

stimulus/ stimuli. In other words, the results of equifinality and multifinality

must be taken into account (Freeman, 2005). In this study the nursing,

patient and system inputs will be viewed within the broader scope of the unit

throughputs and the nurse, patient and system outputs.

5. Systems tend to maintain steady states of dynamic equilibrium, in which

conflicting pressures are balanced. Such steady states have the property of

evolution; the more the system is threatened with disequilibrium, the more

resources it will deploy to maintain or restore balance (Fabb et al., 1997;

Freeman, 2005). In this study the factors that threaten nurses‟ workload and

patient nurse and system outputs will be explored. Further, the current

practice and overall system will not change unless this research is

conducted.

6. To maintain a steady state, open systems need adaptive processes such as

feedback loops and control. This allows the system to detect applicable

changes in the internal and external environment and adjust appropriately

(Fabb et al., 1997; Freeman, 2005). In this study a feedback loop will be

utilised to link the outputs to the inputs and throughputs to demonstrate the

openness of the system.

Consistent with systems theory (Jelinek, 1967), these dependent variables feed

back into the system and, in turn, affect future inputs. This model allows the researcher



to gain comprehension of the nursing system unit and the broader components of the

patient care system. It permits the management of complex interdependent

relationships that exist in the patient care system.

Jelinek (1969), described the patient care systems model comprising inputs and

outputs that can be affected by workload, the environment, and organisation factors.

Inputs are postulated to refer to resources, both personnel and physical, involved in

patient care. Organisational factors capture the form of organisation used in delivering

patient care and include rules and policies. Workload factors explore the workload the

patient imposes on the input resources. Environmental factors include factors that may

affect patient care such as services a hospital offers. Output describes patient

outcomes in terms of the quality and quantity of patient care delivered (O'Brien-Pallas

et al, 2004).


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Appendix 2

Format for Admitted Patient Care Data

Name Size Label Bus.Rules Code/

Library_Table

addttime Date17 Date and Time of admisison admdate & admtime

ageyrs N3 Age in years dateborn, addttime

agedays N3 Age in days of infants aged

under 1 year dateborn, addttime

drg51 S4 DRG5.1 grouped by IMS According to 3M Grouper

Casemix Expert for Windows Version 2.3.3

epis N8 Episode number from

hospital

hospid N2 Hospital Identification Y

pin N8 Patient ID from hospital

sex N1 Sex of patient Y

spdttime Date17 Date and Time of separation sepdate & septime

spyrmth S7 Financial Year and Month of

separation spdttime

pdx S7 Primary diagnosis ICD-10 code

dx2 S7 Additional diagnosis - 2




























Name Size Label Bus.Rules Code/

Library_Table





p1 S8 Procedure 1 ICD-10 code

p2 S8 Procedure 2

p3 S8 Procedure 3

p4 S8 Procedure 4

p5 S8 Procedure 5

p6 S8 Procedure 6

p7 S8 Procedure 7

p8 S8 Procedure 8

p9 S8 Procedure 9

p10 S8 Procedure 10

p11 S8 Procedure 11

p12 S8 Procedure 12

p13 S8 Procedure 13

p14 S8 Procedure 14

p15 S8 Procedure 15

p16 S8 Procedure 16

p17 S8 Procedure 17

p18 S8 Procedure 18

p19 S8 Procedure 19

p20 S8 Procedure 20

p21 S8 Procedure 21

p22 S8 Procedure 22

p23 S8 Procedure 23

p24 S8 Procedure 24

p25 S8 Procedure 25

p26 S8 Procedure 26

p27 S8 Procedure 27

p28 S8 Procedure 28

p29 S8 Procedure 29

p30 S8 Procedure 30

p31 S8 Procedure 31


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Appendix 3

Format for Ward Episode Data

Name Size Label Code/Library_Table

epis N8 Episode no. from admitted

patient care dataset

hospid N2 Hospital Identification Y

pin N8 Patient ID from hospital

wardid S3 Ward identifier Y

wdindt S8 Date patient entered ward

wdintm S4 Time patient entered ward

trtype S1 Type of ward transfer Y

finyr S4 Financial year of ward transfer



Appendix 4

Matching Wards (“Ward Data Transfer Items”)

Hospital ID WardID

Wardcode WardName

82 10A Gastrointestinal Unit

82 11B Orthopaedic

82 12B Rehab and Rheumatology

82 14B Oncology

82 4HD Paediatrics High Dependancy

82 5HD Paediatrics High Dependency

82 5P2 Paediatrics - Isolation

82 5PD Paediatric Day Care on Level 5

82 7AX Holding Overflow Ward

82 7SU Stroke Unit

82 A/N Ante Natal

82 ACU Aged Care Unit

82 BC Birthing Centre

82 BMT Bone Marrow Transplant

82 CAR Coronary Care subacute

82 CAS Emergency

82 CCU Coronary Care (Acute) Unit

82 CLD Cardiac Lab

82 DEL Delivery Suite

82 DIA Dialysis

82 DSU Day Surgery Unit

82 EDS Extended Day Surgery Unit

82 EMU Emergency Medicine Unit

82 END Endocrinology Day Ward

82 GAS Gastro Procedure Unit

82 GAU Gynaecology Assessment Unit

82 HOC Hospital In The Home - Oncology

82 HOM Hospital In The Home

82 ICU Intensive Care Unit

82 ILU Independent Living Unit

82 L4B Paediatrics

82 L5A Adolescent

82 L6A Endocrinology, Respiratory, Cardiology

82 L6B Cardiac Surgery

82 L7A Infectious Diseases & Toxicology

82 L8A Previously renal medicine

82 L8B Renal Medicine

82 L9A Urology, Vascular Surgery

82 L9B Neurology and Neurosurgery

82 NA Post Natal Nursery A

82 NCP NCPH on ICU bed

82 NIC Neonatal Intensive Care

82 NNN Neonatal Nursery

82 ONC Oncology / Chemotherapy day bed

82 PDU Peritoneal Dialysis Unit on L8

82 PNA Post Natal A


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Hospital ID WardID

Wardcode WardName

82 PSA Psychiatry

82 PSD Psychiatry Day Ward

82 PSU Psychiatry

82 ROC Radiation Oncology Day Ward

82 SAT Satellite Dialysis Unit

82 SCN Special Care Nursery

82 NRS Northside Satellite Dialysis Unit

83 2A 23 hour recovery

83 2N Mental Health

83 3S Maternity

83 4E Surgical

83 4W Orthopaedic

83 5E Medical

83 5W Medical

83 CAB Aged Care Assessment Unit (ED)

83 CCU Coronary Care

83 CDU Clinical Decision Unit (ED)

83 CVL ACT Convalescent Unit

83 DC Day Care Unit

83 DS Delivery Suite

83 EDA Emergency Department Admission Ward

83 EDO Emergency Observation Ward

83 HH Hospital in the Home

83 HP Hospice

83 ICU Intensive Care & Intensive Care stepdown

83 NQ Special Care Nursery

83 NU Neonates on the post-natal ward

83 PEN Endoscopy Unit

83 TW Temp Ward (Public Patients admit to private hosp)

83 VAW Veterans Affairs Ward (within 5E)

83 ZM Oncology Ward



Appendix 5

OPSN Analysis

Steps for selecting Denominator

1. Combine All years (2001-2006) data selecting fields: hospital, stay number,

Age, LOS, MDC, separation mode, AR-DRG, same-day field.

2. Link to study hospitals and select only hospitals with nursedata = 1.

3. Run delete query (denomselect.sql) to exclude cases which:

a. Have MDC = 14,15,19 or 20

b. Are paediatrics (ie age <18)

c. Have LOS < 1 day

d. Have LOS > 90 days

e. Have DRG = Inappropriate diagnosis (ie. 961Z, 962Z, 963Z)

4. Add Med/Surg field

5. Update Med/Surg field where second character of AR-DRG:

a. 6 and above = medical

b. below 6 = surgical.

6. Get final denominators by running query “finaldenom” – groupby year and

med/surg, and count. This is in Adverse.mdb database

7. Final denominators have been calculated and added (overwritten old

previous denominators) to the AdverseResults.xls

should be cases 4,in DENOMSALL

should be cases in DENOMS Study Hospitals

Mini notes: Make DENOMSALL – then copy for numerator, then delete irrelevant fields from denoms all.. then make a copy for Denoms Study hosps.

Steps for selecting Numerators

1. Use table NurseworkAdverse (ie.denomintors)

2. Run queries (nGroup1…nGroup11, and Failure to Rescue) to select initial

adverse and flag Group for type of adverse (1 or 0)


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3. Do failure to rescue now – before any conditions/restrictions are made.

4. Run conditional queries to un-mark any adverse events which meet

conditions (nGroup1conditions…nGroup11conditions)

5. Add “AdEps” Field and sum groups (incl failure to rescue) to get total

adverse count for the patient record.

6. Delete any records with AdEps=0

7. This leaves the table of only adverse events, now called “Adversework” (has

220192 cases)

8. Produce final results by group-by FinYear, Med/Surg, and summing over

Group1-11, failure to rescue and AdEps.

9. Final results are found in tables in excel sheet UpdatedDataAdverse.xls

Notes:

There are about 1000 SD fields not marked up. Change after adverse work is complete.

Denominator Criteria:

1. From NSWV51 (years 2000 – 2006) data:

2. Exclude cases which:

a. Have MDC = 14,15,19 or 20

b. Are paediatrics (ie age <18)

c. Have LOS < 1 day

d. Have LOS > 90 days

e. Have DRG = Inappropriate diagnosis (ie. 961Z)

Failure to Rescue (numerator) Criteria:

Patients who died (sepmode = 08) AND had either sepsis (Group #7),

pneumonia (Group #3), GI bleeding (Group #5), or Shock (Group #8).

Notes: Select from denominators all records with sepmode = 08 and make table called failure to rescue. Then re-run queries for select Group 7, Group 3, Group 5 and Group 8. Then delete those not involved and count per year and overall.



Appendix 6

Staffing of the Study Wards

FIGURE 12 WARD 1AA

FIGURE 13 WARD 1AB


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FIGURE 14 WARD 1AD

FIGURE 15 WARDS 1AF & 1AI*

* Note that these data were combined from 2 wards in order to retain reasonable stability in the time series, so should be viewed with caution.



FIGURE 16 WARD 1AG

FIGURE 17 WARD 1AH


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FIGURE 18 WARD 1AK

FIGURE 19 WARD 1AL



FIGURE 20 WARD 1AM

FIGURE 21 WARD 1AO


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FIGURE 22 WARD 2AC

FIGURE 23 WARD 2AE



FIGURE 24 WARD 2AJ

FIGURE 25 WARD 2AN


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Appendix 7

Instruments for Cross-sectional Component




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Broadway NSW 2007

Australia

Documents

GE. DO UTS: O · GE. DO. MARCH 2009 NURSING WORKLOAD ... IMPACT ON PATIENTS T AND STAFF UTS: O. NURSING WORKLOAD ... The focus of this study was on medical and surgical nursing wards