Workforce Management Objective A: Workload Measurement …/media/cna/page-content/pdf-fr/workload... · Workforce Management Objective A: Workload Measurement Tools EXECUTIVE SUMMARY

Workforce Management Objective A: Workload Measurement Tools

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PROPOSITION À L’ MANDATIONS DU COMITÉ CONSULTATIF CANA EN SUR LES SOINS INFIRMIERS

COLLABORATEURS

FÉDÉRA N INF RMIERS

ASSOCIATION DES INFIRMIÈRES ET INF IERS PSYCHIATRIQUES DU CANADA

NURSING WORKLOAD MEASUREMENT TOOLS

Authors:

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Mary Flannery

THE R RILY REFLECT THOSE OF THE COLLABORATING ORGANIZATIONS OR THE PROJECT STEERING COMMITTEE.

APPUI DUPLAN STRATÉGIQUE DE MISE EN ŒUVRE DES RECOMDI

ACADEMY OF CHIEF EXECUTIVE NURSES ASSOCIATION CANADIENNE DES ÉCOLES DE SCIENCES INFIRMIÈRES

TIO CANADIENNE DES SYNDICATS D’INFIRMIÈRES ET D’ IASSOCIATION CANADIENNE DES SOINS DE SANTÉ

ASSOCIATION DES INFIRMIÈRES ET INFIRMIERS DU CANADA ASSOCIATION DES INFIRMIÈRES ET INFIRMIERS AUXILIAIRES DU CANADA

IRM

WORKFORCE MANAGEMENT OBJECTIVE A

ASSESS USE, COMPLIANCE AND EFFICACY

Fran Hadley

Kathleen Graha

ECOMMENDATIONS CONTAINED IN THIS REPORT ARE THOSE OF THE AUTHORS AND DO NOT NECESSA

31 March 2004 Page 1


© Copyright 2005 Canadian Nurses Association Canadian Nurses Association

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Ottawa, Ontario, Canada K2P 1E2



EXECUTIVE SUMMARY

The Canadian Nursing Advisory Committee, (CNAC) report Our Health, Our Future: Creating Quality Workplaces for Canadian Nurses, listed recommendations to create healthy workplace environments for nurses. To address selected recommendations, a collaborative working group funded through Health Canada, directed a number of projects including the Nursing Workload Measurement system (WMS) project.

“Nursing workload” is defined as the amount of care allocated to patients based on an assessment of their nursing needs and the care they require. WMS tools provide a system for collecting the specifics of each patient’s care needs and the standard times required to complete the care in relation to the available staff time. Data generated by WMS provide managers and clinicians with information to support decision-making.

The project objectives were

to assess utilization, compliance, and efficacy of WMS tools for registered nurses, (RNs), licensed/registered practical nurses, (LPNs) and registered psychiatric nurses, (RPsychNs) across hospital and community health care settings, and

to identify elements of effective WMS tools for nurses.

A pan-Canadian approach was selected to compile this Workload Measurement System tool (WMS) report. The research methods included an extensive literature search and review, key informant interviews, a web-based survey of front line staff and managers and focus groups with front line staff.

Findings from the survey, focus groups and key informant interviews were consistent with the information gathered through the literature review. Participants repeatedly reported dissatisfaction with WMS tools and believe that the current WMS tools are outdated and do not reflect:

changes in hospital-based health care,

care needs of the evolving patient profile,

the increased proportion of higher acuity in in-patients, and

adequate tracking and measures of complexity, multi-tasking, and staff-mix elements of the nurses’ work environment.

From the assessment of utilization of WMS tools, the conclusions are as follows.

WMS tools were mainly used in the acute care hospital sector with the greatest concentration in teaching hospitals.

Limited initial and on-going orientation negatively impacted understanding and use of the tool.

Licensed Practical Nurses reported not using WMS tools but expressed a desire to actively participate and be involved in the input of their own data.

From the assessment of compliance of WMS tools, the conclusions are as follows.



The single strongest reoccurring theme was inadequate time or no time to

complete the tool.

There is strong support for online recording of WMS data from electronic records in real time at the point of care. Critical that WMS tools be integrated into nurses’ usual work (assessment/planning/interventions/diagnostics/evaluation).

The lack of technology and the costs of acquiring hardware and software and providing training were seen as significant barriers.

Nurses not involved in the development of the WMS tools were less compliant, had no ownership of the tool and did not feel it reflected their practice.

Greater levels of orientation and ongoing support yielded greater understanding of the specific tool, the purpose and potential benefits of WMS.

Nurses were less likely to comply with accurate recording of data and more likely to manipulate the data to their advantage if they had had negative experiences (especially true where WMS data had been used to reduce staffing or where the nature of the tools and their organizations was punitive).

The perceptions of managers and staff may vary and suggest the need for further study. The variation may be a function of their experience with and use of WMS. Managers may overestimate compliance, validity and reliability of the results. Staff information suggests that there may be significant issues with completion (time, access to computers, understanding and inclination to select particular scores or values).

From the assessment of the efficacy of WMS tools, the conclusions are as follows.

Efficacy was the least well understood and discussed element.

Most respondents understood that the purpose of a WMS tool is for mandatory reporting and for staffing decisions. They were unable to provide examples where the tool was used to support staffing decisions, other than examples of staffing reductions and efficiencies that were not perceived to enhance care or quality of work life.

Misunderstanding was common related to concepts such as validity and reliability, sampling, distribution and probability. There was limited discussion and understanding of WMS tools.

A lack of clear language and commonly accepted definitions was identified as a barrier.

No benefit was identified for patient care or staffing but there was a belief that if the tool was to be effective it must be unit-specific.

All groups reported greater acceptance with retrospective tools than with prospective tools. While some recognized the limitations with documenting assessments and tasks that were done versus what should have been done, retrospective WMS tools were clearly preferred.



Front line nurses did not see the report(s) or identify any subsequent results or

consequences. First level managers said they did not see meaningful reports and that the information seldom resulted in needed staffing additions.

The critical elements of an effective WMS tool as identified in the findings of this project are clustered in two main areas.

Human Resource Elements, including:

• Adequate initial and on-going orientation;

• Adequate scheduled time to complete the tool;

• Include all nurses (RN, RPsychN, LPN);

• Prompt, easily understood summaries and reports shared with staff;

• Clear, timely responses to results (staffing adjusted);

• Support for dedicated staff and education resources for chart development/updating/validation; and

• Include all elements of clinical nursing practice (not just tasks).

Information Technology Elements, which include:

• Adequate access to computers;

• Recording and reporting in real time;

• Reporting at point of care (e.g., personal digital assistant);

• Electronic (e-Health Record with an imbedded WMS tool); and

• Resource support for computer use (e.g., replace keyboards with touch screens, timely resolution of technical problems, etc.).



TABLE OF CONTENTS WORKFORCE MANAGEMENT OBJECTIVE A ................................................................................. 1 EXECUTIVE SUMMARY................................................................................................................. 3 TABLE OF CONTENTS ...................................................................................................................... 6 1 INTRODUCTION....................................................................................................................... 7 2 APPROACH AND METHODOLOGY........................................................................................ 8 3 CONSTRAINTS OR LIMITATIONS........................................................................................... 11 4 SUMMARY OF FINDINGS...................................................................................................... 12 5 ANALYSIS .............................................................................................................................. 33 6 CONCLUSIONS ..................................................................................................................... 39 7 RECOMMENDATIONS........................................................................................................... 42 APPENDIX A - LITERATURE REVIEW .............................................................................................. 44 APPENDIX B – WEB-BASED SURVEY DEMOGRAPHIC AND SURVEY QUESTIONS...................... 68 APPENDIX C – WEB-BASED SURVEY NOTICE OF INTENT, INTRODUCTORY LETTER.................... 72 APPENDIX E – WEB-BASED SURVEY DATA TOOLS....................................................................... 75 APPENDIX F – FOCUS GROUP DATA TABLE ................................................................................ 88 APPENDIX G – ABBREVIATIONS .................................................................................................. 92 APPENDIX H – BIBLIOGRAPHY ..................................................................................................... 94



1 Introduction

1.1 Context

Financial constraints, nursing workload/overload, accountability, clinical decision-making and reporting are driving the need to address workload measurement issues in the Canadian nursing workforce. “Nursing workload” is defined as the amount of care allocated to patients based on an assessment of their nursing needs and the care they require. Workload Measurement System (WMS) tools help collect the specifics of each patient’s care needs and the standard times required to complete the care in relation to the available staff time. Data generated by WMS provide managers and clinicians with information to support decision-making. Automated clinical- decision support is still evolving; most Canadian health care organizations do not use computer-based WMS.

The Steering Committee selected a mixed method of online survey, focus group and key informant strategies for this project. Nurse managers and front-line nurses across hospital and community health care settings were the target groups for information-gathering activities. Nurses and students in all areas of practice (clinical, education, management and research) across the country were invited to participate in various activities. Results of the literature review, surveys, focus groups and data analysis are reported in this document on nursing WMS tools.

1.2 Project Objective

As Project Manager for this collaborative study, the Canadian Nurses Association contracted Hadley Health Administration Services Ltd. (HHAS) to undertake the Workload Measurement System (WMS) tool project. The project goal was to assess use, compliance and efficacy of WMS tools for registered nurses (RNs), licensed/registered practical nurses (LPNs) and registered psychiatric nurses (RPNs) across hospital and community health care settings.

1.3 Definitions

1.3.1 Workload Measurement System Tools

A nursing Workload Measurement System (WMS) tool determines, validates and monitors individual patient-care needs over time. WMS tools provide a system for collecting specific data about each patient’s needs and the standard times required to complete the care. The tool helps nurse managers decide where to allocate nurses.

1.3.2 Health Care Providers: RN/LPN/RPN

Nursing includes three regulated occupational groups that work in a variety of roles and organizations across the continuum of health services. In this report, the following



abbreviated definitions are used to describe these nursing groups, as well as to define nurse managers:

RNs are licensed/registered nurses who practise within their province or territory. An RN uses the nursing process of assessment, diagnosis, planning, implementation and evaluation to serve persons of all ages and at varying levels of illness or wellness. They practise in a variety of settings, including hospitals, long-term care facilities, the community, etc. (CNA - http://www.cna-nurses.ca)

LPNs, known as “registered practical nurses” in Ontario, are licensed/registered practical nurses who assess and treat health conditions, promote health, prevent illness, and help individuals, families and groups achieve an optimal state of health. (CPNA, 2001 - http://web2.gov.mb.ca/laws/statutes)

RPNs are licensed/registered psychiatric nurses who are regulated as a distinct category in British Columbia, Alberta, Saskatchewan and Manitoba. RPNs use a holistic approach to provide psychiatric nursing services to individuals, groups, families and communities whose primary care needs are related to mental and developmental health. (RPNC - http://www.crpnm.mb.ca/profile.pdf )

For this survey, nurse managers are nurses in a first-level administrative position who manage staff providing direct nursing care.

2 APPROACH AND METHODOLOGY 2.1 Approach Using an inclusive, pan-Canadian approach, the study sought input from all three regulated occupational groups of nurses—RNs, LPNs and RPNs—who were in staff nurse and nurse manager positions, as well as from key informant nursing leaders. Research included an extensive literature search and review, interviews, a web-based survey and focus groups. In collaboration with the Canadian Nursing Informatics Association (CNIA) and others, and in keeping with the directions of the Steering Committee, the study addressed the following themes:

existence of workload measurement tools;

format of the tool (paper or electronic);

nature of the tool (prospective or retrospective);

easier ways to complete the tool;

perceptions of tool’s validity;

date of tool’s last update/revision;

dissemination (internal and external) and use of workload measurement data;

completion/compliance rate of the tool;

how the data are used to support decision-making;



any other factors/information used to supplement workload management

decisions;

perceptions of the data’s usefulness; and

existence of and frequency of use of mechanisms to validate quality of data.

2.2 Methodology

2.2.1 Literature Search and Review Method

A literature search and review informed the survey, teleconference/interview and focus group activities. The body of literature related to health human resource is extensive; the most relevant 54 articles and reports were selected for in-depth review.

The literature review sources of information included: Cumulative Index Nursing and Allied Health Literature (CINAHL) database; online resources; Medline, Medscape; and eHealth Resource Centre Database. Key words for searching included workload measurement systems, patient classification systems, WMS tools, managing nursing resources, vendors of WMS and nursing informatics. These references were reviewed to identify existing WMS tools, how data are used to support management decisions, data gaps and other relevant issues. Highlights of the review are presented in the Summary of Findings section and the full review of the literature is in Appendix A.

2.2.2 Key Informants Interview Method

The Steering Committee, the project co-ordination team and the project consultants developed a list of possible key informants. Individuals were then selected from these sources, invited to participate and interviewed.

2.2.3 Survey Method

Web-Based Workload Measurement Surveys for Nurse Managers and Front-Line Staff

Two main groups of interest were identified for the survey, nurse managers and front-line registered nursing staff. Intended respondents included registered nurses (RNs), licensed practical nurses (LPNs) and registered psychiatric nurses (RPNs) from across the country in a wide variety of workplace settings. Recognizing the broad range of respondents, the survey was deliberately structured to evoke responses that were as inclusive and as clear as possible. The survey included issues related to demographics, as well as the key questions from the Steering Committee.

The Steering Committee reviewed the survey, which was then translated and posted on a dedicated website. Given the web-based format and need to compile and summarize the data, the questionnaire featured predominantly forced choices with several options to add comments in free-text form (Appendix B - Demographics for Nurse Managers and Nurses and Survey Questions).

An invitation to participate in the survey was mailed and sent via e-mail (Appendix C – Notice of Intent and Invitation). The potential respondents were invited to participate through direct mail or through the communication network of their respective



association. The following associations were approached to disseminate the study information and invitation to participate: Academy of Chief Executive Nurses, Canadian Association of Schools of Nursing, Canadian Federation of Nurses Unions, Canadian Health Care Association, Canadian Nurses Association, Canadian Practical Nurses Association, Canadian Public Health Association and Registered Psychiatric Nurses of Canada.

CNA hosted the website for all letters/surveys and compiled the survey data. To ensure optimal response rates, reminder letters and reminder e-mails were issued one week after the initial invitation. Survey results were provided in summary tables.

The electronic surveys were completed on a voluntary basis and respondents had the option to identify themselves. This approach created a convenience sample. The web-based survey response rate was more positive than expected and consequently, significantly more qualitative and quantitative data were collected than expected.

Demographic information was collected for all participants (type of nursing registration, type of work setting and province) to describe the sample and to support analysis of results by selected demographic elements.

2.2.4 Focus Group Methods

Focus groups held across the country between January and mid-March 2004 solicited qualitative feedback from front-line nurses. Generally, about six to eight people took part in each focus group, although some groups comprised individual interviews and up to 19 participants. To augment the robustness of the findings, the project consultants planned, implemented and reported on a greater number of focus groups than this assignment required. This increased the front-line staff results significantly.

Preliminary information describing the context of the discussion, as well as a brief outline of the topics to be reviewed, was circulated in advance to the participants. Discussions were taped, transcribed and categorized by themes to capture the richness of participants’ comments.

The focus groups included participants from all three regulated nursing occupational groups (RNs, LPNs and RPNs) and from across all health care sectors (hospitals, long-term care, community-based care, mental health, rehabilitation, etc.). The Atlantic, Central and Western regions were all represented. There were no focus groups for nurse managers.

The focus group questions were based on key questions developed by the Steering Committee, as well as the literature review and questions in the electronic survey. These questions were constructed around the following areas: level of orientation to the tool, understanding of the tool and its purpose, rank/rate, the use and impact of information collected, quality of the data/information, methods used to fill in information, perceived difference in care practice and barriers to using the tool (Appendix D).

To optimize efficiencies and to minimize nurse survey and focus group fatigue, the consultants piggybacked the focus group activity for this project onto other planned focus groups. This approach was acceptable as these two target groups shared homogeneous characteristics (from across Canada, three regulated nursing groups, broad section of health care sectors). The two studies were the WMS study and a national study on recruitment and retention in nursing. The participants expressed



satisfaction with this approach as it provided an opportunity to share their opinions on these timely topics.

3 CONSTRAINTS OR LIMITATIONS 3.1 Method Constraint Discussion findings from focus groups usually generate most of the questions for a survey. However, due to time constraints, focus groups and the survey ran at the same time. To minimize this constraint, the authors prepared questions by collecting and consolidating the key issues from the literature review, using discussions with key informants and drawing upon their own expertise.

3.2 Response Rate Constraint

Focus group participants frequently stated they did not use a WMS. While numerous opportunities to discuss WMS were available, these participants were unable to speak to certain key questions. This gap in the focus groups was also evident in the nurses’ survey; 35 of the 72 respondents stated they did not have or use a WMS. Information about WMS use obtained through the literature review supports these findings.

3.3 Specific Population Segment Constraint

The Steering Committee identified target response rates and specific pan-Canadian demographics to be included in the survey and focus group results. Regrettably, representation from the francophone nursing sector was limited. While several specific focus groups for francophone nurses were scheduled, three were cancelled due to competing union priorities and/or insufficient attendance. Sixty-four nurses responded in French to the French-language web-based survey. Their responses were integrated into the data collection report.

3.4 Sample Constraints

The web-based survey required some computer skills and access to Internet services. Responses were voluntary and based on mail solicitation. This created a convenience sample, which limited the external validity of the findings. While the total number of respondents exceeded Steering Committee targets, respondent characteristics such as province and type of workplace varied disproportionately. As a result, readers should be cautioned against generalizing the findings to the wider population of nurses across Canada. The same constraint applies to the focus group findings.



4 SUMMARY OF FINDINGS

4.1 Literature Review Key Findings

As early as 1937, nursing leaders concluded that a system was needed to objectively determine the number of care hours required for each patient according to a given category. However, this proposed system did not include a process to determine staffing requirements. Almost 67 years later, we are still unable to consistently describe and predict patient care needs and nursing workload effort.

In the 1950s, studies and investigations focussed on classifying patients according to medical needs and nursing care requirements.

In the 1960s, social medicine continued to grow, forcing administrators to evaluate cost effectiveness. This led to renewed awareness of the need for a patient classification system.

Throughout the 1970s, resources were scarce relative to increasing demands. Thus the demand for effective WMS tools became a priority. Three major issues hindered credible data collection: inability to capture high-level, cognitive portions of nursing work; impact of multi-tasking; and caregiver variability.

In the 1980s, with the introduction of the Management Information System (MIS) Guidelines, hospitals established WMS committees, allocated resources and measured nursing resource intensity to predict nursing staff requirements. For the most part, the systems were paper-based and labour intensive. The information was predominantly used to limit staffing resources. Documented patterns of unmet patient-care needs were required before staffing increases would be addressed.

In the early 1990s, nursing faced large-scale layoffs that resulted in increased workloads. Consequently, as reported staff shortages based on WMS often did not result in staffing increases, nurses became less enthusiastic about recording workload data. Overworked nurses found themselves in a vicious cycle. They lacked time to complete the documentation, which produced data that did not substantiate the qualitative anecdotal reports. They feared losing already insufficient resources if the documentation did not support the allocation. Compounding this situation was the reduction in dedicated resources to coordinate, monitor, and evaluate the data collection. This furthered the scepticism of staff and managers about the validity and ultimate fairness of WMS.

The evolution of Patient Classification Systems (PCS) in the United States was initially similar to the development and implementation of WMS in Canada. In the 1980s, however, the introduction of managed care in the US began to transform the American health care system into a more business-focussed model. American health care organizations invested heavily in informatics and adopted new technologies to support and facilitate evidence-based decisions and support billings in a for-profit dominated delivery model. This resulted in American PCS/WMS that could monitor productivity, quality performance, staffing and compliance with regulatory agencies.

In the last decade, senior Canadian stakeholders recognized the need for information and began to address required systems to support decision-making in all health care



environments. Momentum in all levels of Canadian government toward this goal is evident in the plethora of research literature describing an increasing level of Information and Communications Technologies (ICT) activity. The Federal/Provincial/Territorial Tactical Plan for a pan-Canadian health infostructure is one example of how stakeholder groups tried to further the quality and sustainability of the Canadian health care system.

4.1.1 Existence of WMS and tools

There is a clear difference in the focus of the Canadian and American literature on WMS. The mixed private and public funding arrangement for American health care demanded systems and tools to track resource use. In response, multiple information-gathering systems, including WMS, were developed. The momentum to meet this information need is reflected in the extensive volume of American literature reviewing WMS and tools. For its part, the Canadian literature from the early 1990s was prolific in WMS research and descriptions on new WMS tools. Fiscal constraints and health care restructuring in the second half of the 1990s in Canada limited the availability of resources; this negatively affected support for existing WMS tools, as well as for significant further development of WMS tools.

Information on use of WMS and tools in Canada is limited; however, researchers in British Columbia reported on a tool used in conjunction with a WMS. An excellent example of a broad workload measurement tool, it measures the level of complexity, decision-making and clinical judgment required to meet patient-care needs and more accurately reflects nurses’ work. The tool, which objectively measures patient requirements, was designed to capture patient care requirements not identified by existing WMS (Fulton & Wilden, Victoria, BC 1998).

The literature frequently comments on the gaps/limitations in current WMS. The third generation Patient Classification Systems (3PCS) prototype moved from examining multiple discrete nursing tasks to a holistic perspective that includes both empirical and intuitive knowledge. Terminology varies on the subject with the Americans using the term PCS where Canadians would use WMS (Malloch K. et al., 1999).

A second documented set of limitations was the need for the tool to be specific to selected client groups. At the Rehabilitation Institute in Chicago, a prototype PCS assists with staffing decisions, monitors productivity, analyses trends, and supports the budgeting process. Sarnecki et al. report that the development of a specific classification instrument for patients undergoing rehabilitation and the resultant system have significantly enhanced the delivery of quality rehabilitative care (Sarnecki et al., 1998).

In a long-term care facility, researchers evaluated the relationship between the GRASP® WMS tool and the Resource Utilization Group Classification (RUGS III), both nursing workload projections tool. The findings suggested that nursing home managers could move to one standardized tool that measures self-care deficits and staffing resource needs (MDS and RUGS) (Adams-Wendling L., 2003).



4.1.2 Vendors

There are numerous vendors of WMS tools. Of the 19 reviewed, seven were selected for their relevance to this project. Consumer and expert opinions were reviewed from a variety of sources, including Internet bulletin board/discussion groups. The vendors’ efforts reflect a keen awareness of the critical issues from the clinicians’ perspective. The tools are consistently portrayed in the vendors’ marketing material as adaptable to any setting and staff mix. Details of reviewed WMS products are located in Appendix A.

4.2 Key Informants Interview Findings

The key informant group was comprised of administrators from across the country. Most were vice-presidents of community and teaching hospitals, while others were senior managers from regional health authorities including primary care. Four interviewees reported using GRASP® and one used Medicus. Their input was consistent with both the literature and with what the staff and managers reported in the web-based survey. However, the breadth of the interviewees’ comments reflected a more strategic orientation. Their comments focussed more on the health care system as a whole, which reflected a senior management perspective.

The current situation was succinctly summarized.

“The nurses working with the patients do not understand how WMS is used. Nor do nurses understand its relationship to their work and their resources. They see it as paperwork and not as a tool for them. But (they) see WMS as a tool used against them. Because of our history implementing WMS, they don’t like it or trust that action will be taken to address WMS issues.”

This group of interviewees shared a historical perspective related to the changes in workload. They referred to the need to identify workload changes and measure all aspects.

“We keep saying acuity level is so much higher… that higher levels of intervention are what is driving acuity. Technology has made our lives easier, improved technologies help the patient more, but technological advances that move less acute patient to out-patient and day-care treatments leave the more acute in hospital beds and these require more nursing hours. The current WMS tools are outdated and are not tracking the more complex care needs.”

The use and possible misuse of WMS was a concern.

“The WLM Tool can be used by the finance side of the table and it can be used against you, because it (WMS) doesn’t really have any way of validating the targets e.g., after hours (coverage requirements for a) minimum number of staff in terms of safety and scheduling (limitations). They will say there is no interrelated reliability between and among the tools. The government would never want us to have a tool that we could all document, and compare and contrast and put information together. They wouldn’t like the results (unmet needs, resource implications).”



4.3 Survey Findings

4.3.1 Survey Participant Profile

A total of 301 nurses responded to the web-based survey. This convenience sample was comprised of 72 staff nurses and 229 nurse managers. Total responses per question varied since not all respondents answered all questions. Both actual values and percentages have been reported in many of the tables. Variations in total percentages are due to rounding errors. Given the sampling, data from nurse managers and front-line nurses are consolidated in the body of the report. Manager and front-line data are provided separately in full sets of data tables found in Appendix E.

4.3.2 Location by Province

The largest response by province was from Alberta with 29 per cent of total respondents, followed by Ontario, British Columbia and New Brunswick.

Figure 4.3.2 Location by Province

Province Total %

Alberta 86 29%

Ontario 73 24%

British Columbia 36 12%

New Brunswick 26 9%

Nova Scotia 21 7%

Manitoba 20 7%

Saskatchewan 20 7%

Newfoundland/Labrador 7 2%

Territories 4 1%

Prince Edward Island 4 1%

Yukon 3 1%

Quebec 1 0%

Grand Total 301 100%

4.3.3 Area of Employment

Sixty-two percent of respondents identified their area of employment as either a teaching or community hospital.



Figure 4.3.3 Area of Employment

Area of Employment Total %

Teaching Hospital 128 43%

Community Hospital 57 19%

Regional Health Authority 25 8%

Long-Term Care 22 7%

Home Care 16 5%

Public Health 14 5%

Community Health Centres 10 3%

Mental Health 8 3%

Other 6 2%

Rehabilitation 3 1%

Community Rehabilitation 3 1%

Community Mental Health 3 1%


4.3.4 Professional Designation

The sample was comprised of registered nurses (97 per cent or 281), registered psychiatric nurses (three per cent or 8) and licensed practical nurses or registered practical nurses (one per cent or 2).

4.3.5 Position Title

Two-hundred-and-seventy respondents selected a position title. The top three responses comprised 67 per cent of all respondents: nurse managers (28 per cent), program managers (20 per cent) and patient-care managers (19 per cent). Staff nurses formed 16 per cent of the sample with team leaders and clinical nurse specialists adding six per cent and two per cent respectively to the total.



Figure 4.3.5 Position Title

Position Title Total %

Clinical Nurse Specialist 6 2%

Case Manager 36 13%

Nurse Manager 76 28%

Patient Care Manager 51 19%

Program Manager 55 20%

Staff Nurse 43 16%

Team Leader 8 3%


4.3.6 Years in Current Title

Sixty-five percent of respondents had held their title for more than four years, 42 per cent for six years or more, and 23 per cent from four to six years. Twelve percent had held their title for less than one year.

Figure 4.3.6 Years in Current Title

Years in Current Title Total %

6 plus 124 42%

4 to 6 69 23%

1 to 3 65 22%

Less than 1 36 12%

Grand Total 294 99%

4.3.7 Age

Sixty-nine per cent of respondents reported they were over 40 years old. Five per cent were 30 years old or younger.



Figure 4.3.7 Age

Age in Years Total %

51-55 75 26%

46-50 73 25%

41-45 52 18%

36-40 33 11%

56-60 26 9%

31-35 17 6%

26-30 11 4%

61+ 5 2%

0-25 2 1%


4.4 Survey Results

Readers should be cautious about making any general conclusions. Due to limited sample size and convenience sampling, the statistical difference between subgroups of respondents—those with and without WMS or managers and staff nurses—could not be compared. The Canadian Nurses Association Highlights of 2002 Nursing Statistics provides a profile of the population of interest prepared by the Policy Health Division in September 2003 (Canadian Institute for Health Information (CIHI), Workforce Trends of Registered Nurses in Canada, 2002).

The authors deliberately chose to retain incomplete questionnaires as a way to respect the time and effort of those who did participate, as well as to increase the response rate per question. Since not all respondents chose to answer all questions, the total number of responses per question varies. The complete results of the survey items in table form have been included for reference (Appendix E).

4.4.1 Organizations with and without WMS One hundred and-sixty-six respondents (56.3 per cent) indicated they had a nursing workload measurement system while 129 (43.7 per cent) did not have a WMS. Of those with WMS, 62.7 per cent were employed in hospitals, 8.4 per cent in regional health authorities, 7.4 per cent in long-term care, and 5.4 per cent in home care.



Figure 4.4.1 Organizations with and without WMS

WMS Tool Yes No

Teaching Hospital 91 37

Community Hospital 32 25

Regional Health Authority 12 13

Long-Term Care 8 14

Home Care 6 10

Public Health 4 4

Community Health Centre 4 6

Mental Health 3 3

Other 3 11

Community Rehabilitation 2 1

Community Mental Health 1 2

Rehabilitation 0 3

Total by Yes / No 166 129

4.4.2 Type of WMS Tools

GRASP® and QuadraMed/Medicus accounted for almost 70 per cent of the total. Slightly more tools were electronic (89 or 53.6 per cent) than paper-based (77 or 46.4 per cent). Most were retrospective in nature (87 or 53.7 per cent), while 24 per cent (39) were prospective and 22.2 per cent (36) were almost equally balanced between retrospective and prospective tools in their respective organizations.

Figure 4.4.2 Tool Type

Tool Type Total %

GRASP 65 39.9%

QuadraMed/Medicus 49 30.1%

In-House Tool 22 13.5%

NISS 14 8.6%

Other Commercial 12 7.4%

PRN 1 0.6%

Grand Total 163 100.0%



4.4.3 Areas Using WMS Tools

Of the 160 respondents who answered the question about where the tools were used in their organizations, 52.5 per cent (84) indicated their organizations used the tool in most areas, 35 per cent (56) in all areas and 12.5 per cent (20) in some areas.

4.4.4 Validity and Reliability of WMS

Of the 161 respondents who answered the question about how often they validated WMS, 44.1 per cent (71) reported doing so within the last year, 34.2 per cent (55) more than two years ago, and 21.7 per cent (35) in the past two years.

Of the 162 respondents who answered the question about whether the tools were used in accordance with agency protocols, 58.6 per cent (95) said “most of the time,” 24.1 per cent (39) said “all of the time” and 17.3 per cent (28) said “some of the time.”

Of the 165 respondents who answered the question about staff compliance with completing each tool, 46.7 per cent (77) called compliance “good,” 44.9 per cent (74) called it “fair,” while 8.5 per cent (14) called it “poor.”

• Of the 165 respondents who answered the question about the perceptions of the WMS validity, 42.4 per cent (70) said it was “basically valid,” 30.9 per cent (51) said it was “somewhat valid,” 15.8 per cent (26) said it was “not valid” and 10.9 per cent (18) said it was “very valid.”

With the methodological limitations in the data identified, there may be an interesting difference in responses between managers and staff that is beyond the scope of this report. Future studies might ask about the perceptions of managers and staff with respect to staff compliance with following policy, completing the tool, and self-report of WMS validity. Given limitations of the sample, this report draws no conclusions about the responses between managers and staff.

4.4.5 WMS Use

WMS reports are routinely provided in a variety of frequencies; respondents could select any of the options. The selections in descending order were: monthly (72 or 48.7 per cent), quarterly (33 or 22.3 per cent), daily (31 or 21 per cent) and weekly (12 or 8.1 per cent).

Data from the WMS reports are routinely used to support a range of decisions. Again, respondents could select as many options as they wished. Both the manager and staff groups selected the same items in the same rank order.



Figure 4.4.5 How Results Used

How Used Total %

Meet Mandatory Requirements 61 43.3%

Forecast Staffing 28 19.9%

Analyse Benchmark 18 12.8%

Compare And Adjust 14 9.9%

Monitor And Adjust 13 9.2%

Model Scenarios 7 5.0%


4.4.6 WMS Support

Support to develop, audit, compile and consults on the tools and results were reported as “generally sufficient” by 39 per cent of the nurses (62), “limited” by 37.7 per cent (60) and “not really available” by 23.3 per cent of respondents (37).

Almost 40 per cent (62 or 39.5) reported that inadequate resources were provided for software and hardware to support WMS, while 31.9 per cent (50) reported resources somewhat adequate and 28.7 per cent (45) reported adequate resources.

There was a nearly equal split on responses related to dedicated resources for staff orientation and education on WMS.

Figure 4.4.6 Dedicated Resources

Dedicated Resources Total %

Yes 53 33.1%

Some 53 33.1%

Not Really 54 33.8%


4.4.7 Suggestions

An open-ended question invited respondents to suggest ways to make WMS tool completion easier. The themes from the suggestions are summarized in Table 4.4.7. The additional comments are provided in 4.4.7.A. Selected excerpts are provided in section 4.4.7.



Figure 4.4.7 Suggestions

Suggestions Manager and Nurse Frequency

Staff Nurse Frequency

Manager Frequency

Electronic/have better computer access

8 2 6

Real time online input 4 2 2

No reports received 4 4

Clerical staff to input data 2 1 1

Adjust for more mature staff, language barriers

1 1

Do retrospective only 1 1 1

Standardize for typical patients to make completion easier

2 2

Lack of time/time consuming/waste of time

9 5 4

Useless–no money to reduce workload.

8 2 6

Everyone use same tool to make comparisons; include outpatients

3 2 1

Figure 4.4.7.A Other Comments

Other Comments Frequency

Favourable comments about having electronic point-of-care access

4 managers

Favourable staffing results with WMS 1 manager

Did standardize for typical patients and recommended (ER)

1 manager

Has helped situation 1 manager

Too task-focussed, no recognition of judgment

1 staff

Too specialized to be comparable e.g., maternity, ICU

1 staff

2 managers

4.4.8 Selected Excerpts

The current reality reflecting a unit/departmental focus



“Workload measurement is not a valuable tool for nurse managers or staff; changes in patient acuity/census fluctuate quicker than workload can be calculated, especially in emergency room. In small facilities, workload is shared by pulling from another area, and this often does not reflect in workload. If there is only one person in ER all night, what would that workload tell you for tomorrow?”

The competing priorities and tension of managers

“I perform the duties of the front-line management position of the [identifiers removed to protect anonymity] unit, and when we are short of staff, which is occurring with more and more regularity, especially in the rural sector, I am working as a staff nurse on the unit. This then leaves very little time to be a manager/leader or to do the duties of a manager/leader. When I speak with other nursing managers/nursing leaders, they have much the same story to tell.”

A visionary seeking balance

“Computerized system and program with Palm Pilots, so that the data collection is done as soon as the care/intervention is completed, and then the program does the necessary tabulations to create a report on a daily basis. Only this way will we have true measurements to help us improve. However, healthy work life is more than measurements.”

4.5 Focus Group Findings

For ease of reading, the following figures contain only pertinent data related to key areas of interest. A full set of data tables is found in Appendix F.

4.5.1 Focus Group Participant Profile

Fifteen focus groups were conducted across Canada with RN, LPN and RPN groups representing all health care sectors (hospitals, long-term care, community-based care, mental health, rehabilitation, etc.). The 101 participants were comprised of 29 RNs, 55 LPNs, 16 RPNs and 1 with RN/RPN registration. Participants were asked to complete a profile sheet. The total responses per question varied since not all respondents answered all questions. Actual values and/or percentages were reported in many of the tables. Variations in total percentages are due to rounding errors.

4.5.2 Location by Region

More than half of the participants (50) came from the Atlantic provinces followed by the western provinces (33) and central Canada (18). Findings from a number of planned focus groups could not be included as there was insufficient attendance and/or the invited participants had no experience with WMS tools.



Figure 4.5.2 Participant Profile by Region

Region Western Central Atlantic Totals

Registered Nurse 7 14 8 29

Licensed Ppractical Nurse

9 4 42 55

Registered Ppsychiatric Nnurse

17 17

Total 33 18 50 101

4.5.3 Area of Employment

Nurses from nine sectors of nursing work environments participated. The long-term care sector had the highest representation, comprising 27 per cent of all focus group participants. Twenty-one per cent were from teaching hospitals, 15 per cent from community hospitals and 13 per cent from regional health authorities. Seven per cent were from forensic/correctional institutional services, with the remaining 17 per cent equally distributed among community health/health centres, home care, rehabilitation, mental health, nursing station and other sectors.

LPNs represented the largest proportion of participants. In the area of employment, 38 per cent of LPNs worked in long-term care. This percentage is similar to the Canada-wide statistics that report 36.4 per cent of LPNs work in long-term care/nursing homes. (CIHI, 2003)

Figure 4.5.3 Count of Setting Employed

Employed as RN LPN RPN Grand Total

Long-Term Care/Nursing Home

3 21 2 26

Teaching Hospital 8 10 2 20

Community Hospital 4 7 3 14

Regional Health Authorities

1 12 13

Forensic Services/Correctional Institution

1 1 5 7

Community Health/Health Centre

3 1 4

Home Care 2 1 3

Mental Health Centre 3 3

Rehabilitation 3 3

Nursing Station 1 1



Employed as RN LPN RPN Grand Total

Other 2 2

Grand Total 24 55 17 96

4.5.4 Employment Sector by Region

The Atlantic provinces had the single largest group of participants, as well as the single largest group for one sector, representing 73 per cent of participants working in long-term care. The Western provinces represented 40 per cent of participants from the hospital sector. Representation for forensic service/correctional and mental health services was mainly in the western provinces (80 per cent); all were RPNs.

Figure 4.5.4 Employment Sector by Region

Count of Region by Sector Western Central Atlantic Grand Total

Long-term Care/Nursing Home 5 2 19 26


District Regional Health 2 11 13


Forensic Services/Correctional Ins 5 2 7

Community Health/ Health Centre 2 1 1 4

Home Care 1 1 1 3

Rehabilitation 3 3


Other 1 1 2

Nursing Station 1 1


4.5.5 Education Level

LPNs had either a certificate (39) or diploma (16). Of the 29 RNs, 15 had either a diploma or certificate, five reported having a post-registered nurse diploma, five had undergraduate degrees in nursing, two were nurse practitioners and two had baccalaureates in science of mental health. In the final group, 17 RPNs reported having a general diploma.



Figure 4.5.5 Count of Education Level

Education Total

Licensed/RPN Certificate 39

Licensed/RPN Diploma 16

RPN – General Diploma 17

Registered Nurse Diploma 9

Certificates 6

Post-Registered Nurse Diploma 5

Bac. in Nursing 5

Nurse Practitioner 2

Bac. in Science of Mental Health 2

Grand Total 101

4.5.6 Years of Nursing Experience

Number of years employed in nursing ranged from one to 43 years. Fifty-five per cent of participants reported between 20 to 40-plus years of experience, 27 per cent reported 10 to 20 years, 13 per cent had five to 10 years and five per cent had five years or fewer.

Figure 4.5.6 Years of Nursing Experience

Years of Nursing 0-5 6-10 11-20 21-30 31-40 41+

Count 5 12 24 30 19 2

4.5.7 Years of Nursing Experience by Sector

Nurses with one to 10 years of experience were found exclusively in the following sectors: long-term care (17 per cent), teaching hospitals (18 per cent), regional health authorities (40 per cent), community hospitals (23 per cent) and forensic services (14 per cent).

Certain care sectors had a significant number of nurses in the “11 to 30 years of experience” group. These sectors reported a larger proportion of nurses: long-term care (71 per cent), teaching hospitals (65 per cent), community health centres (67 per cent), regional health authorities (47 per cent) and community hospitals (38 per cent). Home care, rehabilitation, mental health centres and other had between 75-100 per cent within the “31 to 40 years of experience” group. The profile in the convenience sample may or may not reflect the profile of all nurses by sector.



Figure 4.5.7 Years of Nursing Experience by Sector

Years of Nursing Service 0-5 6-10 11-20 21-30 31-40 41+ Grand Total

Long-Term Care/Nursing Home

1 3 9 8 3 24

Teaching Hospital 3 5 6 7 17

District Regional Health 3 3 3 4 2 15

Community Hospital 1 2 2 3 5 13

Forensic Services/ Correctional Institution

1 2 2 1 1 7

Community Health / Health Centre

2 1 3

Home Care 1 2 1 4

Mental Health Centre 1 2 3

Rehabilitation 1 1 1 3

Nursing Station 1 1

Other 1 1 2

4.5.8 Employment Status

Seventy-six per cent (70) of participants who responded to the employment status question reported permanent full-time positions. Eighteen per cent (17) of participants were in permanent part-time positions and the rest (five per cent) were in casual and temporary part-time positions.



Figure 4.5.8 Count by Employment Status

Employment Status Total

Permanent Full-time 70

Permanent Part-time 17

Term Part-time contract 1

Casual 4

Grand Total 92

4.6 Focus Group Results

4.6.1 Focus Group Results by Region

For this report, individual comments were aggregated into statements that reflect the frequency and strengths of themes generated by participants. The focus group questions related to the following: orientation to the tool, understanding the tool, rating the tool, quality of data and barriers. For ease of review, the responses were aggregated into use, compliance and efficacy categories. The findings have been reported by region (West, Central and Atlantic).

West (British Columbia, Saskatchewan, Manitoba–RN/LPN/RPN)

British Columbia: RPNs

Use

• Believes the tool determines the appropriateness of the patient on the unit/determines acuity levels.

• Evaluates nurse’s ability to assess new admissions.

• Supports budget decisions related to amount of supplies, staffing, staff mix, etc.

• Suggests managers use the tool to reduce staff positions or demonstrate they were not working hard enough.

• RPNs in two focus groups report they don’t use a tool in their community/mental health settings.

Compliance

• Feels use of the tool is futile, as the data are not used.

• Feels the tool is a burden; they were barely getting patient care completed. They most often make patient care a priority over paperwork.

• Tool is redundant; we are duplicating, unnecessary repetitive reporting.

• Managers need education on how to use it. We get no support.

• It’s manual; we need an electronic way to enter data, but lack computers and physical space for computers.



• Have to do it, so am doing it.

• Don’t have enough time, do it quickly, whip through it as it means nothing, mimic what was checked off the previous day.

• In hospital, we input twice a shift and more frequently if it’s a 12-hour shift. This is especially true if you have frail elderly patients with many changes during your shift.

Efficacy

• Feel if they had a good tool 10 years ago, it would have identified the overall increases in caregiver workload.

• Difficult to assess the quality of the data, as we don’t know where the information goes or its use.

• Quality is compromised because of insufficient orientation.

• Automated staffing tool that identifies need for staff, but replaces with the least expensive staff type leads to a poor staff mix.

Saskatchewan: RNs

Use

• Tool is the Nursing Information System Saskatchewan (NISS), which is used in hospital sectors (acute—rural and urban) not in long-term care or community sectors. NISS needs updating. System was old and out of date, not user friendly, difficult to use by nurses who do not have computer and/or keyboarding skills. Believe the tool was used for patient assignments. They reported using the tool as a reminder and charted against it.

• Tool used for care planning. One rural hospital reported the tool was a legal part of the chart.

• Input divided between manual/automated. Keyboarding is a barrier, half done prospectively and the other retrospectively. Cannot comment on which is better as no results are evident.

Compliance

• Useless tool, not being used as it should for staffing. Waste of time.

• Have filled it out for 10 years and nothing happens/no changes noted.

• Not specific to our patients in mental health sector, as items are missing that reflect what we do. Staff in surgery unit report that tool does not fit their changing patient profiles. More and more off-service (medicine) patients are admitted to their surgical unit.

• Limited compliance to complete forms because it is time consuming and subject to interpretation. This constraint caused inconsistent data collection.

• Don’t know how data are used, as they don’t see any related reports. No one fills them out properly or at all. No repercussions if you don’t; managers encourage us to do it, but don’t follow up/enforce. Takes away time from the patient.

Efficacy



• No quality data, as the forms are not completed accurately. Information related

to staffing is not acted on. No consistency as tool is subject to interpretation.

Saskatchewan: LPNs

Use

• LPNs report use of tool in acute and rehabilitation hospital settings, but not throughout the hospital. Report only RNs’ input; done twice a day. Believe that the tool does not collect their workload data.

Compliance

• State they are frustrated as staff mix information is not included and therefore related decisions are inaccurate.

• Access to computers is/will be a problem.

Efficacy

• Do not see any changes because when the tool reports we need extra help, it doesn’t happen.

• Believe there is rigid adherence to the old patterns of staffing. Also not flexible enough to provide extra staff when major changes in demand occur.

• Tool needs to be updated to reflect the current patient profile.

Saskatchewan: RPNs

Use

• RPNs from the hospital sector report NISS is used in their health region. However, only the RNs input the data and this is done at the end of the day. Data input is by hand, but they started the Saskatchewan Health Information Network. They report this will automate data, improving input.

Compliance

• Frustrated because they believe the report of their workload is not included.

Efficacy

• Never see reports so cannot assess if the tool leads to changes.

Central: (Ontario/Quebec–RN/LPN)

Ontario/Quebec: RNs

Use

• Hospitals were only sector using WMS tools (Medicus and an in-house tool). They understand the tool is used for staffing and budgeting.

• Some community health centres use an “encounters”-based system that records activity, but their tool did not incorporate a time element.

• Other sectors, including public health and long-term care, use a system that tracks activity. However, these systems do not meet the definition of a WMS tool.



Compliance

• Participants use a manual system, view the tool as a burden. They believe managers underestimate staffing needs because of incomplete information generated by the tool. They report non-compliance related to insufficient time to complete the tool. At one centre, where the tool was computerized, the staff had sufficient time to input the data.

• Lack of time to complete the tool and weak keyboarding skills to record the data were barriers.

• Accessibility of computers was problematic in a tertiary hospital, whereas in a community hospital they had dedicated computers for WMS tools.

• Insufficient orientation and ongoing training affect the ease of using the tool.

• Lack of automated definitions and classifications is a barrier.

Efficacy

• Feel that managers responded to the information and would hire extra staff when warranted. Nurses who had used GRASP® noted that the customized WMS tool did not include certain elements such as patient teaching and emotional support. Consequently, nurses felt these critical components of care were devalued.

• Lack of clear definitions and insufficient orientation with respect to WMS compromised quality of data.

• Insufficient resources preclude regular validity and reliability audits. Previously this group had benefited from WMS support resources; presently the participants report these resources are inadequate.

Ontario: LPNs

Use

• At times, participants used the term ”GRASP” as a generic label to identify their WMS tools. They perceived the tool was used for staffing purposes. This group identified reliability and validity as a process of matching their work schedule and patient assignments to completed WMS forms.

Compliance

• Suspected the tool was vulnerable to manipulation because they believe it is highly subjective. Retrospective input of the data was the usual. Half the group identified accessibility to computers as a problem. However, the entire group was comfortable using computers.

Efficacy

• This group cannot comment on the quality of the data due to the lack of feedback or reports. As well, they could not identify changes that resulted from the WMS tool information.

East: (New Brunswick, Nova Scotia–RN/LPN)

New Brunswick: LPNs in Acute Care



Use

• Only RNs directly input data into the GRASP® WMS tool. Believe the tool is used for staffing and budgeting.

Compliance

• The RN records the data with no input from the LPNs. Even though this group does not record data directly into the WMS tool, they believe that keyboard skills, computer access, and orientation could all be barriers. Insufficient time to complete the tool causes the RNs to input the data after the end of their shift.

Efficacy

• Do not feel the tool is accurate as it does not record recurring tasks or additional time needed to care for confused patients. It neglects multi-tasking, an intrinsic part of nursing care. When it is determined that additional staff are required, managers most often hire additional RNs. The tool does not appear to support appropriate staff-mix decisions.

Nova Scotia: RNs

Use

• Only the hospital sector uses a WMS tool, and in this group’s work environment, they use GRASP®.

• Felt the tool attempted to apply science to measure the amount of nurses’ work and was focussed on staff requirements.

• Felt the data are used for accountability reports to the ministry. Some caregivers explained the charge nurse completed the WMS tool. Believe it could be used for staffing if the tool was unit-specific.

Compliance

• While one centre helped develop the WMS tool, the majority had little or no input and therefore did not value the system.

• Only three of eight participants have access to a computer; location of the computer was also problematic.

• Felt that nurses are not typists and this is a barrier to complying with automated systems.

Efficacy

• Strong feeling that when the tool is used prospectively, it is inaccurate because it is not updated throughout the shift. The tool does not have the ability to record the frequency of a given task.

• Tool is only used on weekdays, hence incomplete data compromise data quality. Believe that the tool needs frequent updating as unit services and patient care-profiles change.

Nova Scotia: LPNs

Use

• Only use WMS (GRASP®) in teaching hospitals.



• Only RNs directly input data into the WMS tool.

• Believe that RNs dislike WMS tool and that managers use the data to support compensation rates for RNs.

• Believe the tool is used to track work hours needed for patient care.

Compliance

• Lack of available computers would be a barrier if an electronic system were adopted.

• Reported that they do not see the information on their unit as only the charge nurse records data.

Efficacy

• Felt that, regardless of WMS reports about numbers of staff needed, managers did not respond.

• Believe the tool is highly subjective, which leads to inconsistencies and inaccuracies.

• Feel data would be more reliable if all caregivers were inputting the data.

5 ANALYSIS

5.1 Analysis of Web-Based Survey Participants

5.1.1 Organization Type by Province

The table below lists all respondents to describe the distribution by organization and provincial/territorial location. In descending order, the largest numbers of respondents were: Ontario teaching hospitals, Alberta teaching hospitals, Ontario community hospitals, British Columbia teaching hospitals, Manitoba teaching hospitals, followed by a tie between New Brunswick teaching hospitals and Alberta community hospitals.



Figure 5.1.1 Organization Type by Province

Organization Type By Province AB BC MB NB NL NS NT ON PE QC SK YT

Total Org’n

Teaching Hospital 40 12 11 10 1 9 42 3 128

Community Hospital 10 7 6 5 5 18 1 3 2 57

Regional Health Authority 9 4 1 2 2 2 1 4 25

Long-Term Care 8 4 3 1 1 1 1 3 22

Home Care 2 1 1 1 2 5 1 3 16

Public Health 2 3 2 1 3 1 1 1 14

Community Health Centre 1 1 5 1 1 1 10

Mental Health 3 1 1 1 1 1 8

Other 3 1 2 6

Community Rehabilitation 1 1 1 3

Community Mental Health 1 1 1 3

Rehabilitation 1 1 1 3

Total by Province 81 35 20 26 7 21 4 73 4 1 20 3 295

5.2 Analysis of Survey Results

The same caveats are offered as previously identified in Section 3 of this report for the survey results. Readers should be cautious about reaching any conclusions about statistical difference or generalizing the results. Due to limited sample size and convenience sampling, the authors could not calculate the statistical difference of results between subgroups of respondents e.g., those with and without WMS or by type of WMS tool. The Canadian Nurses Association Highlights of 2002 Nursing Statistics provides a profile of the population of interest and distributions of selected factors. The Policy Health Division prepared the report in September 2003 based on the 2002 CIHI report, Workforce Trend of Registered Nurses in Canada.

The authors deliberately chose to retain incomplete questionnaires as a way to respect the time and effort of those who did participate, as well as to increase the response rate per question. Since not all respondents chose to answer all questions, the total number of responses per question varies.

5.2.1 Organizations with/without WMS by Province/Territory

The results were tabulated to describe the existence of WMS by location (province/territory). In descending order, the highest percentage of respondents from



provinces with WMS was: Ontario (76.7 per cent), Alberta (69.8 per cent), New Brunswick (65.4 per cent), Saskatchewan (55 per cent) and Nova Scotia (52.4 per cent).

Figure 5.2.1 Organization With/Without WMS by Province

Have WMS AB BC MB NB NL NS NT ON PE QC SK YT Total

Yes 60 7 3 17 2 11 56 11 1 168 No 26 29 17 9 5 10 4 17 4 1 9 2 133 Total by Province

86 36 20 26 7 21 4 73 4 1 20 3 301

5.2.2 WMS Data Uses and Type of WMS Tool

Most commonly, the tool was used to meet mandatory reporting requirements; this held true for all types of tools.

Figure 5.2.2 WMS Data Uses and Type of WMS Tool

Tool and Uses

Analyse Benchmark

Compare and Adjust

Forecast Staffing

Mandatory Requirement

Model Scenarios

Monitor and Adjust

Total Tool Type

Grasp 4 2 9 28 4 6 53

Quadra Med

6 7 11 16 1 2 43

In-House Tool

1 2 3 7 2 3 18

NISS 3 1 1 4 2 11

Other Commercial

4 2 1 3 10

PRN 1 1

Total Type of Use

18 14 26 58 7 13 136

5.2.3 Perceived Validity of WMS Results and Compliance with Agency Protocols for Tool Completion

The single largest group of respondents reported its WMS was basically valid (69 or 42.9 per cent). Those who reported their tools were basically valid also most frequently reported compliance with agency protocols for tool completion most of the time (43).

The second largest group of respondents report described their WMS as “somewhat valid” (48 or 29.8 per cent), while 31 selected reported compliance with completion protocols “most of the time.”



Figure 5.2.3 Perceived Validity/Results and Compliance

Validity and Protocol Compliance All Time

Most Time

Some Time

Total Results Validity

%

Validity

Very Valid 9 8 1 18 11.18%

Basically Valid 21 43 5 69 42.86%

Somewhat Valid 7 31 10 48 29.81%

Not Valid 1 13 12 26 16.15%

Total Compliance 38 95 28 161

% Compliance 23.60% 59.01% 17.39%

5.2.4 Perceived Validity of WMS Results and Staff Compliance with Completing Each Tool/Chart

The single largest group of respondents reported that WMS was “basically valid” (70 or 42.7 per cent). Those who reported their tools were basically valid also reported that staff compliance with completing each tool or chart was “good” (35) or “fair” (34).

The second largest group of respondents reported that WMS was “somewhat valid” (50 or 30.5 per cent). Those who reported their tools were somewhat valid also reported that staff compliance with completing each tool or chart was “fair” (27), “good” (19), or “poor” (4).

Figure 5.2.4 Perceived Total Validity/Staff Compliance

Validity and Completion Good Fair Poor

Total Validity

% Completion

Very Valid 17 1 18 10.98%



Not Valid 5 12 9 26 15.85%

Total Compliance 76 74 14 164

% Validity 46.34% 45.12% 8.54%

5.3 Focus Groups Analyses

5.3.1 Focus Groups Analyses by Region

Due to the nature of qualitative data collected during focus groups, descriptive analyses follows. For ease of reading, the analyses have been reported by region (West, Central



and Atlantic). The analysis is structured around the three nursing occupational groups and types of health care sectors.

West (British Columbia, Saskatchewan, Manitoba–RN/LPN/RPN)

British Columbia: RPNs

Representation in this group included participants from long-term care, acute hospitals and psychiatric facilities. However, only the hospital sector had adopted a WMS tool.

• General impressions Participants varied in their understanding of the tool and its intended purpose. Compliance is affected by their perception that using the tool is futile and an administrative burden that reduces time with patients. Feel they need feedback, need to be involved in planning, and implementing. Managers need to be educated about the process and to support staff in implementation and training. Schedule time in the workday to complete the tool.

• The efficacy of the tool was directly affected because of the frequently reported inadequate staff orientation and the inability to use the tool to identify mental health or off-service patient care needs accurately.

Saskatchewan: RN/LPN/RPN

The RNs use NISS (The Nursing Information System Saskatchewan). However, the WMS tool has not been updated in 20 years. While new staff receives eight hours of orientation, older staff had limited orientation varying from one hour to a half day; the sessions were informal and often provided by peers on the unit. Again, initial and ongoing orientation were limited—key factors in the understanding and use of the tool.

• General impressions Would rather focus on patient care instead of completing more paperwork and would like to discard the tool. No value in the data; prospective/retrospective aspects are irrelevant. Do not think it influences their practice.

• Tool does not reflect patient care needs, hence the information generated is inaccurate, which compromises the efficacy.

LPN and RPNs stated they give their information to the RN at the end of the shift as they are not permitted to input data into the tool. They believe it is used for staffing. However, when the tool reports that extra staff is required, the request is not addressed. This group expressed a desire to be included in recording WMS data.

Central: (Ontario/Quebec–RN/RPN)

Ontario/Quebec: RNs

Several health care sectors were represented in this focus group; only the hospital sector used a WMS tool, however. The non-hospital sectors had significantly different understanding of the purpose and function of WMS tools.

• General impressions Insufficient time to complete the tool in the manual format contrasted with the report by the nurses using an automated tool. This focus group overall was enthusiastic regarding improving the WMS tool and positive about its potential value. As well, participants indicated that management was supportive and there were dedicated budget resources to implement and improve the tool’s usability.



• Participants repeated the caution that insufficient orientation and resources to

support the tool would compromise the accuracy and efficacy of the tool.

Ontario: LPNs

Ontario LPNs are unique compared to other practical nurses as they input data on their assigned patients. Similar to all focus groups, participants indicated orientation was limited, informal and that they learned the application from peers.

• General impressions Compared to other practical nurse groups, this group had a more positive attitude towards WMS tools and this is possibly due to their direct involvement.

East: (New Brunswick, Nova Scotia–RN/LPN)

New Brunswick: LPNs in Acute Care

LPNs do not input data into the tools; however, they expressed a desire to be included in using the WMS tool.

• General impressions This group perceived that insufficient time to record data during the shift would affect compliance. The WMS information needs to be integrated into existing documentation requirements.

Nova Scotia: RNs

This focus group had broad representation from all sectors. GRASP® was identified as the WMS tool. Unfortunately, for community hospitals, the regionalization of services had resulted in discarding the WMS tool after many years of data collection. As in other areas, orientation was limited to a few hours and often was provided by peers informally.

• General impressions Believed that an electronic health record (EHR) would facilitate the collection of WMS data as a by-product of routine information collection. Reliability and validity tasks are completed regularly. Nonetheless, insufficient resources to respond to the information generated by the tool will compromise its efficacy.

Nova Scotia: LPNs

Although some focus group participants did not use a WMS tool, they were aware of it and its difficulties and wished to share this information.

• General impressions Most felt that an automated system would facilitate compliance. Possible use of personal digital assistant (PDA, e.g.,Palm Pilot) technology to document events at point-of-care. Believe that ownership in the development would ensure tools are unit-specific, which they believe affects compliance and efficacy.



6 CONCLUSIONS

Conclusions will be presented as a compilation of the literature review, survey and focus group findings and analyses. The respondents from the web-based survey were mainly nurse managers, mainly from acute care and almost all registered nurses. The focus groups were comprised of nurses only; the largest group was LPNs and the sector most represented was long-term care. Findings from the survey and focus groups were consistent with the information gathered through the literature review. The authors identified distinct trends during the assessment of use, compliance and efficacy of WMS tools used by the three occupational groups across hospital and community health care settings.

Results of the survey and focus group were consistent with the themes and recommendations identified in the literature. Participants repeatedly reported dissatisfaction with WMS tools. They believe the current WMS tools are outdated and do not reflect the changes in hospital-based health care.

Existing tools do not reflect care needs of the evolving patient profile. These new patient profiles result from changing demographics, improved technology and constrained fiscal resources that force reduced length-of-stay. Higher acuity in inpatients affects nurses’ workload. Existing WMS tools are not adequately tracking and measuring the complexity, multi-tasking, and staff-mix elements of the nurses’ work environment.

6.1 Assessment of use

WMS tools as defined in this document were mainly used in the acute-care hospital sector with the greatest concentration in teaching hospitals. As identified in the literature, most nurses in the focus and survey groups were familiar with the uses of WMS tools.

One theme identified consistently was that limited initial and ongoing orientation negatively affected understanding and use of the tool.

LPNs frequently reported they were not using WMS tools. The LPN group expressed a desire to actively participate in the development of WMS tools and to be directly involved in the input of its own data.

6.2 Assessment of Compliance

The strongest recurring theme was there was inadequate time or no time to complete use of the tool. There was strong support for online recording of WMS data from electronic records in real time at the point-of-care. It is critical that WMS tools be integrated into nurses’ usual work (assessment/planning/interventions/diagnostics/evaluation). Anything less is seen as needless duplication, more work, more bureaucracy, insensitivity to nurses’ workloads and ultimately a lack of respect for the nurses trying to care for their patients. More than one nurse described recording WMS as a waste of time that takes nurses away from the patients. The lack of technology and costs of acquiring hardware, software and training were seen as significant barriers.



Nurses who were not involved in the development of the WMS tools were less compliant as they had no ownership of the tool and did not feel it reflected their practice. Other factors that impact compliance included insufficient orientation and insufficient ongoing resources, including dedicated staff to support the tool. Greater levels of orientation and ongoing support yielded greater understanding of the specific tool, its purpose and potential benefits and can be attributed to improving compliance. Nurses were less likely to comply with accurate recording of data and more likely to manipulate the data to their advantage if they had had negative experiences. This was especially true where WMS data had been used to reduce staffing or where they felt the nature of the tools and their organizations was punitive.

The perceptions of managers and staff may significantly vary as a function of their experience with and use of WMS. Managers may overestimate the compliance, validity and reliability of results since the staff information suggests significant issues may exist with completion (time, access to computers, understanding and inclination to select particular scores or values).

6.3 Assessment of Efficacy

Efficacy (the power to produce an effect) was the area that was the least well understood and discussed. Most nurses understood a WMS tool was for mandatory reporting. They also identified a role for WMS in staffing decisions, but were often at a loss to provide actual examples where use of the tool information actually supported staffing decisions. Their examples were overwhelmingly focussed on staffing reductions and efficiencies that were not perceived to enhance care or quality of work life.

Misconceptions around concepts such as validity and reliability further hindered efficacy. The conceptual challenge was itself further hindered by the lack of clear and commonly accepted language and definitions. Other concepts such as sampling, distribution and probability also limited discussion and understanding. Clearly, if a nurse does not understand or believe that the data fit a pre-selected distribution, then the nurse will not trust anything less than complete documentation as accurate. Many did not see any benefit to WMS for either patient care or staffing, but did believe the tool must be unit-specific to be effective.

All groups reported greater acceptance with retrospective tools than with prospective tools. This may partly reflect the underlying mistrust and misunderstanding about the validity and reliability of the data. While some recognized the limitations in documenting assessments and tasks that were done (retrospective WMS) compared to what should have been done (prospective WMS), retrospective WMS was clearly preferred.

An added element when assessing the tools’ effectiveness relates to observing outcomes. Most staff did not see the report(s) or identify any subsequent results or consequences. Many managers said they do not see meaningful reports and that the information seldom results in needed staffing additions.

6.4 Critical Elements in a WMS-tool

The critical elements of an effective WMS tool are clustered into two main areas, human resources and information technology.

Human Resource Elements



Adequate initial and ongoing orientation

Adequate scheduled time to complete the tool

Inclusion of all nurses (RN, RPN, LPN)

Easily understood summaries and reports shared promptly with staff

Clear, timely responses to results (staffing added)

Support for dedicated staff and education resources for chart development/updating/validation

Inclusion of all elements of clinical nursing practice (not just tasks)

Information Technology Elements

Adequate access to computers

Recording and reporting in real time

Reporting at point-of-care (e.g., PDA)

Electronic (e-Health Record with an imbedded WMS tool)

Resource support for computer use (e.g., replace keyboards with touch screens, timely resolution of technical problems, etc.)



7 RECOMMENDATIONS

7.1 Key Findings

The legacy of suspicion and mistrust surrounding WMS requires that future initiatives build a transparent process with results that staff will value. There are clear messages from the literature, focus groups and the survey respondents about the lack of time and supports to collect WMS data coupled with the perception about the lack of results for the effort involved. Clearly, nursing is demanding a much different approach if WMS tools are to succeed.

The preceding section identified critical elements for successful WMS tools. Skipping or skimping on any of these elements will impede the ability of a WMS to accurately reflect patients’ needs, nurses’ care delivery and optimal resource distribution.

7.2 Recommendations

This WMS tool study generated several recommendations based on findings from the literature review, the survey, the focus groups and interviews with key informants. The order of the recommendations is not intended to imply importance or priority.

7.2.1 Data, Information and Decision-Making

Select and standardize data elements and data collection to permit valid comparisons and benchmarking (“Information Highway”, minimum data sets). Software must match specifications for integration into the Information Highway.

Create a clearinghouse to share the tools, information and network to support decision-makers. This clearinghouse might be national or international given the market and use of WMS in other countries.

Investigate whether WMS could be sufficiently standardized and validated so that WMS can be included in formulas for case weights; obtain adequate resources.

Create, pilot and disseminate decision-support models that address and evaluate changes in patient mix and staff mix.

Critically evaluate and develop a generic business case to substantiate an investment of scarce resources (time, money, forgone opportunities) in the WMS to benefit the patients and nurses. Nurse leaders could use and adapt this business case to support both institution- and system-based decisions e.g., CIHI. Everyone needs a clear return on investment— staff nurse, manager, hospital, administration, patient, funders and taxpayers.



7.2.2 .Acceptance by Practitioners

Develop comprehensive strategy to address negative past experiences with WMS and the notable gap in understanding the internal and external validity of the tools. This recommendation will hopefully influence critical reflection about the current preference for retrospective tools and the perceived need for highly specific unit-based tools.

Investigate an interdisciplinary tool that captures the holistic aspect of patient-care needs.

Ensure that WMS reports clear results and that appropriate resources are provided in response to results (funding to adjust staffing, adequately trained and resourced nurses, adequately trained and resourced administrators).

Ensure human and IT resources for support (dedicated staff, tool orientation, computer skills training, ongoing monitoring, audits for reliability/validity and hardware and software).

Ensure adoption of electronic solutions by addressing the possible aversion to computers through use of intuitive systems, training and support.

Implement and sustain change. The plan must involve staff in all phases within organizations as part of the promotion and marketing strategies. The plan should include how to embed WMS into the organizational fabric so there is ongoing support and review.

Develop tools that are sensitive to issues that nurses identify as affecting their workload: 1) patient frailty or acuity index, or a system that recognizes care differences between patient profiles; 2) nurse indexes sensitive to the aging work force, lack of equipment, staff mix issues, etc.



APPENDIX A - LITERATURE REVIEW

Introduction This literature search investigates the utilization, compliance and efficacy of workload management tools for registered nurses (RNs), licensed practical nurses (LPNs), and registered psychiatric nurses (RPNs), across hospital and community health care settings. The review sources of information include: CINAHL database; on-line resources (CIHI, OHIH, HC, CNIA, CNA, NRU and others); Medline, Medscape; and eHealth Resource Centre Database. Key words for searching included workload measurement systems, patient classification systems, tools for WMS, managing nursing resources, vendors of WMS, and nursing informatics. These references were reviewed to identify existence of WMS tools, how data is used to support management decisions, data gaps and relevant issues.

Financial constraints, nursing workload/overload, accountability, clinical decision making and reporting are some of the driving forces behind the need to address issues in the Canadian Nursing Workforce regarding workload measurement. Clinical decision support is still in the early stages of its evolution. While some Canadian health care organizations have implemented computer based WMS; the majority have low use of information and technology.

The 2001 updated Tactical Plan of the Advisory Committee on Health Infostructure (ACHI) reflects a consensus achieved by federal, provincial and territorial jurisdictions on strategic first actions needed to implement the pan-Canadian health infostructure. The MIS Guidelines are national reporting standards; however, only Ontario has mandated provincial reporting of nursing workload measurement. In the 2003 budget, the federal government allocated millions of new dollars to ICT-related initiatives.

There is no shortage of vendors that can supply WMS tools with plug-in modules to address data collecting for performance analysis; staff planning tools; and integrated budget management tools. At issue for many organizations is the lack of technology infrastructure or infostructure to adequately implement computer-based WMSs. Another important issue is the need for dedicated human resources to implement, monitor and provide education resources for the WMS. The federal government has taken on a number of initiatives to address the development of a pan-Canadian infostructure and is funding pilot projects to test predictive modelling for nursing human resources. This review of the literature while extensive is not all-inclusive as the subject matter is vast and relates to numerous human health care issues and delivery of service. One information gap noted in completing this review was access to information for some nursing web sites that limited entry to organization members.

31 March 2004 Confidential Page 44


Historical development of WMS Canadian Workload Measurement Systems

In 1937, The National League of Nursing Education concluded that a system was needed to determine objectively the number of patient care hours required for each patient according to a given category. This did not, however, determine staffing requirement. . Almost 67 years later we are unable to consistently predict this beyond 24 hours. In 1947, the same organization attempted to group patients by severity of illness.

The 1950s were a period of studies and investigations that focused on classifying patients’ according to medical needs and nursing care requirements. While many systems were attempted, they faded into oblivion. The 1960s were a time when social medicine increased. Administrators were obliged to evaluate cost effectiveness, which resulted in a resurgence of the need for a patient classification system. Doctors completed prototype evaluations and nurses used a multi-criterion list. Unfortunately, to reach an agreement between the two groups required nurses to reduce their list to physical care tasks. Emotional and psychosocial well being of the patients was not considered and this system became ineffective. Studies were done to determine the appropriate mix of RNs to LPNs and orderlies and aids were responsible for non-nursing tasks.

Throughout the 1970s resources were scarce due to rising costs, which ultimately pushed some of the more expensive and less urgent health groups into outpatient settings and the private sector. While the balance between RNs and LPNs improved, administrators recognized that standard care hours based on census was not relevant. Thus the demand for an effective WMS became a priority. Consequently, a proliferation of studies ensued that resulted in numerous patient classification system (PCS) and WMSs. Many of these systems were adapted from industrial time and motion models. Three major issues hindered credible data collection: inability to capture high-level cognitive portions of nursing work, impact of multi-tasking, and care giver variability.

The 1980s saw the introduction of the Management Information System (MIS) Guidelines, which provided a national framework to collect and record financial and statistical data. Resource Intensity Weights (RIW) measure expected resource use by patient grouping according to diagnosis called Case Mix Groups (CMG). Workload measurement is an essential component of the MIS Guidelines. Throughout the eighties hospitals established WMS committees, allocated resources (financial and human), and implemented systems to measure nursing resource intensity to predict nursing staff requirements. For the most part the systems were paper-based and labour intensive.

In the early 1990s nursing was faced with large-scale layoffs that ultimately resulted in increased workloads. Consequently, nurses became less enthusiastic with recording workload data because reported staff shortages based on WMSs often did not result in staff increases. As well, there was a reduction in dedicated resources to coordinate, monitor, and evaluate the data collection.

Computer technologies significantly impacted opportunities for advancement of information gathering, analysis and decision-making. In the second half of the nineties the information technologies revolution exploded providing tremendous opportunities for data collection and implementation of effective WMSs. However, there was a concurrent need for an analytical framework; government involvement to develop regulation and policies for use and distribution; as well as, government to provide implementation and ongoing funding.



Only Ontario has mandated provincial reporting of nursing workload measurement. The Joint Policy and Planning Committee, (JPPC) a partnership between the Ontario Hospital Association, (OHA) and the Ministry of Health, is responsible for coordinating the implementation of the MIS Guidelines in Ontario. In 1996 the Nursing Professional Advisory Working Group, (NPAWG) was established to advise on issues related to nursing data collection and reporting. Since 1997 the federal government has invested over 1.5 billion dollars in a variety of health care projects involving information and communication technology (ICT). In the 2003 budget, the federal government allocated millions of new dollars to ICT-related initiatives.

Clinical decision support is still in the early stages of its evolution in Canada. Coupled with low use of information and technology generally by health care providers, there is still limited availability of clinical decision support tools. Where they are available, they are still not well integrated with professional practice. While there are recognized leaders in Canada promoting the use of clinical decision support tools there are gaps in the public policy required to support evidence-based care and accountability. (For more information visit: www.hc-sc.gc.ca and search for Office of Health and Information Highway.)

American Patient Classification Systems

The evolution of PCS in the United States was similar to the experience in Canada until the 1980s. The introduction of managed care began to transform health care into a business. The federal implementation of diagnosis-related groups (DRGs) in 1983 dramatically affected management of inpatient services. Outpatient services began to increase, and accrediting organizations mandated patient classification systems. As well, the Joint Commission on Accreditation of Healthcare Organization, (JCAHO) utilized DRGs as a basis for funding and accreditation. Organizations invested heavily in technology and informatics. Numerous PCS were introduced to monitor productivity, quality staffing and to comply with regulatory agencies. For example, acute care hospitals in California are required by both the JCAHO and California Title 22 to have a reliable and valid patient classification. In May of 2003 Senator Daniel Inouye (D-Hawaii) introduction the Registered Nurse Safe Staffing Act of 2003, federal legislation that aims to ensure that patients receive safe, quality nursing care in hospitals and other health care institutions. The legislation mandates the development of staffing systems that require the input of direct-care RNs and provides whistle-blower protections for RNs who speak out about patient care issues.

The advent of new technologies are leading to the development of PCS in which nurses carry a transponders, to record patient data while at the bed side. They are then able to transmit this data to workstations or access embedded system devices at the patients’ bedside, which can be tracked by software that produces real-time work activity charts.

Definitions: RN, LPN, RPN

Nursing includes three regulated occupational groups that work in a variety of roles and organizations across the continuum of health services. These include registered nurses (RN), licensed practical nurses (LPN), and registered psychiatric nurses (RPN). In 1999, there were 256,544 RNs (76%), an estimated 66,100 LPNs (22%), and 5,408 RPNs (2%). The delivery of nursing care is also supported by a number of unregulated workers such as nursing aides. [Canadian Institute for Health Information. Supply and Distribution of Registered Nurses in Canada, 1999. (2000), Ottawa, ON.



RN: A registered nurse is a nurse who is licensed or registered to practice within his/her province or territory. This requires that the nurse’s practice is consistent with legislation, professional nursing standards and the code of ethics of the profession. Registered nursing practice requires the application of an appropriate conceptual model or models and utilizes the nursing process of assessment, diagnosis, planning, implementation and evaluation. A registered nurse may practice within the domains of clinical, research, administration, policy and education. Registered nurses provide services to individuals, families, communities and populations. They serve persons of all ages and at varying levels of illness or wellness. Registered nurses practise in a variety of settings including hospitals, outpost stations, clinics, homes, long term care facilities, community health centres, prisons, schools, workplaces, government, etc (CNA).

LPN/RPracN: Licensed practical nurse (sometimes known as registered practical nurse); the practise of practical nursing is the provision of nursing services for the purpose of assessing and treating health conditions, promoting health, preventing illness, and assisting individuals, families and groups to achieve an optimal state of health. (CPNA, 2001, Licensed Practical Nurse Act in Manitoba).

RPsychN: (Registered Psychiatric Nurse) Psychiatric nursing is regulated as a distinct profession in Canada in the provinces of Alberta, British Columbia, Manitoba and Saskatchewan. Registered Psychiatric Nurses use a holistic approach in providing psychiatric nursing services to individuals, groups, families and communities whose primary care needs are related to mental and developmental health. At the core of the practice of psychiatric nursing is the therapeutic relationship and a commitment to the promotion, restoration and maintenance of optimal health. RPsychNs focus on understanding the relationship between the physical, spiritual, cultural and psychosocial aspects of individuals. Interventions are planned, implemented and evaluated within the context of the therapeutic relationship. RPsychNs practise in a variety of settings and with various populations, including community mental health with children, adolescents or adults; in psychiatric units/hospitals; in psychogeriatric programs; in forensic units; and in crisis services (Registered Psychiatric Nurses of Canada).

Nursing Informatics in Canada

Nursing informatics organizations include: Canadian Institute for Health Information (CIHI), Canadian Nursing Informatics Association (CNIA) and Office of Health and Information Highway (OHIH) who hosted a national workshop for all funding recipients under the Canada Health Infostructure Partnerships Program (CHIPP). Other important contributors to nursing informatics include: Canadian Nurses Association (CNA) Canadian Nursing Advisory Committee (CNAC), Canadian Nurses Federation (CNFU), National Nursing Informatics Project (NNIP) and Ontario Nursing Informatics Group (ONIG). Health Infostructure Atlantic (HIA) Western Health Information Collaborative (WHIC).

More than 15 federal funding initiatives/programs have been identified. These initiatives/programs in total have funded more than 153 ICT-related projects. The Health Infostructure Support Program (HISP), by sponsoring more than 35 projects, is the largest funding program in terms of the number of projects sponsored, and the Canadian Health Infostructure Initiative (CHI), which received more than $112,500,000 (between 1999 to 2002), is the largest in terms of the amount of money spent.

Pan-Canadian Health Infostructure



The 2001 updated Tactical Plan of the Advisory Committee on Health Infostructure (ACHI) reflects a consensus achieved by federal, provincial and territorial jurisdictions on strategic first actions needed to implement the pan-Canadian health infostructure.

At federal, provincial and territorial levels, this has been translated into numerous health information and technology initiatives. Varying considerably in scope and size, these include local systems planning projects to ambitious pan-Canadian initiatives aimed at addressing pressing information needs or introducing new technologies to improve service delivery.

Many relevant information and technology initiatives are currently underway in Canada, both at a local and national level. Almost all federal, provincial and territorial governments across Canada have a strategic information systems initiative. HealthNet/BC, Alberta Wellnet, Saskatchewan Health Information Network, Manitoba Health Information Network, Smart Systems for Health, Inforoute Santé, Nova Scotia Telehealth Network and PEI's IslandNet are all examples of these initiatives. At the federal level, Health Canada has the Canadian Health Network, Network for Health Surveillance in Canada, First Nations and Inuit Health Information System (FNIHIS) and the HISP/CHIPP projects; Industry Canada and CANARIE support ehealth and network infrastructure, and together CIHI, Health Canada and Statistics Canada have the Health Information Roadmap initiative underway.

Clinical Decision Support

Data Analysis and Reporting The use of the health data holdings for analysis and reporting is a critical requirement. There are significant efforts in the different jurisdictions across Canada to use health data for identifying potential health risks, as well as for planning, research and evaluation purposes. Further, there has been a very focused and successful effort by CIHI to improve the reporting of health status and health system performance to the Canadian public. Apart from the limitations of the data currently available, the major gaps include data analysis and reporting for evidence based decision-making in clinical, public health, research and management settings, as well as for health system accountability.

Human Resources Linked to the change management is the need to educate health human resources in the use of health information and technology. Canada suffers from a severe shortage of health informaticians to support clinicians and other users in the implementation of electronic health record and telehealth solutions. There are currently Health Informatics programs in some Canadian colleges and universities,but there is a serious lack of accessible health informatics programs for health professionals who are already in the workplace.

http://www.hc-sc.gc.ca/ohih-bsi/pubs/2001_plan/plan_e.html#intro

Health Information: Nursing Components

Information concerning aspects of nursing care that affect client outcomes must be included in existing and future databases on client-centred information. Although the information is available on the client's documents, none of the data are abstracted from the records and saved in a permanent database of health care information. There is no overall system in place, either nationally or provincially, to collect, retain and retrieve information which reflects nursing care. Health Information: Nursing Components (HI:NC) data that could represent nursing's contribution to a larger system of client-centred health information. HI:NC is information that represents the most important pieces of data



about the nursing care provided to a client during a health care episode. Another term for HI:NC is Nursing Minimum Data Set.

International Classification for Nursing Practice

CNA believes data involving identifiable health information should only be used with the consent of the individual. Based on achieving these objectives, CNA endorses testing, through demonstration and other projects, by the International Classification for Nursing Practice (ICNP®) for use in Canada as a foundational classification system for nursing practice in Canada. (CNA Policy Statement 1993)

CNA endorses testing, through demonstration and other projects, by the ICNP® for use in Canada as a foundational classification system for nursing practice in Canada.

There is national data from provincial/territorial registration files on the supply of registered nurses but there is no data on what nurses do in terms of health interventions, little data on the utilization of nursing resources by patients, and little data on patient outcomes that are relevant to nurses. With changes being made to CIHI’s national coding systems, there are opportunities to formalize a data collection process for nursing within the larger health care information system.

The International Council of Nurses (ICN) began its work over 10 years ago to lead the development of a universal language for defining and describing nursing practice—-the ICNP®. The purpose of ICNP® is to provide a tool for describing and documenting key elements that represent clinical nursing practice. ICNP® provides nursing with a framework that facilitates cross mapping of existing nursing vocabularies and classifications to enable comparison of nursing data across organizations and health sectors, and among countries. The nursing care elements of HI:NC (i.e., client status, nursing intervention and client outcome) are included in ICNP®. (CNA Position Statement 2001)

International Nursing Minimum Data Set (i-NMDS)

The i-NMDS is composed of the essential, minimum data elements to be collected in the course of providing nursing care. These data provide information to describe, compare, and examine nursing practice. Work toward the i-NMDS is intended to build on the efforts already underway in individual countries. It is imperative that the national health care infrastructure supports the collection and reuse of nursing data. The contribution of nursing care and nurses is essential to health care globally. The i-NMDS as a key data set will support: Describing the human phenomena, nursing interventions, care outcomes, and

resource consumption related to nursing services

Improving the performance of health care systems and the nurses working within these systems worldwide

Enhancing the capacity of nursing and midwifery services

Addressing the nursing shortage, inadequate working conditions, poor distribution and inappropriate utilization of nursing personnel, and the challenges as well as opportunities of global technological innovations

Testing evidence-based practice improvements

Empowering the public internationally



http://www.icn.ch/matters_i-NMDS_print.htm and

http://www.nursing.uiowa.edu/sites/NI/research_frm.htm



Existence of Workload Measurement Systems What Form

There are many WMSs and the exact number of organizations using WMSs in Canada is at present unknown. However, the Nursing Professional Advisory Working Group of the JPPC conducted a survey in 1997 that reported three systems predominate in Ontario. GRASP®/GRASP-like, NISS, and Medicus. While some centres are using automated or semi-automated systems the majority remain paper-based. Shaw 2003, reported on three centres that are using computerized clinical decision support systems. University Health Network (UHN); St. Michael’s Hospital in Toronto; and at the McGill University Health Centre (MUHC) in Montreal. All three institutions are producing both clinical and financial advantages for themselves and their patients through decision-making that is supported by a computer system. UHN uses a Misys Data Warehouse, and MUHC uses the latest ”Gold” version of the Eclipsys Sunrise Decision Support Manager product that allows for access to the data through their Intranet. At St Michael’s, decision-support does care costing for all outpatient visits. Their system provides fully-costed patient visits data, which include 30,000 in-patients, 60,000 emergency patients, and now 500,000 ambulatory whose visits are also fully costed.

Patient Requirements for Nursing Care: The development of an Instrument Fulton & Wilden, Victoria, BC (1998)

Patient Requirements for Nursing Care (PRNC) A tool to be used in conjunction with WMSs. Five distinct components: instability; clinical judgement; educational needs; emotional support and physical care. Patient care needs as a determination of nursing staff mix. Incorporates components of nursing work beyond performance of tasks because it includes measuring level of complexity, decision making and clinical judgement required to meet patient care needs and therefore accurately reflects nurses’ work. The PRNC differentiates between the complex, unstable patient requiring a high level of nursing care and those patients that are less complex, more stable and have more predictable outcomes. This system was implemented in a medical-surgical unit. This tool objectively measures patient requirements and was not designed to replace a WMS but to capture patient care requirements that are not identified by existing WMS.

Workload/Outcome Measurement for Hospital Educators: Confirming the Value of Education

Vancouver Hospital and Health Sciences Centre Prociuk, J.; Beetstra, J. (1998)

Vancouver Hospital and Health Sciences Centre, the education department has implemented a workload/outcome measurement system. The system is unique in that it considers the “costs” in terms of the time and money necessary to produce an educational product in relationship to the benefits of that product to the organization. This independent study offering is appropriate for nurses engaged in any aspect of nursing staff development. The learner will achieve the following objectives:

1. Identify the elements necessary to deliver educational services; 2. Describe a system to monitor workload and identify outcomes of education.



Monitoring workloads can assist new educators in understanding their role in the organization and can help set work priorities and adjust work activities. Outcome measurement, the more significant of the two, can provide justification for workloads and can acknowledge the importance of removing system barriers and of providing ongoing support in the workplace to achieve higher level outcomes.

Patient classification systems (PCSs), also known as patient acuity systems; The Third Generation

Providence St. Peter Hospital, Olympia, Washington Malloch K. et al. 1999

3PCS Providence: This third-generation model is designed to address the lack of sensitivity to the unique holistic nature of patients, fluctuations in economies of scale, and variability in caregivers. The 3PCS includes five elements: 1) standardized, researched-based, interventions and outcomes categories; 2) descriptions of patient care in "comprehensive units of service"; 3) identified caregiver roles; 4) patient medical record documentation forms; and 5) caregiver competency profiles.

Development of the 3PCS moved the staff from examining multiple discrete tasks to a holistic perspective that includes both empirical and intuitive knowledge reflective of the multiple dimensions of the health experience. The following nine advantages can be attributed to the new system. • A more accurate reflection of the holistic nature of the human health experience. It

considers the multidimensional, interactive essence of both the quantitative and qualitative aspects of care, as well as the specific context of care.

• A more accurate representation of professional nursing practice than an activity-based patient classification system. Interactions of clinical, psychosocial, environmental, and health management issues are considered in 3PCS.

• Research based. Information from the University of Iowa Nursing Interventions and Outcomes Classification project, and Benner's care-giver novice-to-expert classifications are incorporated in the model.

• Easily automated using generic spreadsheet software. • A relatively simple method for collecting trend information. • Accommodates a patient-centred model with multiple care-giver roles-the new

system is able to respond efficiently to patient care delivery system changes in skill mix and labour cost.

• Emphasizes accountability, information sharing, professional expertise, and care-giver competence. Access to crucial patient care information enhances care-giver ability to get things done, to mobilize resources, and to meet goals.

• Provides opportunities for cost savings. Savings should be realized from the streamlined evaluation process. The elimination of extensive auditing of task processes and data collection decreases labour costs and the associated system support.

• Decreases competition for resources between staff and management.

Design and Implementation of a Patient Classification System for Rehabilitation Nursing The need for a well-developed PCS in the rehabilitation setting has increased Sarnecki et al. (1998)



Division of Nursing at Rehabilitation Institute of Chicago (RIC) Development of a prototype patient classification (PCS) instrument designed specifically for rehabilitation patients. The primary advantage of this new model is that it provides more direct professional nursing care to patients on a shift-by-shift basis, as required by the increased acuity of patients. There are three major types of patient classification instruments: the factor instrument, nursing task documents, and the prototype instrument. The prototype instrument involves designation of several levels of nursing intensity reflecting demands for nursing care requirements and criteria for each level. The prototype instrument is a form of triage and can incorporate all aspects of the nursing process.

Nursing task instrument systems reflect work sampling and time and motion methods seen in industrial settings. The critical indicators approach or factor evaluation system involves the listing of indicators representing clusters of care activities. In the United States, prototype systems practically were abandoned for the more objective systems. The scope of nursing practice captured by task systems is limited to those very visible activities of nurses. Task systems tend to portray nurses as technicians, while ignoring assessment, diagnosis, planning, coordinating, and evaluation, the high-level cognitive/judgmental portion of nursing work that is the essence of discretionary professional nursing practice.

Clocking Care Hours with WLM Tools

Current Management literature focuses on redefining existing nursing resource measurement rather than inventing new PCS Adams-Wendling L. (2003) Kansas, USA

An evaluation of the relationship between the GRASP® WLM tool and the Resource Utilization Group Classification (RUGS III) nursing workload projections tool. Management can rely on one workload assessment tool that measures self-care deficits and move to one mandated standardised tool (MDS –Minimum Data Set and RUGS)

Each facility in the study also completed the mandated MDS assessment and the LOL assessment. Due to this duplication, the researcher wanted to identify whether or not the MDS RUGS nursing staff time measurements (hours per resident per day) would be similar or related to the LOL nursing staff time measurements (hours per resident per day).

The results of this analysis of these two workload systems suggests that it is legitimate to consider similar hours of care estimates of one system from the other. This study supports the staff time measurements used by the CMS to develop the RUGS for nursing facilities, which accurately reflect the nurse staffing needs in nursing facilities similar to an established workload estimate tool such as the GRASP methodology. What, exactly, does this mean for long-term care nurse leaders? Managers can rely on one workload assessment tool that measures self-care deficits and move to one mandated standardized tool (MDS and RUGS) that integrates financial data, clinical data, reimbursement data, and nursing workload projection. They can adjust their daily practice to eliminate the duplication of two systems, as long as they continue to evaluate the relationship and accuracy of staff time nursing workload projections provided by the CMS with other established nursing workload measurement tools.

The RUG-III Case Mix Classification System for Long-Term Care Nursing Facilities: Is It Adequate for Nurse Staffing?

Mueller C., (2000) Minnesota, Minneapolis



Using the nationally mandated resident assessment instrument, the Minimum Data Set (MDS), Medicare residents of LTC facilities are classified into a case mix group associated with the Resource Utilization Group Version III (RUG-III) case mix classification system to determine the payment the nursing home will receive for providing their care.

Means to determine the nursing care needs and associated nursing time for the residents' care, selected RUG-III groups were simulated by using the items on the MDS to describe residents classified into a case mix group. A description included information about the residents' cognition, sensory deficits, mood and behaviour, physical functioning, elimination patterns, clinical conditions, nutrition patterns, and medications.

Before implementing the study, content validity and test/retest reliability were demonstrated for the study instruments (12 resident descriptions and descriptions of two nursing units). Content validity was conducted and established using the expertise of two nurses in the Health Care Financing Administration National Case Mix Demonstration Project state project offices who worked intensively with the MDS and the RUG-III system. Test/retest reliability was established for the resident descriptions and the descriptions of the nursing units with reliability coefficients of 0.73 and 0.95, respectively.

Although more attention has been focused on the adequacy of nurse staffing in nursing facilities, nursing research is needed to test staffing methodologies in LTC facilities, to evaluate care delivery systems in relation to staffing, and to investigate clinical and cost outcomes associated with different staffing models.

A Comparison of Two Patient Classification Instruments in an Acute Care Hospital To compare the predictive validity of two types of PCS Seago J., (2003) California

California Hospitals are required by both the JCAHC and California Title 22 to have reliable and valid PCS. Two types are summative and critical incident. There is modest research demonstrating the validity and reliability of different PCS but none regarding predictive values. The findings indicate that there are virtually no differences in the predictive ability of criterion versus summative PCS. More importantly managers should select a system that uses the fewest nursing resources possible and that at least one full time equivalent is allocated to maintain validity and reliability of the PCS.

MIS Resource Doc. For costing, workload should ideally be expressed in retrospective units of service. It is recognized, however, that many nursing workload systems are designed to collect workload prospectively in order to assist with staffing decisions. Prospective workload measurement systems also facilitate the identification of gaps in service provision or failure to meet performance expectations. If it is expected that each patient will receive a full bed bath each day, for example, this will be predicted and documented as workload in a prospective system. If the unit is short staffed on a particular day, only a partial bath may be provided but the full workload value would still remain. This failure to meet the standard would result in a productivity ratio, which is greater than 93%. This is not to be interpreted as efficiency but as failure to meet performance expectations.



Perceptions Validity of the tool

Validity can be defined as the extent to which a Workload Measurement System measures what it is designed to measure, that is, the ability to quantify and/or predict requirements for nursing resources (Hernandez, 1996). Validity is a matter of degree, not an all or none property, and the process of validation is unending (Giovanetti, 1984). In today’s changing environment validity must be measured at regular intervals to ensure that the system continues to reflect current practice, process, time, procedures, activities and technology (Hernandez, O’Brien Pallas, 1996). Redesign of the measurement tool may be necessary on a regular basis. There are several levels in the validation of a workload measurement tool. The first is related to the process for measuring the workload. The second is the validation of content. Do activities and times reflect current practice? The type of system in use will determine how much of the validity maintenance is the responsibility of the vendor and how much of the system can validated at the facility level (Hernandez, O’Brien Pallas, 1996). In a workload system based on task identification with facility developed times, the validation of the tool is primarily the responsibility of the facility. These systems are very flexible and can be adapted easily to reflect the rapid changes occurring in our current health care system. Measurement systems, which require all changes by the vendor, may be less responsive (Hernandez, O’Brien Pallas,1996).

Reliability of an instrument is the degree of consistency with which it measures the attribute it is supposed to be measuring. Inter-rater reliability refers to the extent to which data is reproducible. It is important that different nurses using the same WMS, to measure the same individual, at the same time, derive a consistent result. A reliable system shows consistent data no matter who takes the measurement (Hernandez, O’Brien Pallas, 1996). Inter-rater results below the target indicate a need for reeducation, redesign of the tool or the instructions on how to use the tool. The frequency and number of checks should be related to the use of the data and the importance of the resulting decisions. It is useful to test reliability both within a unit and between units (Hernandez, O’Brien Pallas, 1996).

Dissemination and Utilization of Workload Management Data

The Use of Information Generated by a Patient Classification System The Use of PCS-information by nursing departments in eastern Wisconsin Botter, (2000)

Findings indicated that the most common use of PCS information was to assist the nursing department with staffing decisions. Most respondents indicated interest in using PCS information to develop nursing personnel budgets and to cost out nursing care. Medicus PCS is used in more than 400 US hospitals. One manager said, "PCS wouldn't be the first thing for me to go and show them because . . . I don't think the staff has much faith in PCS."

However, the direction, intensity, and interaction of these relationships need to be investigated. Research regarding the costs and benefits of these systems is essential as well. The actual cost of purchasing a PCS is known, but no empirical data exist regarding the impact of use or non-use of PCS information in terms of cost per patient-day. Studies exploring the relationships between PCS information use in decision-making and the cost, quality, and outcomes of care would provide valuable information for nurse administrators.



Improving Staffing with a Resource Management Plan

Change from a crisis to a proactive mode for staffing, scheduling, resource pool utilization, information management and unit workload Kirkby et al. (1998) Ann Arbor, Michigan

In September 1995, the nursing leaders at St. Joseph Mercy Hospital (SJMH) were struggling to have the right mix of staff on hand during each shift. The outcome of the project was a detailed plan for responding to resource needs either because of service demands (patient volume) or deficit demands (lower-than-needed staffing). The foundation of the plan was to stabilize the unit core staffing so that it would meet the unit’s staffing model every shift and thus provide care for the expected number of patients.

In the plan, the nursing leaders decide the desired census for a unit, budget for it, hire into an ideal position complement, keep the positions filled, prepare schedules that meet model every shift, and make adjustments every shift. The resource pool is used for deficit demands (leave of absence, unscheduled absence, posted vacancies) and for service demands (patient volume). When demands are higher than budgeted, extra shifts, prescheduled overtime, and travelers are used. When demands really peak, agency staff, incentives, and mandatory overtime are used.



Types of Tools Selected Vendors There are numerous vendors of WMS and PCS see appendix 2. However for the purpose of this review the following have been selected. Descriptions are based on information from the vendors.

GRASP®

The GRASP® Methodology was first developed and used 25 years ago in the United States, designed by Grace Hospital, Morgan Town, NC. Today, four privately held companies make up The GRASP Systems International Companies. They provide automated work measurement and consulting services to a variety of health care facilities in three countries, the United States, Canada, and the United Kingdom. Corporate headquarters are based in the United States, in Colorado.

By 1980, it was becoming clear that automation was needed to help facilities better implement The GRASP Methodology. By 1987, many more facilities had computerized. To meet client needs GRASP introduced MIStro® Software to provide a more sophisticated level of automation. The GRASP® suite of PC-based software products includes DataWorks, StaffWorks, and Interfaces.

DataWorks saves costly time by automating the definition, collection, reporting, and quality monitoring of workload information. No other product offers such a powerful and broad scope of functionality for workload collection. Nowhere else will you get the kind of reports that give you the tools to analyse workload patterns, make equitable workload distribution assignments, analyse workload by caregiver, analyse intervention specific usage, and provide data for true performance improvement.

StaffWorks Automates the conversion of workload hours into staffing requirements specifically modeled for your organization. StaffWorks projects the cost associated with staffing decisions allowing the manager to make truly informed staffing decisions, as well as providing the data to create department specific budgets. Clients can also collect quality measures for each unit/department and report those out in a variety of management reports for analysis of staffing trends, utilization, and quality measures. Interfaces A variety of interfaces are available to import and export data to and from each of our MIStro modules. Below is a complete list of available interfaces. ADT Interfaces–Two Admission, Discharges and Transfers (ADT) interfaces (batch and

HL7 interfaces) for DataWorks

Acuity Import–Import of GRASP® workload from a mainframe HIS system to StaffWorks

Staffing Export–Export of workload data from DataWorks to a variety of staff scheduling systems and costing systems

Scheduling Import–Import of staff hours to StaffWorks from any scheduling or payroll system

QuadraMed® (formerly Medicus)

QuadraMed® is dedicated to improving health care delivery by providing innovative health care information technology and services. From clinical and patient information management to revenue cycle and health information management, QuadraMed delivers real-world solutions that help health care professionals deliver outstanding patient care with optimum efficiency. Behind the products and services is a staff of more



than 850 professionals who provide support to more than 1,900 health care provider facilities.

Information Systems with Affinity®

Affinity is an industry-leading health care information system that integrates clinical, financial, and patient information around a single, patient-centred scalable database. With its open technology and powerful suite of client-focused products it is easy to implement and even easier to use.

Information Management with Quantim®

With the multi-dimensional Quantim application, provides access to the full breadth of health information management, skills, software, and services. Their suite of modular and integrated tools eliminates the duplication of records and streamlines the billing process so that users can process and deliver the most appropriate patient care.

Compliance Solutions with Complysource®

Complysource provides powerful software tools to ensure a strong compliance program that protects patients and the ongoing viability of the organization. With the growing complexity of health care laws and federal regulations, having the right tools and services in place for a truly effective compliance program is now more important than.

Financial Management with Chancellor™

Chancellor financial products and services enable organizations to improve cash flow and deliver the in-depth data analysis that is crucial to managing budgets of any size. With a spectrum of solutions, organizations can perform a wide range of tasks from accelerating collections to identifying outstanding receivables that are vital to the bottom line.

ENEPCS:

ENEPCS is a contemporary model that incorporates all activities and care needs of the patient. ENEPCS is customized by the caregivers in each organization to reflect actual workload requirements rather than using pre-set standards. Caregivers actively participate in the development of the system and in the ongoing monitoring of the effectiveness of the model. In most situations, a quick and obvious revitalization of the registered nurse’s professional accountability for interventions and outcomes occurs. Professional accountability is emphasized and reinforced in the ENEPCS model with the use of standardized intervention language developed by the University of Iowa Nursing Interventions Classification project.

ENEPCS Reflects the complexity of patient care and considers patients’ needs from a holistic

perspective using the Comprehensive Unit of Service (CUS).

Identifies the uniqueness of each patient using eight categories of care: 1. Cognitive Status 2. Self-care Status 3. Emotional PsychoSocial Support needs 4. Comfort/Pain Management needs 5. Family information and support needs 6. Treatment needs7. Interdisciplinary coordination, patient teaching, and documentation needs



8. Transition planning needs.

Is based on professional nursing judgment, not a task-based model, using the expert knowledge and experience of nurses delivering the care

Can be used in multiple clinical settings by multiple caregiver roles

Identifies the skill mix and cost of labour for each patient

Creates a valid and reliable system for your organization

Transitions the benefits of current industrial based PCS to ENEPCS

Meets regulatory requirements of Medicare Conditions of Participation, JCAHO, California-Title 22, and ANA Principles of Staffing

Can be computerized or used manually

Provides a simplistic method for information trending, calculation of labour cost each shift, labour cost per patient day, and overall productivity monitoring using a generic spreadsheet application.

Catalyst

Catalyst is the leading provider of data-based solutions that enable health care organizations to make precise staffing-related decisions. The EVALISYS® tools, methodologies, and systems have proven their value time and again in health care organizations of all types and sizes.

EVALISYS Patient Classification System (PCS) is a family of valid, reliable workload measurement tools and processes that set the standard for clear-sighted staffing solutions. With its unique ability to capture care complexity, EVALISYS PCS allows flexible, acuity-based staffing for all care disciplines.

EVALISYS PCS Plus combines leading-edge analytic and observational methods to make workload and problematic staffing patterns visible. PCS Plus takes the guesswork out of staffing and skill-mix changes.

EVALISYS Patient Classification/Staff Activity Study™ (Study) process offers comprehensive, enterprise-wide evaluation of staff utilization patterns and workload for all clinical disciplines, no matter what patient classification system you use. This objective process illuminates ways to ensure that the right care is provided to the right patient by the right staff.

With Catalyst, you have the knowledge-based foresight to make tough decisions with greater ease and confidence. You can see what your needs really are and make changes that are responsible, informed, and more precise than ever before.

Labour Resource Management System (LRMS)

For Patient Care Decision Making

The LRMS created by Applied Management Systems, Inc. provides for shift, daily, weekly and monthly reports, or can look at any desired timeframe to help quickly understand the operational status of various nursing areas. Designed to run on an IBM compatible personal computer, LRMS provides concise, meaningful management reports at the department and division levels. It takes data that is typically captured and organizes it into relevant information sets that describe at-a-glance how each unit, department or



division is performing. This easy-to-use tool provides essential information to support decision-making as well as reports to management.

Patient care managers need reports that combine relevant indicators of workload and calculate required hours for comparison to actual hours. This tool is highly flexible, and forms and reports can be adapted to the unique needs of any area within an organization—inpatient, outpatient, surgical suite and so on. More important, it gives accurate, timely information to help manage budget, adjust actual to required staffing levels, adjust staffing mix and assess productivity. An affordable tool that's designed to run on the hardware and software typically found in health care organizations, LRMS is an essential tool for patient care managers.

Tenet Information Services, Inc

Tenet provides software (EDNet32, Intellichart), consulting services, and system solutions to Emergency, In-patient Nursing, and Out-patient Nursing departments as well as other hospital departments and clinics. As a leader in health care information system and management engineering solutions for clinicians, nurses, and therapists, they make a positive impact on patient care by improving efficiency, containing costs, and minimizing risk.

Tenet Software Products

EDNet32 A Computerized Patient and Data Management System for the Emergency Department

Intellichart A Computerized Patient and Data Management System for In-Patient Nursing Units

P.E.R.F.O.R.M.: A Hospital Productivity Monitoring System

QuikStaf: The Quality Staffing System

RES-Q® Workload and Productivity Management

Founded in 1979, RES-Q has implemented hundreds of computerized applications for enterprise-wide employee staff scheduling, nurse scheduling, patient classification, productivity management, operating room scheduling, and surgery department management throughout the U.S., Canada and Europe.

RES-Q Healthcare Systems is a leading provider of labour management and employee scheduling software for the health care industry. The RES-Q family of software products include applications for enterprise-wide employee scheduling, nurse scheduling, patient classification, productivity management, credentialing, operating room scheduling, and surgery department management.

RES-Q Healthcare Systems' Labour Resource and Workload Management System is a modular PC-based client/server software application for performance and productivity management. The system includes capabilities to support enterprise-wide employee scheduling, personnel management, workload staffing, department labour budgeting, as well as other functional areas.



Nursing Workload Related Issues in Other Countries East Switzerland, Centre for Informatics and Managed Care

Fischer, in Switzerland, has been examining PCS issues and theory. He proposes independent application of PCSs for each professional sector-clinical, nursing, and support services.

ISE's Institute for Health and Economics

NURSING data project aims to set up a national nursing information system that covers all three health care sectors—home care, nursing homes, and hospitals—as well as Switzerland's four linguistic regions and all medical specialities. This system has to be compatible with other Swiss systems, such as National Health Statistics, health or disease classifications (ICD-10), medical and nursing pricing systems, etc., and will also have to allow for international comparisons, taking in account national legislation on data protection. In 2001, the project entered the second and operational phase.

The Institute is in charge of the following projects and activities. The management of the Swiss APDRG project (All Patient Diagnosis Related Groups),

mandated by the Swiss APDRG group

The management of the NURSING data project

The management of the FARMED health network, mandated by its Joint Commission

The secretariat of the Intercantonal Technical Commission of PLAISIR©

The operational secretariat of the Swiss Working Group on Patient Classification Systems (CST-PCS). http://www.hospvd.ch/ise/en/

New Zealand Ministry of Health

Nursing Sector Update August 2002

In June 2000 the Ministry piloted a project to determine the applicability of a nurse workload measurement and planning system in New Zealand’s public hospitals. The system used for the pilot was TrendCare Systems Limited, Australia which was piloted in six DHBs:Auckland, Capital and Coast, West Coast, Bay of Plenty, Taranaki, and Wairarapa DHBs.

The Ministry received the final report in September 2001 and has been analysing the findings. Implementing the recommendations contained in the report will require in-depth information on nursing workload management within DHBs.

The Ministry has sought international peer review of the report from Professor Donna Diers (Yale School of Nursing, US).

In order to gain an understanding of the information that is currently available in the sector, the Ministry contracted the New Zealand Health Information Service to undertake a stock take of current systems in DHBs that are conducive to collecting, measuring and reporting on nursing workload information. We also sought to establish the capacity of existing systems for medications. We have now received the stock take and international peer review and are developing a work program to proceed with this work.

New Zealand Ministry of Health Acute Mental Health



Nursing Workload Measurement in Acute Mental Health Inpatient Units. February 2002

There is currently no means of estimating the nursing requirements of consumers, or the nursing workload in acute mental health inpatient units in New Zealand. A team of researchers from South Auckland Health (SAH) and the University of Auckland undertook this project. The project addressed issues of nursing workload measurement, the infrastructure necessary to meet nursing workload requirements, and the terminology for national consistency in managing mental health nursing workload.

This report details a project aimed at establishing an acuity system at a national level, appropriate to multiple acute mental health inpatient settings. Measurement of nursing workload is a complex process aimed at providing a range of data, which will enable rational decision making in allocating resources to nursing, and in allocating nursing resources to consumers. Determining the distinction between nursing and ‘non-nursing’ work, and linking this to an ideal skill mix, is an essential requirement for nursing workload measurement.

Key findings of the literature review Health funding restraint and development of community mental health care has

restricted inpatient use to those with as high level of clinical acuity.

All systems for consumer care attempt to standardize the nursing resource needed for hours of care for consumer type.

Most of the systems reviewed have been developed in general hospitals and few studies have been carried out in mental health settings.

Two systems in current use, Trendcare, and PANDA were identified for further consideration.

Recommendations Future work for New Zealand requires rigorous prospective evaluation of PANDA

and/or Trendcare, measuring their impact on key nursing, economic and consumer outcomes. This should be carried out in the context of nationwide infrastructure limitations, and the concern expressed by DHB staff of the ways in which an instrument may be used. Such an analysis would need to consider the compatibility of these systems with existing information systems.

That future studies aimed at matching consumer needs to nursing skill mix are focused on nursing workload rather than clinical acuity.

That the issue of nursing infrastructure is further investigated, particularly the problems of recruitment, retention, and skill mix.



Research Bibliography 1. Arthur T, James N. (1994). Determining nurse staffing levels: a critical review of the

literature. J Adv Nurs 19(3), 558-565.

2. Adams-Wendling, L. (2003). Clocking care hours with workload measurement tools. Nursing Management August, 34 (8), 34–39.

3. Benton D. (2003). On the front line: David Benton describes a new system for managing the deployment of front-line staff. Nurs Manage (Lond) Sep 10 (5), 15-7

4. Bolton LB, Aydin CE, Donaldson N, Brown DS, Nelson M, Harms D. (2003). Nurse Staffing and Patient Perceptions of Nursing Care. JONA, 33 (11), 607-614.

5. Botter M L. (2000). The Use of Information Generated by a Patient Classification System. J Nurs Adm 30 (11), 544-551.

6. Burke TA, McKee JR, Wilson HC, Donahue RMJ, Batenhorst AS, Pathak DS. (2000). A comparison of time-and-motion and self-reporting methods of work measurement. J Nurs Adm 30 (3), 118-125.

7. Curtin LL. (1995). Nursing productivity: from data to definition. Nurs Manage Apr 26, 4, 25, 28-9, 32-36.

8. Darmoni SJ, et al. Horoplan: Computer-Assisted Nurse Scheduling Using Constraint-Based Programming. Internet: www.chu-rouen.fr/dsii/publi/plao.html.

9. Gaudine AP. (2000). What do nurses mean by workload and work overload? Can J Nurs Leadersh, 13 (2), 22-27.

10. Fulton TR. Wilden BM. (1998). Patient Requirements for nursing care; the development of an Instrument,. Can J Nurs Adm, 11(1), 31-51.

11. Houser J. (2003). A model for evaluating the context of nursing care delivery. J Nurs Adm, 33 (1), 39-47.

12. Park JH. Recent Publications of Jung Ho Park. Internet http://www.imia.org/ni/Country_reports2003/Korea_2003.pdf

13. Hughes M. (1999). Nursing workload: an unquantifiable entity. J Nurs Manage, 7(6), 317-322.

14. Johnson, Nolan, MT. (2000). A Guide to Choosing Technology to Support the Measurement of Patient Outcomes. J Nurs Adm, 30 (1), 21-26.

15. Kirkby MP, Dost P, Holdwick CC, Poskie M, Glaser D, (1998). Improving staffing with a resource management plan Sage M. J Nurs Adm,DM 28 (11), 25-29.

16. Koivula M, Paunonen M, Laippala P. (1998). Prerequisites for quality improvement in nursing. J Nurs Manage, 6 (6), 333-42.

17. Malloch K & Conovaloff A. (1999). Patient Classification Systems, Part 1: The third Generation. J Nurs Adm, 29 (7/8), 49-56.

18. Malloch K, Neeld A P, McMurry, C, Meeks L, Wallach M, Williams S, Conovaloff A.



(1999). Patient Classification Systems, Part 2: The third Generation. J Nurs Adm, 29 (9), 33-42.

19. Manthey M. (1989). The role of the LPN or . . . the problem of two levels. Nurs Manage, 2 (3), 17-22.

20. Mark BA, Salyer J, Harless DW. (2002). What explains nurses' perceptions of staffing adequacy? J Nurs Adm, 32 (5), 234-242.

21. Martorella C. (1996). Implementing a patient classification system. Nurs Manage, 27 (12), 29-31.

22. Mueller C. (2003). The RUG-III Case Mix Classification System for Long-Term Care Nursing Facilities: Is It Adequate for Nurse Staffing? J Nurs Adm, 30 (11), 535-543.

23. O’Brien-Pallas L. Baumann A. (2000). Toward evidence-based policy decisions: a case study of nursing health human resources in Ontario, Canada, Nursing Inquiry, 7, 248-257.

24. O’Brien-Pallas L. Baumann A. Donner G. Tomblin Murphy G. Lochhaas-Gerlach J. Luba M. (2001) Forecasting models for human resources in health care. Journal of Advanced Nursing, 33 (1), 120-129.

25. O’Brien-Pallas L. Irvine D. Peerboom E. Murray M. (1997). Measuring Nursing Workload: Understanding the Variability. Nursing Economics, 15 (4), 171-182.

26. O’Brien-Pallas L. Thomson D, Alksnis C. Luba M. Pagnielllo A. Ray K. Meyer R. (2003). Stepping to Success and Sustainability: An Analysis of Ontario’s Nursing Workforce. NHR,79-81.

27. Prociuk JL, Beetstra J. (1998). Workload/outcome measurement for hospital educators. Confirming the value of education. J Nurs Staff Dev, 14 (1), 7-15 .

28. Rosenfeld P, McEvoy MD, Glassman K. (2003). Measuring practice patterns among acute care nurse practitioners. J Nurs Adm,DM 33 (3), 159-165.

29. Sarnecki AJ, Haas S, Stevens K A, Willemsen J A. (1998). Design and Implementation of a Patient Classification System for Rehabilitation. J Nurs Manage, 28 (3), 35-43.

30. Seago J. (2002). Comparison of two patient classification instruments in an acute care hospital. J Nurs Adm, 32 (5), 243-249.

31. Seago JA. (2002). Nurse Staffing, Models of Care Delivery, and Interventions. University of California, San Francisco School of Nursing, 423-446.

32. Shaw A. (2003). Decision support systems can help hospitals reduce costs and improve patient care. Canadian Healthcare Technology November/December. Iinternet: http://www.canhealth.com/current%20issue.html

33. Simpson RL. (2000) In our hands? The future of health care technology. Nurs Manage, 31(12), 34-36.

34. Walsh E. (2003) Get real with workload measurement: this innovative technique accurately determines the hours needed for comprehensive patient care. Nurs Manage, 34 (2 part1 ), 38-42.



Reports and Studies 1. A report for the Mental Health Research and Development Strategy Nursing

Workload Measurement in Acute Mental Health Inpatient Units. Ministry of Health of New Zealand, February 2002.

2. A Report on The Nursing Strategy for Canada, Advisory Committee Health Delivery and Human Resources, 2003, Health Canada.

3. Canadian Institute for Health Information. Supply and Distribution of Registered Nurses in Canada, 1999. 2000, Ottawa, ON.

4. Canadian Institute for Health Information. Workforce Trends of Nurses in Canada, 2002. 2003, Ottawa, ON.

5. Canadian Nurses Advisory Committee. Our Health, Our Future: Creating Quality Workplaces for Canadian Nurses. Final Report 2002.

6. Canadian Nurses Association & Canadian Federation of Nurses Union Country Report for The International Council of Nurses Workforce Forum. Ottawa, Canada September 17-18, 2001.

7. Canadian Nurses Association & Office of Health and Information Highway. Vision 2020 Workshop on Information and Communications Technology in Health Care from the Perspective of the Nursing Profession. Ottawa, Ontario, March 21, 2000.

8. Canadian Nurses Association. Measuring Nurses’ Workload. Nursing Now Issues and Trends in Canadian Nursing March 2003, Number 15.

9. Canadian Nurses Association. What is Nursing Informatics and Why is it so Important? Nursing Now Issues and Trends in Canadian Nursing September 2001, Number 11.

10. Canadian Nurses Association. Health Informatics: Nursing Components. CNA Policy Statement, November 1993.

11. Canadian Nurses Association. Collecting Data to Reflect the Impact of Nursing Practice. CNA Position Statement, November 2001.

12. Canadian Nursing Informatics Association (CNIA) launches the national study “Educating tomorrow’s nurses: Where’s nursing informatics?” News Release, September 2002.

13. F/P/T Advisory Committee on Health Infostructure Blueprint and Tactical Plan for a pan-Canadian Health Infostructure. A Report on F/P/T Collaboration for the Planning of the Canadian Health Infostructure. Office of Health and the Information Highway. Health Canada, December 2000.

14. F/P/T Advisory Committee on Health Infostructure. Tactical Plan for a pan-Canadian Health Infostructure, 2001. Update Office of Health and the Information Highway. Health Canada, November 2001.

15. Faculty of Nursing, University of Toronto. A Study on the Impact of Nursing Staff Mix Modules and Organizational Change Strategies on Patient, System and Nurse Outcomes. Principal Investigators: McGillis Hall L & Irvine Doran D.



16. Future Development of Information to Support the Management of Nursing

Resources: Recommendations. Canadian Institute for Health Information, 2001.

17. Health and Community Services Human Resource Sector Study. Recruitment and Retention in the Health System: A Discussion Paper, 2002.

18. How Do Health Human Resources Policies and Practices Inhibit Change? A Plan for the Future. October 2002 Discussion Paper, Number 30, Tomblin Murphy G. O’Brien-Pallas L.

19. Information and Communications Technologies in the Canadian Health System: An Analysis of Federally-Funded ICT-Related Projects. Office of Health and the Information Highway, Health Canada, 2003.

20. Joint Commission on the Accreditation of Healthcare Organizations. 1994 Accreditation Manual for Hospitals. Volume I Standards. Oakbrook Terrace, IL: Joint Commission on the Accreditation of Healthcare Organizations; 1993.

21. MIS: Nursing Resource Consumption. Prepared by the Professional Advisory Working Group of the JPPC, August 1997.

22. Nursing Workload Measurement in Acute Mental Health Inpatient Units. A report for the Mental Health Research and Development Strategy. February 2002 Ministry of Health, Auckland New Zealand.

23. Ontario Guide to Case Costing, version 2.0. Ontario Case Costing Initiative, Chapter 9, Departmental Case Costing Standards, 2003.

List of companies that provide Nursing Workload Measurement Systems

BDM Information Systems

Cerner

Combeck Computer Design

Crystal Decisions

Delphic Medical Systems Ltd.

Dynaworks

Eclipsys

GRASP Systems

HBO & Company

Health Information Systems

HealthVISION Corporation

Healthware Technologies Inc.

Med2020

Meditech

NISS

Ormed Home Page



Per-Se - since bought out by Misys

Phoenix Solutions

Ulticare



APPENDIX B – WEB-BASED SURVEY DEMOGRAPHIC AND SURVEY QUESTIONS

DEMOGRAPHICS

WORKLOAD MEASUREMENT SURVEY

Directed at Nurse Managers

What best describes your area of employment: (choose only one of the following options - 1.1 to 1.12)

Facility-Based

1.1 Teaching Hospital

1.2 Community Hospital

1.3 Rehabilitation 1.4 Mental Health

1.5 Long-term Care

Community-Based

1.6 Home Care e.g. VON, St. Elizabeth’s, Centre Local de Services Communautaires (CLSC), Community Access Care Centre (CCAC), Red Cross

1.7 Public Health

1.8 Community Health Centre

1.9 Rehabilitation

1.10 Mental Health

1.11 District/Regional Health Authority

1.12 Other: (please specify) (input field)

Province/Territory you work in:

(drop down list)

Your professional designation is:

Registered Nurse (RN)

Registered Psychiatric Nurse (RPN)

Licensed/Registered Practical Nurse (LPN/RPN)

Approximate number of nursing staff in your organization: (choose one number for each type of staff)

RN <50 50-250 >250

RPN(Psych) <50 50-250 >250

LPN/RPN <50 50-250 >250

RN: Registered Nurse, RPN: Registered Psychiatric Nurse, LPN/RPN: Licensed/Registered Practical Nurse



Your present age is:

<25 years 31-35 41-45 51-55 >60

26-30 36-40 46-50 56-60

What is your gender:

Female

Male

What is your title within the organization: (choose one)

Patient/Client/Resident-Care Manager

Clinical Services Manager

Nurse Manager

Program Manager

Manager

Other (please specify) (input field)

How many years have you held your current title:

<1 year

1-3 years

4-6 years

> 6 years

What level of education have you completed: (select the highest)

Diploma

Certificate(RPN-Psych, LPN/RPN)

Baccalaureate

Masters

Ph.D.



Directed at Nurses

What best describes your area of employment: (choose only one of the following options - 1.1 to 1.12)

Facility-Based

1.1 Teaching Hospital

1.2 Community Hospital

1.3 Rehabilitation 1.4 Mental Health

1.5 Long-term Care

Community-Based

1.6 Home Care e.g. VON, St. Elizabeth’s, Centre Local de Services Communautaires (CLSC), Community Access Care Centre (CCAC), Red Cross

1.7 Public Health

1.8 Community Health Centre

1.9 Rehabilitation

1.10 Mental Health

1.11 District/Regional Health Authority

1.12 Other: (please specify) (input field)

Province/Territory you work in:

(drop down list)

What is your professional designation:

Registered Nurse (RN)

Registered Psychiatric Nurse (RPN)

Licensed/Registered Practical Nurse (LPN/RPN)

Approximate number of nursing staff in your organization: (choose one number for each type of staff)

RN <50 50-250 >250

RPN (Psych) <50 50-250 >250

LPN/RPN <50 50-250 >250

RN: Registered Nurse, RPN: Registered Psychiatric Nurse,

LPN/RPN: Licensed/Registered Practical Nurse

What is your present age:

<25 years 31-35 41-45 51-55 >60

26-30 36-40 46-50 56-60

What is your gender:



Female

Male

What is your title within the organization: (choose one)

Staff Nurse

Team Leader

Clinical Nurse Specialist

Other (please specify) (input field)

How many years have you held your current title:

<1 year

1-3 years

4-6 years

> 6 years

What level of education have you completed: (select the highest)

Diploma

Certificate( RPN-Psych, LPN/RPN)

Baccalaureate

Masters

Ph.D.



APPENDIX C – WEB-BASED SURVEY NOTICE OF INTENT, INTRODUCTORY LETTER

tice of intent:

The report was released by federal / provincial / territorial

ment and research) and students across the e in various activities. ALL

e encourage you to participate. Your voice is important.

No

Canadian Nurses Association, Canadian Practical Nurses Association, Registered Psychiatric Nurses of Canada, Canadian Association of Schools of Nursing, Canadian Federation of Nurses Unions, Canadian Health care Association, and Academy of Chief Executive Nurses are undertaking a collaborative project to facilitate the implementation of recommendations contained in the final report of the Canadian Nursing Advisory Committee (CNAC). governments in 2002. It includes 51 recommendations designed to create quality workplaces for nurses.

There has been some action in response to the majority of the CNAC recommendations. This new collaborative project, funded through Health Canada, will focus on those recommendations where there has been little or no action. The project includes literature reviews, surveys, focus groups and data analysis. Nurses in all communities of practice (clinical, education, managecountry will be invited (randomly chosen) to participatRESPONSES WILL BE KEPT CONFIDENTIAL.

W



eate es.

,

Practical Nurses Association, and

sur

s

r(ère)s Auxiliaires du Canada et les

, and regarding w nt tools you

o 10 minutes to complete.

All responses will be anonymous and kept

nir

e mesure de la charge de tra ou avez déjà

n e 5 à

Please participate. Your voice ous encourageons à participer. Ce que ortant.

et Canada

On behalf of the seven organizations/ Au nom des sept organismes

February 2004 Février 2004 Dear Colleague: The final report of the Canadian Nursing Advisory Committee (CNAC) included 51 recommendations designed to crhealthy workplace environments for nursSeven national organizations are collaborating to facilitate the implementation of a number of these recommendations. The seven are the Academy of Chief Executive Nurses, Canadian Association of Schools of NursingCanadian Federation of Nurses Unions, Canadian Health care Association, Canadian Nurses Association, Canadian

Chère collègue, cher collègue, Le rapport final du Comité consultatif canadien les soins infirmiers (CCSI) renfermait 51 recommandations en vue de la création de milieux de travail de qualité pour les infirmières. Sept organismes nationaux collaborent au processus visant à faciliter l’adoption d’un certainnombre de ces recommandations. Ces organismes sont : l’Academy of Chief Executive Nurses, l’Association canadienne des écoles de sciences infirmières, la Fédération canadienne des syndicats d’infirmières et d’infirmiers, l’Associationcanadienne des soins de santé, l’Association deinfirmières et infirmiers du Canada, l’Associationdes Infirmie

Registered Psychiatric Nurses of Canada.

As part of this project, we are seeking your input as a nurse manager with respect to competencies pertaining to your role

Infirmières/infirmiers psychiatriques autorisés du Canada. Dans le cadre de ce projet, nous aimerions obteles remarques qu’en votre qualité d’infirmière/infirmier gestionnaire vous pourriez faire au sujet des compétences se rapportant à votre rôle, ainsi qu’au sujet des outils d

orkload measuremeare using or have used. Please visit

http://209.217.65.7 between February 5 and February 19, 2004 to participate in a quick on-line survey thatshould take 5 t

confidential.

vail que vous utilisezutilisés. Veuillez consulter le site Web

http://209.217.65.8 entre le 5 et le 19 février 2004 pour participer à usondage en ligne qui ne devrait prendre qu10 minutes de votre temps. Toutes les réponses demeureront anonymes et confidentielles.

is important. Nous vvous avez à dire est imp

Thank you / Merci Lucille Auffrey, RN, MN/ inf., M. Sc. inf. Executive Director/Directrice générale Canadian Nurses Association/ Association des infirmièresinfirmiers du





APPENDIX E – WEB- SED SURVEY DAT TOOLS BA A

Managers Staff All

Count of NMD1 t of NMD1 nt of NMD1 Coun Couemploy employ employ

NMD1 employ 1 employ 1 employ Total NMD Total NMD Total

1.1 Teaching g Hosp 3 g Hosp 12 4Hosp 95 42%1.1 Teachin 3 48%1.1 Teachin 8 3%

1.2 Comm Hosp 48 21%1.2 Comm Hosp 9 13%1.2 Comm Hosp 57 19%

1.13 RHA 22 10%1.6 Other 5 7%1.13 RHA 25 8%

1.5 LTC 18 8%1.5 LTC 4 6%1.5 LTC 22 7%

1.7 Home Care 6%13 6%1.9 CHC 4 1.7 Home Care 16 5%

1.8 Public Health 4% ealth 11 5%1.13 RHA 3 1.8 Public H 14 5%

1.9 CHC 6 3% .4 Mental Health 3 4%1 1.9 CHC 10 3%

1.4 Mental Health 5 2% .7 Home Care 3 4% ealth 1 1.4 Mental H 8 3%

1.10 Comm ehab 3 1% .8 Public Health 3 4% 2%R 1 1.6 Other 6

1.11 Comm MH 3 1% hab 2 3% 3 1%1.3 Re 1.3 Rehab

1.3 Rehab 1 0%Grand Total 69 1.10 Comm Rehab 3 1%

1.6 Other 1 0% 3 1% 1.11 Comm MH

Grand Total tal 5226 Grand To 29

Managers Staff All

Count of D1 Prov Prov Prov Count of D1 Count of D1

D1 Prov Total Total Total D1 Prov D1 Prov

AB 68 30% 25% 29%AB 18 AB 86

ON 55 24% 18 25% 73 24%ON ON

BC 25 11% 11 15% 36 12%BC BC

NB 21 9% 1 15%NB 26 9%SK 1

MB 20 9%NB 5 7%NS 21 7%

NS 17 7%NS 4 6%MB 20 7%

SK 9 4%NL 2 3%SK 20 7%

NL 5 2%YT 2 3%NL 7 2%

NT 4 2% 1% 1%PE 1 NT 4

PE 3 1%Grand Total 72 PE 4 1%



QC 1 0% YT 3 1%

YT 1 0% QC 1 0%

Grand Total Grand Tota 301229 l

Managers AStaff ll

Count of title o itle C of titl C unt of t ount e

Title To Total Ti Total tal Title tle

Case Managers in rse

pe 6 %C l NuSpecialist 6 236 17%

Cl ical NuS cialist 11

linica rse %

Nurse Manager ta 43 %Ca ana 36 1376 35%S ff Nurse 75 se M ger %

Patient Care Manager der 8 %Nu an 76 2851 23%Team Lea 14 rse M ager %

Program ger 25% ra 57

Pa t CarManager 51 19%Mana 55 G nd Total

tien e

Grand Total 21 Program M 55 28 anager 0%

Staff Nurse 43 16%

Te Leader 8 3% am

Grand Total 275

Managers Staff All

Count of ProDesig Count of ProDesig Count of ProDesig

ProDesig Total ProDesig Total roD g T l P esi ota

RN 216 RN 65 92 N 281 9798% %R %

RPsychN 4 RPs hN 4 6 Psy N 8 32% yc %R ch %

Grand Total 220 LPN\RPN 2 3% PN\RPN 2 1L %

Grand Tot l 71 ran Tot 291a G d al

Managers Sta All ff

Count of Yrs current title

Count of current title

of Y curr nt tle

Yrs

Count rs eti

Yrs current title Total Yrs curren itle Total s curren Total t t Yr t title

6plus 89 40%6pl 35 51% lus 124 42%us 6p

4 to 6 56 1 to 13 19 o 69 23%25% 3 %4 t 6

1 to 3 52 4 t 13 19 to 6 223% o 6 %1 3 5 2%

Less t en 1 28 12%Less Then 1 8 12%Less Then 1 36 12%h

Grand Total 225 G nd T al 6 Grand To l 294 ra ot 9 ta



Managers Staff All

Count Count of nt of Age of Age Age Cou

Age Total Age Total Age Total

51-55 68 30 4 8 5 75 26%% 6-50 1 25% 1-55

46-50 55 25 4 7 46 73 25%% 1-45 1 24% -50

41-45 35 16 3 2 41 52 18%% 6-40 1 17% -45

56-60 24 11 3 8 36 33 11%% 1-35 11% -40

36-40 21 9 5 7 56 26 9%% 1-55 10% -60

31-35 9 4 2 5 7% 31 17 6%% 6-30 -35

26-30 6 3 0- 3% 26 11 4%% 25 2 -30

61pl 5 2 5 2 3% 61 5 2%us % 6-60 plus

Gra 3 Grand Total 1 0- 2 1%nd Total 22 7 25

Grand Tota 294 l

ince vinc

Managers

Count of ProvPe

ro

Employment Area AB BC MB NB NL NS NT ON PE QC SK YT Total

1.1 Teaching Hosp 3 1 9 1 7 29 1 0 7 1 95

1.2 Comm Hosp 9 5 6 4 5 1 1 1 1 4 6 8

1.13 RHA 9 3 1 2 2 2 1 2 22

1.5 LTC 7 4 1 1 1 3 1 1 8

1.7 Home Care 2 1 1 2 5 1 1 13

1.8 Public Health 1 2 2 1 3 1 1 11

1.9 CHC 1 1 4 6

1.4 Mental Health 3 1 1 5

1.10 Comm Rehab 1 1 1 3

1.11 Comm MH 1 1 1 3

1.3 Rehab 1 1

1.6 Other 1 1

Total 65 25 20 21 5 17 4 55 3 1 9 1 226

Staff

ince Count of Prov



Province

Employment Area AB BC NB NL NS ON PE SK YT Total

1.1 Teaching 10 5 1 2 13 2 33Hosp

1.2 Comm Hosp 1 2 1 2 2 9 1

1.6 Other 2 2 1 5

1.5 LTC 1 2 1 4

1.9 CHC 4 1 1 1 1

1.13 RHA 3 1 2

1.4 Mental Health 3 1 1 1

1.7 Home Care 1 2 3

1.8 Public Health 1 1 1 3

1.3 Rehab 1 2 1

Total 16 10 5 2 4 18 1 11 2 69

All (Mgrs&Staff)

Count of Prov D1 Prov

NMD1 employ AB BC M NB NL NS NT ON PE QC SK YT Total B

1.1 Teaching 40 1 10 1 9 42 3 128Hosp 2 11

1.2 Comm Hosp 10 7 6 5 5 18 1 3 2 57

1.13 RHA 2 2 2 1 4 259 4 1

1.5 LTC 8 4 3 1 1 1 1 3 22

1.7 Home Care 2 1 1 1 2 5 1 3 16

1.8 Publ Health 2 3 2 1 3 1 1 1 14ic

1.9 CHC 1 5 1 1 101 1

1.4 Mental Health 3 1 1 1 1 81

1.6 Other 1 2 63

1.10 Comm Rehab 31 1 1

1.11 Comm MH 1 1 3 1

1.3 Rehab 1 1 1 3

Total 81 5 26 7 21 4 73 4 20 3 2953 20 1

WMS by Prov Prov

HaveNWLMS AB M NB L NS T PE Q SK YT Total BC B N N ON C



Yes 60 3 17 2 11 6 11 1 1687 5

No 26 29 17 10 4 17 9 2 1339 5 4 1

Total 86 36 20 21 3 0 3 30126 7 4 7 4 1 2

WMS/Employ

HaveNWLMS

osp Co

os R Ae Teach

Hm p H H LTC Care

Hom

MenHealt

h HC th r

Pubc

h Rehab

t.

C O e

li

Healt Com MH

Comm Reh Total ab

Yes 91 32 12 8 6 4 4 3 3 2 1 1660

No 37 14 4 6 3 3 12925 13 10 11 1 2

Total 5 8 10 6 14 3 295128 7 25 22 16 3 3

79% .612.6

1% 2 3 4.74%95. 33 8% 3

8.42%6.32%4.2% 4. 1%3.16% .16%2.11% 1.05% 0.00% 17

08%

8 7

% % 8.33%.50% 6.25%

22.9217% 5% 268.75%

77.

52.0 %2 .08

%29.17 20.83 12

% 2.08% 4. 6.2

HaveNWLMS Hospitals

Non Hospit la

s Total

Yes 123 4 66 3 1

No 6 29 2 67 1

Total 185 5 110 29

% Yes of Total Resp 5 41.69% 14. 8%

% Total Hosp+Non % 9 Hosp 62.71 37.2 %

Managers Staff All

Count of HaveNWLMS

Count of HaveNWLMS

Count of HaveNWLMS

HaveNWLMS Total % HaveNWLMS Total % HaveNWLMS Total %

Yes 129 5

Yes 35 4

Yes 16856.3

% 8.6%

5.8%

No 100 4

% 5

% 4

% 3.7

No 37 1.4

No 1334.2

Grand Total 229 Grand Total 72 Grand Total 301



Managers Staff All

NWLMSType Count of

NWLMSType Count of

NWLMSType

NWLMSType Total % NWLMSType Total % NWLMSType Total %

GRASP 52 40.9

% GRASP 13 36.1

% GRASP 6539.9

%

QuadraMed\MI

QuadraMed\Medicus 46

36.2% n House Tool 11

30.6% edicus 49

30.1%

In House Tool 11 8. 7% NISS 6 16.7

% In House Tool 2213.5

%

Other Comm l Comm l 8. N S 14 6% ercia 9 7.1%

Other ercia 3 3% IS 8.

NISS 8 6. Q

8. 12 4% 3% uadraMed\M

edicus 3 3% Other

Commercial 7.

PRN 1 0. 0 8% Grand Total 36 PRN 1 .6%

Grand Total 127 Grand Total 163

Count of N

Count of N

Count of WLMSStyle WLMSStyle NWLMSStyle

NWLMSStyle Total % NWLMSStyle Total % NWLMSStyle Total %

Electronic 70 5

% 4 5

% 4.7

Electronic 19 8.7% Electronic 89

3.6

Paper Based 58 45 3

% 20 5 3

% 774

% .

Paper Based 1.

Paper Based 46.

G l 1 Gr al 39 Gr al 166rand Tota 28 and Tot and Tot

Count of NW re

Count of NW re

Count of NW re LMSNatu LMSNatu LMSNatu

NWLMSNature Total % NWLMSNature Total % NWLMSNature Total

Al y Balanced 24

1

Al y Balanced 12

3 Retrospective 87

5most Equall 9.4%

most Equall 0.8%

3.7%

Prospective 34 2

% 1 2

% 7.4

Prospective 5 2.8% Prospective 39

4.1

Re e 66 53 2

% Re e 21 5 8

% Almost ally

362

% trospectiv.

trospectiv3. Equ

Balanced 22.

Grand Total 124 38 162 Grand Total Grand Total



Managers Staff All

Count of NWLMSUsedFor NWLMSUsedFor NWLMSUsedFor

Count of Count of

NWL For TMSUsed otal % NWL For TotalMSUsed % NWL For TotMSUsed al %

Al s All s All s 33.3

% l Area 42 Area 14 41.2

% 35.0

% Area 56

Mo as Mo as Mo as 55.6

% st Are 70 st Are 14 41.2

% 52.5

% st Are 84

Some reas 1 11 1

% Some reas 1 6

Some 205

% . 7. 12.

% A 4 A 6 Areas

Grand Total 126 Grand Total 34 Grand Total 160

CountValida

Count Valida

CountValida

of Tools ted

of Tools ted

of Tools ted

Tools ted T Valida otal % To d ols Validate Total % T dools Validate Total %

Last Year 6 53.2

7 % Last Year 4 11.4 44.1

% % Last Year 71

L 2 1 Last2Years ast2Years 4 19.0

% Last2Years 1 31.4

% 3521.7

%

MoreThan2Years 35 27.

% MoreThan2Years57

% MoreThan2Years34.2

% 8

20 .1 55

Grand otal 12 Grand Total 35 Grand Total 161 T 6

Co w policies

Co

C ow

unt of follo unt of follow policy

ount of foll policy

Follow Policies Total % F y ollow polic Total % F y ollow polic Total %

A 37 5. ll Time 28.9

% All Time 2 9% 24.1

% All Time 39

M 757.

2 M e ost Time 3 0

% Most Time 2 64.7

% ost Tim 9558.6

%

Sometimes 18 14.1

% % % Sometimes 10 29.4

Sometimes 2817.3

Grand otal 12 Grand Total 34 Grand Total 162 T 8

C f C Comp

C f C Comp

C of e

Completing

ount oompliance

leting

ount oompliance

ount Complianc

leting

C Toompliance tal % Compliance Total % Compliance Total %



Managers Staff All Com Comp Completing leting pleting

Good 69 53.5

% Good 8 22.2

% Good 7746.7

%

Fair 51 39.

% Fair 23 % Fair 74 % 5 63.9 44.8

Poor 9 7.0% Poor 5 % Poor 14 8.5% 13.9

Grand Total 12 Grand tal 16 9 Grand Total 36 To 5

Count of Results

Are

Count of Results

Are Count of

Valid

Va d Total % Resu Total % Resu otal % li lts Are lts Are T

Very Valid 17 13. 2.7% 18 9%3% Very Valid 1 Very Valid 10.

Basi lid 42. Basi lid 43. Basi lid 42.cally Va 54 2% cally Va 16 2% cally Va 70 4%

Som lid Som lid Som lid ewhat Va 39 30.5% ewhat Va 12 32.4% ewhat Va 5130.9%

Not Valid 18 14.1% Not Valid 8 21.6% Not Valid 2615.8%

G 128 G 37 G 165 rand Total rand Total rand Total

Count of Reports Prov ed

Rout y Prov ed

Rout y Prov ed id

Count of inelid

Count of inelid

Re dports Provide Total % To l % tal % Routinely Provided ta

Routinely Provided To

Daily 20 17.4% 11 33.3% 7248.6%Daily Monthly

Weekly 10 8. 6 227% Weekly 2 .1% Quarterly 33 .3%

M y M y Donthl 66 57.4% onthl 6 18.2% aily 3120.9%

Quarterly 19 16.5% Quarterly 14 42.4% Weekly 12 8.1%

G G G 1rand Total 115 rand Total 33 rand Total 48

Count of How

Count of How

Count of How Used Used Used

How Used Total % Total % Total % How Used How Used

Analyse Benchmark 14 13 2 Me ry.0%

Analyse Benchmark 4 1 .1% et Mandato 6143.3%



Count of Valid

Count of Results

Are

Count of Results

Are

CA

CA Forecast affing

ompare And djust 11 10.2%

ompare And djust 3 9.1% St 2819.9%

Fore ForeA

B cast Staffing 22 20.4% cast Staffing 6 18.2%nalyse

enchmark 1812.8%

M ryeet Mandato 47 43 % M ry.5 eet Mandato 14 42 %Comp

9% .4are And

Adjust 14 9.

M % Mo el Scenari 2 6. % Monitor And

Adjust 13 9.2% odel Scenarios 5 4.6 d os 1

Monitor And Adjust 9 8.3% Adju 4 12.1% Model Scenarios 7 5.0%

Monitor And st

Grand Total 108 ran 33 Grand To al 1 G d Total t 14

Count of Support Count of Support Count of Support

Support Total % Suppor Total % t Total % Support

Generally 53 42.

GS 7

6239.0%Sufficient 7%

enerally ufficient 9 25. %

GenerallySufficient

O d Basis 43 34.7% mi Basis 48.6% n Limite asi 6037.7%n Limite On Li ted 17 O d B s

Not Really Available 28 22.6%

Not lly Ava le 9 25.7%

Not R ly Avail e 3723.3%

Reailab

ealabl

Grand Total 124 Grand otal 3 Grand tal 9 T 5 To 15

Count of Resources

Cou of Resources Pro d

Coun f Resour s Prov Provided

nt

vide

t oce

ided

Resources Provided Total %



Yes 39 6 17.6% Yes 528.7% 31.7% Yes 4

Somewhat 36 Somewhat 14 41.2% Somewhat 5031.8% 29.3%

Not Really 48 lly 14 ly 6239.5% 39.0% Not Rea 41.2% Not Real

Grand Total 12 Grand tal 157 3 Grand Total 34 To

Count of Dedicated Resources

Count of De ated Resources

d s

dicCount of

DedicateResource

Dedicated Total % Dedicated Total % Dedicated Total %



Count of Valid

Count of Results

Are

Count of Re ults s

Are Resources Resources Resourc s e

Yes 43 34.4% Yes 10 28.6% Yes 5333.1%

Some 38 Some 15 42.9% Some 33.1%30.4% 53

Not Really 44 35.2% Not Really 10 28.6% Not Really 5433.8%

Grand Total 125 o t Grand T tal 35 Grand To al 160

Count of HaveNWLMS

S otal Pe entage HaveNWLM T rc

Yes 168 55.81%

No 133 44.19%

Grand Total 301

NWLMSType/Validity Valid2 Per aceived V lidity

NWLMSType Very

lidBasically

ValidSomewhat

Va Not

ValGrand Total Va lid id

GRASP 4 23 2 6425 1

Quadra Med 3 2 27 16 48

In HouseTool 4 5 7 6 22

NISS 2 5 3 3 13

Other Commercial 5 2 1 3 11

PRN 1 1

Grand Total 0 9 18 65 5 26 15

NWLMSType/valid clusters Valid2 Valid

NWLMSType

ry And Basically Va

Somewha N

Ve

lid t Valid ot Valid Grand % Valid x

Type Total

GRASP 29 64 35.94% 23 12

Quadra Med 30 16 2 48 33.33%

In House Tool 9 7 6 22 31.82%

NISS 7 3 3 13 23.08%



Other Commercial 9.09% 7 1 3 11

PRN 1 0.00% 1


NWLMS/Count of How Used

How Used

NWLMSType

yse Benchma

rk

areAnd F t

Meet Mandato Model

Scenarios

Anal Comp

Adjust orecasStaffing ry

Monitor And

Adjust Grand Total

GRASP 4 2 9 28 4 6 53

Quadra Med 6 7 11 16 1 2 43

In House Tool 1 2 3 7 2 3 18

NISS 3 1 1 4 2 11

Other Commercial 3 104 2 1

PRN 1 1

Grand Total 18 14 26 58 7 13 136

13.24% 10 % 19. % 42.65% 5.15% 9.56% .29 12

Valid/How Used How Used

Valid2

An se Benchma

Com areAnd

A Forecast Staffing

MeetManda

ry

aly

rk

p

djust

to Model

Scenarios

Monitor And

Adjust Grand Total

Very and Basically 25 10 7910 10 20 4

Somewhat Valid 24 1 437 4 5 2

Not Valid 1 3 11 1 2 18

Grand Total 60 7 13 14018 14 28

12.86% 10.00% 20.00% 42.86% 5.00% 9.29%

Validity/Up to Date

Valid Last Year Last 2 Years

More Than 2 Years

Grand Total

Very Valid 13 3 2 18

Basically Valid 35 17 15 67

Somewhat Valid 18 12 20 50

Not Valid 4 3 18 25



Grand Total 70 35 60 55 1

43.75% 21.88% 34.38%

Validity/Up To Date

Tools Validated

Valid2 Last Year Last 2 Years

More Than 2 Years

Grand Total

Sum Very & Basically 48 20 17 85

Somewhat Valid 18 12 20 50

Not Valid 4 3 18 25


43.75% 21.88% 34.38%

Validity/Follow Policies Tool

Policies

Valid All Time Most TimeSometim

es Grand Total

Percentage

Very Valid 9 8 1 18 11.18%



Not Valid 1 13 12 26 16.15%


23.60% 59.01% 17.39%

Validity/Complete Tools Compliance

Valid Good Fair Poor Grand TotalPercentage

Very Valid 17 1 18 10.98%



Not Valid 5 12 9 26 15.85%


46.34% 45.12% 8.54%

NWLMSType/Nature NWLMSNature



Validity/Complete Tools Compliance

NWLMSType Almost Equally

Balanced ProspectiveRetrospective Grand Total

GRASP 13 14 36 63

In House Tool 3 6 12 21

NISS 6 2 6 14

Other Commercial 3 2 6 11

PRN 1 1

Quadra Med 10 15 23 48


22.15% 24.68% 53.16%

NWLMSStyle/Validity Valid

NWLMSStyle Very Valid Basically

Valid Somewhat

Valid Not Valid Grand Total

Electronic 10 41 23 13 87

Paper Based 8 26 28 13 75

Grand Total 18 67 51 26 162

11.11% 41.36% 31.48% 16.05%

NWLMSNature/Validity Valid

NWLMSNature Very Valid Basically

Valid Somewhat

Valid Not Valid Grand Total

Almost EquallyBalanced 3 19 6 8 36

Prospective 5 17 12 5 39

Retrospective 9 31 32 12 84

Grand Total 17 67 50 25 159

10.69% 42.14% 31.45% 15.72%



APPENDIX F – FOCUS GROUP DATA TABLE

Focus Group Participant Profile by Region Region Western Central Atlantic Totals

RN 7 14 8 29

LPN 9 4 42 55

RPsychN 17 17

Total 34 18 50 101

Area of Employment Count of Setting Employed

Setting Employed

Employed As LTC

Teaching Hosp

Regional Health Authority

Comm Hosp

Forensic Services/

Corr Inst

Comm Health/ Health Centre

Home Care

Rehab

Mental Health Centre

Other

Nursing Station

Grand Total

RN 3 8 1 4 1 3 2 2 24

LPN 21 10 12 7 1 1 3 55

RPsychN 2 2 3 5 1 3 1 17

Grand Total 26 20 13 14 7 4 3 3 3 2 1 96

Employment Sector by Region Count of Region by Sector Western Central Atlantic

Grand Total

Long-term Care/Nursing Home(LTC) 5 2 19 26


Regional Health authority 2 111 13


Forensic Services/

Correctional Institution 5 2 7

Community Health/ Health Centre 2 1 1 4



Home Care 1 1 1 3

Rehabilitation 3 3


Other 1 1 2

Nursing Station 1 1


Education Level Licensed/RPN Certificate 39

RPsychN – General Diploma 17

Licensed/RPN Diploma 16

Registered Nurse Diploma 9

Certificates 6

Post Registered Nurse Diploma 5

Bac in Nursing 5

Nurse Practitioner 2

Bac in Science of Mental Health 2

Grand Total 101

Years of Nursing Experience Years Nursing

0-5 5

6-10 12

11-20 24

21-30 30

31-40 19

41+ 2

Employment Status Permanent Full time 70

Permanent Part time 17

Term Part time contract 1



Casual 4

Grand Total 92



Area of Employment by Sector by Province Setting Employed BC Sask ON QC NB NS Grand Total

LTC 2 3 2 19 26

Teaching Hospital 1 5 5 1 3 5 20

District Regional Health 2 7 4 13

Comm Hospital 4 4 2 4 14

Forensic Services/Corr Inst 1 4 2 7

Comm Health/ Health Centre 1 1 1 1 4

Home Care 1 1 1 4

Rehabilitation 2 1 3


Other 1 1 2

Nursing Station 1 1

Grand Total 13 20 11 2 35 15 96

Employed as By Province

Employed As N.B. Sask NS. BC On. QC. Grand Total

RN 7 8 8 2 25

LPN 37 9 7 4 57

RPsychN 5 13 18

Grand Total 37 21 15 13 12 2 100

Respondents By Province Province Total %

NB. 37 36.63%

Sask 22 21.78%

NS. 15 14.85%

BC 13 12.87%

ON. 12 11.88%

QC. 2 1.98%

Grand Total 101



APPENDIX G – ABBREVIATIONS

Advisory Committee on Health Human Resources ACHHR

Advisory Committee on Health Infostructure ACHI

Agency for Healthcare Research and Quality AHRQ

American Nurses Association, ANA

Aboriginal Nurses Association of Canada ANAC

Canadian Association of Schools of Nursing CAUSN

Canadian Council on Health Services Accreditation CCHSA

clinical documentation CD

Canada Health Infostructure Partnerships Program CHIPP

Canadian Health Services Research Foundation CHSRF

Canadian Institute for Health Information CIHI

Canadian Institutes of Health Research CIHR

Case Mix Groups CMG

Canadian Nurses Association CNA

The Canadian Nursing Advisory Committee CNAC

Canadian Nurses Federation CNFU

Canadian Nursing Informatics Association CNIA

Canadian Practical Nurses Association CPNA

Canadian Union of Public Employees CUPE

District Health Council DHC

Day Procedure Groups DPG

Expected Length of Stay ELOS

Expert Nurse Estimation Patient Classification System ENEPCS

Electronic patient record EPR

Full Time Equivalent FTE

Health Infostructure Atlantic HAI

Heath Canada HC

Health Care Financing Administration HCFA

Health Informatics HI

Health Information: Nursing Components HI:NC

Health Infostructure Support Program HISP



Health Transition Fund – Health Canada HTF

International Council of Nurses ICN

information and communications technologies ICT

International Nursing Minimum Data Set I-NMDS

Inforoute Santé in Quebec IS

Joint Commission on Accreditation of Healthcare Organizations JCAHO

Joint Policy and Planning Committee JCCP

Licensed practical nurse LPN

Guidelines for Management Information in Canadian Health Care Facilities MIS

Ministry of Health MOH

Ministry of Health and Long-Term Care MOHLTC

National Ambulatory Care Reporting System NACRS

Nursing Computers Application Network NCAN

National Nursing Informatics Project NNIP

The National Nursing Workload Project NNWM

Nursing Professional Advisory Working Group NPAWG

Nursing Effectiveness, Utilization and Outcomes Unit NRU

Ontario Case Costing Initiative OCCI

Office of Health and Information Highway OHIH

Ontario Healthcare Reporting System OHRS

Ontario Nursing Informatics Group ONIG

Resource Intensity Weights RIW

Registered nurse, RN

Registered psychiatric nurse RPN

Resource Utilization Group Classification RUGS

Saskatchewan Health Information Network SHIN

University Health Network UHN

Western Health Information Collaborative WHIC

Workload Measurement System WMS



APPENDIX H – BIBLIOGRAPHY

Articles 1. Arthur T, James N. (1994). Determining nurse staffing levels: a critical review of the literature. J

Adv Nurs, 19 (3), 558-565.

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