Measuring nursing essential contributions to quality patient care outcomes.
Subject: Decision-making (Analysis)
Nursing (Management)
Medical care (Quality management)
Medical care (Analysis)
Authors: Wolgast, Kelly A.
Taylor, Katherine
Garcia, Dawn
Watkins, Miko
Pub Date: 10/01/2011
Publication: Name: U.S. Army Medical Department Journal Publisher: U.S. Army Medical Department Center & School Audience: Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2011 U.S. Army Medical Department Center & School ISSN: 1524-0436
Issue: Date: Oct-Dec, 2011
Topic: Event Code: 200 Management dynamics Computer Subject: Company business management
Geographic: Geographic Scope: United States Geographic Code: 1USA United States
Accession Number: 274955766
Full Text: INTRODUCTION

Ensuring available personnel resources for quality patient care is a major responsibility of healthcare leaders in all facets of healthcare. In order to appropriately manage these resources, leaders require accurate, timely information, especially in today's world where budgeting for healthcare is on the forefront of our minds. Patient classification systems assist nurse leaders in ensuring that they have the acuity based tools in place to match patient care delivery to demand management. This process and system provides leaders with valid tools that assist with improvement of patient outcomes, proper staffing, budgeting and cost containment and nurse retention.

CURRENT ARMY NURSING WORKLOAD PRACTICES

Army healthcare leaders face resource constraints similar to the civilian healthcare environment. Current issues facing the Army Medical Department (AMEDD) include ensuring adequate nurse staffing to patients within the constraints of a deployed workforce, an aging population, a nursing shortage, and rising healthcare costs. Efforts are underway to improve our leadership's ability to provide valid, reliable, quality data for the practice of nursing while balancing efficiency, all the while delivering quality patient care. Ongoing discussions regarding the use of nurse-to-patient ratios to assess the adequacy of the nursing support for the care of the patients are among many of the issues being researched. Systems such as Workload Management System for Nursing Internet (WMSNi) will be critical to the Army as a development strategy to determine more accurate measures. Among those measures is the ability to account for the various factors that contribute to resourcing, such as patient acuity and complexity, workload, and staffing mix. In particular, the visibility of workload to key leaders and front line clinicians will influence workload distribution, skill mix decisions, patient assignment, and staffing flexibility.

HISTORICAL BACKGROUND

The original patient classification study was conducted in 1955 by Ester Claussen (1) at the Walter Reed Army Medical Center and resulted in a "Nine Category Scale of Patient Needs." Subsequently, the Army made several quantitative adaptations and prototype changes to the model leading up to 1985, when a joint effort of the US Army Nurse Corps and the US Navy Nurse Corps was established. Through this effort, the Workload Management System for Nursing (WMSN) was developed to establish a patient classification system that would capture nursing workload based on patient acuity and provide guidelines for effective and efficient allocation and utilization of nursing personnel. In December 1986, the Army Manpower Requirements and Documentation Agency approved the incorporation of the WMSN into the Manpower Staffing Standards System, now known as the Automated Staffing Assessment Model Inpatient module. To populate the model, information is generated by WMSN and used to determine manpower requirements for inpatient nursing units Army-wide. (2,3)

REVALIDATION PROCESS

Since its inception, WMSN has been used to measure patient acuity and the complexity of care, and to establish manpower requirements for inpatient nursing units. (2) However, concerns arose that WMSN had lost its validity, since the critical indicators no longer reflected current inpatient work practices. With changes in inpatient work practices, technological advances, regulatory requirements, and the need to capture all sources of workload, the nursing care hours associated with each critical indicator required updating and revalidation. An Army team made up of a nurse researcher, informatics nurse-to-nurse methods analysts, and additional staff from each of the locations visited, began the process of revalidating each of the critical indicators and ensuring compliance with current practice standards. This initial phase involved a frequency analysis to look at all 99 critical indicators at a total of 26 medical treatment facilities (MTFs). The information collected led to current clinical model revisions, resulting in consolidation of some indicators and removal of others.

The next phase included a series of time and motion studies to establish current minimal essential mean times for each direct nursing care activity. Timed observations were completed on 60 direct care activities generating 3,241 time measurements. These data were analyzed to assess the overall mean times for each of the activities and processes. The resulting patient classification system measures the direct nursing care activities and combines it with the appropriate indirect care activities to determine the best mix and skill level of care providers for the critical care, medical/surgical, obstetric, psychiatric, neonatal, and pediatric clinical services. Figure 1 illustrates changes in direct care times as a result of the studies.

WMSNI: A FOURTH GENERATION PATIENT CLASSIFICATION SYSTEM

Patient classification systems (PCSs) were originally adapted from industrial time and motion models. Prior to the 1980s, first generation systems were based on manual calculations originating from historic performance ratios used to estimate staffing needs and budgets. Second generation systems were designed to focus on patient care as accrediting organizations such as the Joint Commission mandated their use. By the 1990s, third generation patient classification systems were critical due to shorter hospital stays and less stationary staff. Fourth generation systems now focus on prospective modeling, evolving to provide real-time matching of caregiver skill profiles to meet staffing needs for the current and upcoming shifts. (4)

The 2 general types of systems commonly used are the summative task type and the critical incident or criterion type PCS. Summative tasks PCSs usually appear comprehensive because they list major tasks, and tend to be easier to design and code in order to interface with automated documentation systems. However, criterion PCSs can be more easily adapted to the organization. (4) Despite the modality selected, benefits include data-driven decision-making, standardized clinical documentation which supports evidence-based medicine, and easier integration into staffing and scheduling systems. (5) Clinical officers-in-charge can then compare scheduled staff to required staff specified by patient classification system, and then reallocate staff or call in additional staff to accommodate over- or understaffed units, ensuring appropriate mix of skill levels. Ultimately, patient classification systems provide improved staffing accuracy, streamlined workflow, and enhanced patient outcomes.

The WMSNi is a summative task PCS that expresses patient care requirements as the sum of the frequency of tasks. The WMSN-A Refresh project was begun in April 2009 and reached full operating capacity in July 2010. Currently it is used by over 6,500 caregivers on a daily basis. Figure 2 presents its current operational view. It is an interim solution to ensure the maintenance of the basic functionalities that are needed for enterprise level capability.

The "refresh" has given the application a modern-day format as well as many new and convenient features. Previously calculated through a subsystem, workload management for postanesthesia care and labor and delivery units are now fully integrated into the WMSNi, as illustrated in Figure 3. Formerly a decentralized managed system at 26 MTFs, WMSNi is now a centralized, web-based application with a contemporary user interface. Data can be entered through easy-to-use features, adding and retrieving information are as simple as clicking a button, and information entered is immediately employable.

STAFF FEEDBACK

To assess the users' perceptions of the legacy WMSN and WMSNi respectively, an electronic survey was created using the AMEDD Center and School Survey Application Tool. The survey was administered at preimplementation, and approximately 60 days postimplementation. The preimplementation survey was completed by 105 participants, while 47 participants completed the postimplementation survey.

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As shown in the Table, 8 of the 11 questions solicited opinions of the systems using a 5-point Likert rating scale. Three questions were strictly demographic in nature, addressing the length of nursing experience, where they worked and whether they were military, GS civilian, or contract staff.

Preimplementation survey--Forty-eight nurses (46%) had more than 6 years of nursing experience in one of the various nursing specialties, the distribution of which is shown in Figure 4.

Significantly, 70 participants (66%) strongly agreed or agreed that the legacy WMSN was not optimal nor did it meet the their needs. Forty-five participants (43%) rated WMSN as frustrating and considered it not an effective way to document the inpatient clinical workload data. Figure 5 presents the survey results.

Postimplementation survey--The same survey was given as the postimplementation survey. Twenty participants (43%) strongly agreed or agreed that the WMSNi was much easier to use and that it met their needs. Twenty-eight participants (60%) strongly agreed or agreed that WMSNi provides an effective way to document the inpatient clinical workload data. Figure 6 presents the survey results.

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The surveys also helped to identify any high level user requirements that were not previously identified for future enhancements to WMSNi. For example, the responses indicated that Behavioral Health's workload was not fully identified. Other survey participants commented on the technical issues, frequent downtimes, speed of application, and the potential that WMSNi could provide them, especially with staff scheduling and 24-hour report modules.

WORKLOAD AND STAFFING

As requests for scheduling and 24-hour report capabilities increase, Army leaders recognize the critical requirement to more objectively make assignments for increased staffing equity and patient safety. Recent studies indicate that more efficient use of staff was associated with lower hospital-related mortality, failure to rescue, and other patient outcomes. (6) Balancing clinical workload with nurse staffing was also associated with better outcomes in intensive care units and among surgical patients.

Several studies also found a significant relationship between lower nurse staffing levels and higher rates of pneumonia. For example, a multisite study in California found that an increase of one hour worked by RNs per patient day correlated with an 8.9% decrease in the odds of pneumonia among surgical patients. (6) Another study found a significant relationship between full-time-equivalent RNs per adjusted inpatient day and rate of pneumonia--the rate of pneumonia was higher with fewer nurses. (6) However, other studies have not confirmed these findings. For example, there is conflicting evidence regarding the impact of nurse staffing levels on pneumonia. (6,7) As workload is affected by more than just staffing levels, a deeper understanding of nursing workload is required to better assess the impact of workload on patient outcomes.

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Developing a schedule that satisfies staffing needs and individual needs of nurses, while at the same time satisfying workload and training requirements is no easy task without a standardized and centrally managed scheduling system that is adaptable to the organization's needs. In planning for the future, Army leaders must consider fulfilling staffing and workload requirements a priority. Communication must be fostered between all levels of supervisors and staff concerning scheduling in order to foster a positive work environment, avoid burnout, increase morale, lower absenteeism, and lessen turnover.

The management of nursing contract dollars is also varied across the Army Medical Command. Several tools, including MS Excel, are used to understand and manage the large amount of personnel services dollars used. Based on current systems, it is challenging to obtain a detailed view of contract utilization. Capabilities must be identified to ensure more efficient utilization. There are clear opportunities to better manage contract dollars and improve understanding of where the dollars are spent and where we can maximize our potential cost savings.

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HOW THE PATIENT CLASSIFICATION SYSTEM SUPPORTS DECISION MAKING

Evidence-based practice uses the best clinical knowledge to determine interventions and clinical actions developed through the rigor of sound research to assure the best possible clinical outcomes for patients. Similar expectations of our administrative decision making are necessary. Multimillion dollar decisions are made every day in our organizations. Is evidence beyond the anecdotal experience of the leaders introduced into these decisions? Do we apply the same standard to both clinical and administrative decision making, as well as to address adverse outcomes from poor decision making? Developing staffing plans, adhering to standards, and evaluating the effectiveness of the staff are challenges facing leaders every day.

Because the nursing workforce accounts for the largest cost of care in every hospital, achieving and sustaining efficiency is critical to managing the delivery of high quality patient care. All of this is dependent on timely, accurate data. In the past, getting timely access to the data was challenging. Often a period of 45 to 90 days was necessary to see any data from WMSN. The data at the medical treatment facility was maintained in a system that was difficult to access, or only on paper. This made the data difficult to synthesize and analyze. With the implementation of WMSNi, the application provides decision makers with real-time, multidimensional reporting analysis capabilities. Several reporting features allow users to analyze trends, then devise and implement staffing strategies more quickly than ever before. Figure 7 illustrates one of many strategic reports available within WMSNi to assist leaders with more visibility into their operations.

Moreover, the Army Nurse Corps has developed a standardized nursing practice model for inpatient and outpatient settings which incorporates nationally recognized standards from professional nursing organizations. This has led to the need to change the skill mix for staffing.

Effective clinical workload management for inpatient nursing requires continual review of past work accomplished to evaluate the performance, effectiveness, and efficiency of the organization, and identify improvements to work processes. Projected clinical workload data is currently being provided using WMSNi to determine the manpower and financial requirements at all medical treatment facilities. Clinical work must be validated in order to be resourced for appropriate support for all Warfighters and their families.

CONCLUSION

The WMSNi is a core AMEDD business system that provides real-time, comprehensive nursing workload and manpower data for decision making at all levels. The WMSNi's expanding missions and approved requirements support strategic initiatives for the Army Medical Command balanced scorecard (defined on page 25), and multiple personnel and manpower business processes. In the future should be the creation of a validated predictive model leveraging WMSNi that is intuitive to users, with strong statistics and clinical analytics, which uses other objective patient data, such as surgery day and type, diagnosis related group, length of stay, etc. Future projects will include consolidation of inpatient and outpatient workload data in a centralized location. The system would enable standardized business practices through automation and seamless report delivery with multiple interfaces, including census and workload information, with acuity, complexity, and staffing recommendations. The desired results include a self-schedule module to communicate with the Defense Medical Human Resources System Internet and WMSNi. Benefits will include improved morale of staff members, decreased turnover and absenteeism, improved ability to manage contract dollars, and improved dispersion of clinical staff to ensure safe staffing based on workload expertise and skill mix. Upgrading WMSNi is essential in order to reflect current clinical practice and streamline operations, reduce contract labor expense for interim nursing staff, lessen staff overtime, decrease patient length of stays, and minimize staffing turnover. The proposed solution will provide well-organized and accurate patient classification, improved patient outcomes, more precise and effective forecasting, and enhanced analysis of patient care requirements.

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REFERENCES

(1.) Claussen E. Categorization of patients according to nursing care needs. Mil Med. 1955;116,(3):209-214.

(2.) Field Manual 8-5-01: Workload Management System for Nursing. Washington, DC: US Dept of the Army; 1991 [cancelled, not in effect].

(3.) Army Regulation 570-4: Manpower Management. Washington, DC: US Dept of the Army; February 8, 2006.

(4.) Harper K, McCully C. Acuity systems dialogue and patient classification system essentials. Nurs Adm Q. 2007;31

(4):284-299.

(5.) Van den Heede K, Diya L, Lesaffre E, Vleugels A, Sermeus W. Benchmarking nurse staffing levels: the development of a nationwide feedback tool. J Adv Nurs. 2008;63(6):607-618.

(6.) Carayon P, Gurses A. Nursing workload and patient safety- a human factors engineering perspective. In: Hughes RG, ed. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville, MD: Agency for Healthcare Research and Quality; 2008:1-14

(7.) Brady A, Bryne G, Horan P, Griffiths C, Macgregor C, Begley C. Measuring the workload of community nurses in Ireland: a review of workload measurement systems. J Nurs Manag. 2007;15:481-489.

COL (Ret) Kelly A. Wolgast, AN, USA

LTC Katherine Taylor, AN, USA

LTC Dawn Garcia, AN, USA

LTC Miko Watkins, AN, USA

When this article was written, COL Wolgast (Ret) was the MEDCOM Chief Nurse Executive and Functional Proponent, Headquarters MEDCOM, Fort Sam Houston, Texas.

LTC Taylor is the MEDCOM Nurse Acquisition Officer and WMSNi Program Manager, Headquarters MEDCOM, Fort Sam Houston, Texas.

LTC Garcia is the Consultant Nursing Methods Analyst to the Office of The Surgeon General, Headquarters MEDCOM, Fort Sam Houston, Texas.

LTC Watkins is the Chief Medical Information Officer, Pacific Regional Medical Command and Tripler Army Medical Center, Honolulu, Hawaii.
Questions used in the pre- and postimplementation survey of
users' opinions of the WMSN and WMSNi systems.

1. How many years of nursing experience do you have?

2. Where do you work (area of specialty)?

3. Are you military, GS civilian, or contract staff?

Indicate your opinion of each of the following statements on a five
point (Likert) scale: 1=strongly agree, 2 = agree, 3 = neither
agree nor disagree, 4=disagree, 5=strongly disagree

4. Overall the system is wonderful and meets all of my needs.

5. Overall the legacy system is frustrating and does not meet my
needs.

6. The WMSN system is easy to use.

7. It was difficult to learn how to operate the WMSN system.

8. The system allows me to perform required tasks in a
straightforward manner.

9. The system provides an effective way to document the inpatient
clinical workload data.

10. The system provides accurate patient acuity data.

11. The system has reporting options available to me.
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