The purpose of this research project was to determine the appropriate mix of nursing personnel on the day shift for a given workload. The top management team at the Milwaukee Veterans Affairs Medical Center (VAMC) has questioned nursing staffing methodologies for several years. Nurse executives have been unable to educate the top management team on nurse staffing methodologies because they lacked quantifiable data to support the nurse staffing methodologies. Recently, the Department of Veteran Affairs Central Nursing Office instituted a new staffing methodology, called the Expert Panel-Based Methodology. Since this methodology once again utilized subjective data and individual judgments, the top management team was uncertain of its validity. Empirical data was collected for the time studies and retrospective data was analyzed in order to obtain a historical perspective on admissions, discharges, transfers onto the unit, transfers off the unit, average census, and cardiac catheterizations. This data was placed into a statistical package, Stat: :Fit, and the appropriate distributions were assigned to each measurement. The flow of the nursing unit's day shift was modeled in the computer simulation program, MedModel 3.01. A separate program was developed for each day of the week. The researcher then varied the staffing and determined the appropriate nurse staffing mix for the average daily census at the beginning of the shift and the workload for each day of the week.

The purpose of this research project was to determine the appropriate mix of nursing personnel on the day shift for a given workload. The top management team at the Milwaukee Veterans Affairs Medical Center (VAMC) has questioned nursing staffing methodologies for several years. Nurse executives have been unable to educate the top management team on nurse staffing methodologies because they lacked quantifiable data to support the nurse staffing methodologies. Recently, the Department of Veteran Affairs Central Nursing Office instituted a new staffing methodology, called the Expert Panel-Based Methodology. Since this methodology once again utilized subjective data and individual judgments, the top management team was uncertain of its validity. Empirical data was collected for the time studies and retrospective data was analyzed in order to obtain a historical perspective on admissions, discharges, transfers onto the unit, transfers off the unit, average census, and cardiac catheterizations. This data was placed into a statistical package, Stat: :Fit, and the appropriate distributions were assigned to each measurement. The flow of the nursing unit's day shift was modeled in the computer simulation program, MedModel 3.01. A separate program was developed for each day of the week. The researcher then varied the staffing and determined the appropriate nurse staffing mix for the average daily census at the beginning of the shift and the workload for each day of the week.

The World Health Organization has estimated a worldwide shortage of around nine million nurses and midwives by 2030. In the United States, a shortage of registered nurses (RN)s is predicted to occur in several states by 2030. There is a gowning concern that this shortage may adversely impact the quality of nursing care and may lead to nurse burnout. To address this concern, hospitals have adopted several staffing strategies to effectively manage their scarce nursing resources. Traditionally, nurse-to-patient ratios have been used to staff inpatient care units, which specify the number of patients that can be safely supervised by a nurse. However, patients often require different levels of care based on the complexity of their medical conditions. Furthermore, not all care tasks need the support of highly trained RNs and thus, hospitals often employ nursing teams consisting of care givers at different skill levels for cost-saving purposes. The heterogeneity in patient mix and nursing skill mix can potentially render ratio-based staffing strategies ineffective. We propose to incorporate this heterogeneity into staffing decisions using stochastic modeling approaches. In particular, queueing theory and discrete-event simulation techniques are employed to investigate the potential impacts of patient acuity and staff heterogeneity on staffing needs of inpatient units. Moreover, the developed stochastic models are embedded in a multi-criteria optimization (MCO) framework to determine the optimal number of nursing teams and the corresponding staff configurations that yield the desired trade-off between different performance evaluation criteria measuring staffing cost, timely delivery of care, and nurse burnout. The proposed models can provide decision makers and staff planners with planning tools for safe and cost-effective staffing of inpatient care units.

The debate on how to appropriately staff nurses in the acute care setting is an ongoing one. However, with health care reform on the horizon, staffing to patient acuity must become a focus in the future of nursing in order to adequately meet the increasing demands on nursing, specifically in the Intensive Care areas. Patient acuity should be the key factor in determining staffing so that patients may receive the high quality care they deserve while maintaining desirable working environments for nurses. According to evidence-based research, nurse staffing levels in intensive care settings have a profound effect on patient satisfaction, nurse satisfaction, health care expenses, and patient outcomes. Reassessing current staffing models set in place for these patient care areas is a crucial step towards improving patient outcomes, increasing nurse retention rates, and enhancing the financial well-being of health care organizations. The problem with under staffing, or staffing to ratios, is the lack of consideration for patient acuity. As a result patient care is being jeopardized, often leading to higher unexpected health care costs/ expenses. Nurses are being stretched to the limit and forced to leave numerous nursing tasks undone, which at times can lead to patient injury, causing longer hospital stays, increased incidences of hospital readmissions and patient mortality. While health care administrators are being pressured to reduce costs/ expenses as a result of health care reform, staffing to acuity can actually help contribute to these expectations on numerous levels in terms costs reduction. Acuity-based staffing has proven to help decrease patient mortality rates, be more cost-effective than staffing to ratios in regards to patient outcomes, and increase nurse satisfaction and retention. With health care reform on the horizon, health care administrators have no choice but to reassess staffing models to meet patient needs, retain nurses and gain the financial outcomes they desire.

The purpose of this proposal is to examine the association between patient to nurse staffing ratios and patient outcomes, in hospitalized patients, in the acute care environment. After identifying the patient care issue related to the absence of mandatory nursing staffing ratios, fifteen evidence-based articles were reviewed to determine a solution. The use of mandatory nurse staffing ratios, based on unit specific acuity, was determined to be an evidence-based solution, and an implementation plan was created. The implementation plan integrated the use of mandatory patient to nurse staffing ratios throughout a 339 bed acute care facility over a six month trial period. An evaluation plan was formed to review results of the implementation plan, as well as to provide information to disseminate to key stakeholders, and the greater nursing community. The findings provide evidence that mandated patient to nurse staffing ratios, in comparison with no mandated patient to nurse staffing ratios, result in fewer incidences of inpatient mortality, pressure ulcers, failure to rescue, patient falls, pneumonia, urinary tract infections, and blood stream infections in hospitalized patients, in the acute care setting.

" This is the first research-based book to confront workplace issues facing nurses who have disabilities. It not only examines in depth their experiences, roadblocks to successful employment, and misperceptions surrounding them, but also provides viable solutions for creating positive attitudes towards them and a welcoming work environment that fosters hiring and retention. From the perspectives and actual voices of nurses with disabilities, nurse leaders, nurse administrators, and patients, the book identifies nurses with disabilities (including sensory, musculoskeletal, emotional, and mental health issues), discusses why they choose to leave nursing or hide their disabilities, and analyzes how their disabilities may influence career choices. "