Model to estimate the nursing workload required by trauma victims on intensive care unit discharge.

AIM: To validate a model to estimate the nursing workload required by trauma victims on intensive care unit (ICU) discharge. BACKGROUND: Identifying a reliable model of nursing workload measurement that can assist professionals. METHODS: A prospective cohort study conducted in 2010 (first phase) and 2015 (second phase) with 342 trauma victims admitted into the ICU of four hospitals located in São Paulo, Brazil. The original model was created during the first phase and included the Simplified Acute Physiology Score and New Injury Severity Score variables. Coefficients of determination (R2 ) were calculated to identify the reliability. RESULTS: The original model presented high reliability (R2  = 44%) in the hospital of origin, and unsatisfactory performance (R2  < 4%) in the other institutions. An improvement in R2 was observed after adjusting the coefficients for each hospital. CONCLUSION: For the Nursing Activities Score prediction of survivors for ICU discharge, trauma centres must validate the original model with coefficient adjustments for their population, or preferably derive their own models. IMPLICATIONS FOR NURSING MANAGEMENT: Although models are useful in predicting discharge of these ICU patients, for treatment continuity after critical care and for organising services, the study showed that they should be evaluated prior to use for nursing management.

Objective measures of workload in healthcare: a narrative review.

PURPOSE: Workload is a critical concept in the evaluation of performance and quality in healthcare systems, but its definition relies on the perspective (e.g. individual clinician-level vs unit-level workload) and type of available metrics (e.g. objective vs subjective measures). The purpose of this paper is to provide an overview of objective measures of workload associated with direct care delivery in tertiary healthcare settings, with a focus on measures that can be obtained from electronic records to inform operationalization of workload measurement. DESIGN/METHODOLOGY/APPROACH: Relevant papers published between January 2008 and July 2018 were identified through a search in Pubmed and Compendex databases using the Sample, Phenomenon of Interest, Design, Evaluation, Research Type framework. Identified measures were classified into four levels of workload: task, patient, clinician and unit. FINDINGS: Of 30 papers reviewed, 9 used task-level metrics, 14 used patient-level metrics, 7 used clinician-level metrics and 20 used unit-level metrics. Key objective measures of workload include: patient turnover (n=9), volume of patients (n=6), acuity (n=6), nurse-to-patient ratios (n=5) and direct care time (n=5). Several methods for operationalization of these metrics into measurement tools were identified. ORIGINALITY/VALUE: This review highlights the key objective workload measures available in electronic records that can be utilized to develop an operational approach for quantifying workload. Insights gained from this review can inform the design of processes to track workload and mitigate the effects of increased workload on patient outcomes and clinician performance.

Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload.

Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.

Developing a caseload classification tool for community nursing.

Acuity and dependency in the community nursing caseload in combination with safe staffing levels are a national issue of concern. Current evidence suggests that there are no clear approaches to determining staff capacity and skill mix in these community settings. As community nursing caseloads are large with differing complexities, there is a need to allocate community nursing with the best skill mix to achieve the best patient outcomes. A city-wide service improvement initiative developed a tool to classify and categorise patient demand and this was linked to an electronic patient record system. The aim was to formulate an effective management response to different levels of acuity and dependency within community nursing teams and a consensus approach was used to allow the definition of complexity for twelve packages of care. The tool was piloted by a group of community nurses to assess the validity as a method to achieve a caseload classification. Seventy nurses were trained and applied the tool to 3000 patient referrals. Based on this, standards of care were agreed including expectations of assessment, intervention, visit length and frequency. Community nursing caseloads can now be organised according to acuity and complexity of patient need, which determines allocation of staff and skill mix.

A nursing care classification system for assessing workload and determining optimal nurse staffing in a teaching hospital in China: A pre-post intervention study.

We aimed to evaluate the effectiveness of a nursing care classification system for re-assessing nurse workload and determining staffing needs. Adequate bed-nurse ratios help manage hospital cost-efficiency, quality of care and patient safety. A prospective pre-post intervention study was conducted from January 2010 to December 2012 in 16 medical-surgical units of a tertiary teaching hospital. Nursing tasks were classified into four grades of care reflecting actual workload. Units were re-staffed accordingly and bed-nurse ratios compared with government-authorized bed-nurse ratios. Patient satisfaction, hospital stays and mortality were evaluated pre- and poststaffing changes. Average bed-nurse ratio (1:0.41) exceeded the national standard (1:0.40) in 16 units, but was inadequate in five units. Re-staffing increased average bed-nurse ratio from 1:0.41 to 1:0.48. Patients' satisfaction increased from 96.9% to 97.6%, and hospital stays decreased significantly. Nursing care classification effectively distributes nurse staffing to match patients' care levels, improving patient outcomes.

[Catalogues of therapeutic nursing activities in neurological early rehabilitation]. / Leistungskataloge der aktivierend-therapeutischen Pflege in der neurologischen Frührehabilitation.

Under the German DRG-system, hospital-based rehabilitation of still critically ill patients becomes increasingly important. The code for early neurological rehabilitation in the DRG-system's (Diagnosis Related Groups) list of operations and procedures requires an average daily therapeutic intensity of 300 min, part of which is being contributed by therapeutic nursing. As therapeutic aspects are integrated in other nursing activities, it is difficult to separate its time consumption. This problem is pragmatically resolved by catalogues of therapeutic nursing activities which assign plausible amounts of therapeutic minutes to each activity. The 4 catalogues that are used most often are described and compared. Nursing science has not focused yet on therapeutic nursing.

Nurse Fatigue and Shift Length: A Pilot Study.

Increasing emphasis on patient quality and concerns about the impact of health care worker fatigue has stimulated efforts for leaders to address patient quality and caregiver satisfaction. Shift length has been associated with nurse fatigue and has become a growing concern in the United States with the routine shift length of 12 hours. In this project, shift lengths from 12 hours to 8 hours for a 4-week period to evaluate fatigue levels associated with 12-hour and 8-hour shifts. Lessons learned from this experience: nurses are agreeable to try a proposed change, numerous ideas should be tried to develop additional innovative solutions to the issue of nurse fatigue, and nurses may not want to work 5 days per week.

[Expectations, requirements and limitations of future task sharing between the nursing profession and the medical profession: results from the Care-N Study M-V]. / Erwartungen, Wünsche und Grenzen der künftigen Aufgabenteilung von Pflege und Medizin - Ergebnisse der Care-N Study M-V.

BACKGROUND: The aging population causes a sustained increase in demand of medical and nursing care services. At the same time health care professionals are aging, too. This leads to a growing number of health care gaps. Therefore, the health care system needs to be reformed. This includes a reallocation of task between some of the health care professions. AIMS: This article addresses developments, potentials and limitations in the context of the future allocation of tasks between the nursing and the medical profession. Aim is to specify the future task sharing between nurses and physicians regarding expectations, requirements and limitations. METHODS: We conducted questionnaire based Delphi interviews with an interdisciplinary group of experts. (type aggregation of ideas). RESULTS: In the future, to expert's point of view, nurses will take over routine tasks in the medical and nursing health care supply. Task sharing by substitution is regarded with skepticism by experts. It requires a long time perspective and an early involvement of all stakeholders. CONCLUSIONS: Germany is at the beginning of the process of the future task sharing between nurses and physicians. The realization requires a comprehensive political support and further development of concepts including scientific implementation and evaluation.

[Nursing workload in a coronary unit according to the Nursing Activities Score]. / Carga de trabalho em unidade coronariana Segundo o Nursing Activities Score.

OBJECTIVE: this study aimed to using the Nursing Activities Score to assess nursing workload in a coronary care unit, to assess the distribution of workload between shifts, and to compare the current staff of the care unit with that recommended by the instrument. METHOD: this was a longitudinal study, conducted in a teaching hospital in Southern Brazil, between April to June 2012. RESULTS: A total of 604 NAS measures were obtained from the 61 patients included. The mean workload per shift was 47% (±12), with the greatest workload being reported in the afternoon shifts. CONCLUSION: according to the NAS, a mean of two and a maximum of 2.4 nursing professionals would be required per shift to meet all patient demands, suggesting that the current staff size in the CCU is adequate. The NAS was successful in assessing nursing workload and changes in patient demands over time.

[The validity of the "Working Group on Nursing in Early Rehabilitation" (AGnFP) catalogue]. / Dokumentation von Leistungen der therapeutisch-aktivierenden Pflege in der neurologisch-neurochirurgischen Frührehabilitationmit Hilfe des Katalogs der niedersächsischen "Arbeitsgemeinschaftneurologische Frührehabilitationspflege" (AGnFP).

INTRODUCTION: Nursing is most important in neurological early rehabilitation to achieve a good outcome. In the present study, the validity of the catalogue of the "Working Group on Nursing in Early Rehabilitation" (AGnFP) has been examined. METHODS: 54 neurological early rehabilitation patients (mean age 68.3 (14.7) years) have been enrolled. All nursing processes (basic and medical care) have been documented through timekeeping. RESULTS: A nursing total of 205.9 (122.6) min/d per patient was found. In the AGnFP-catalogue, 177.5 (130.9) min/d have been documented (86.2% of all nursing processes). Barthel-index correlated negatively with basic nursing care (r = − 0.42, p < 0.01). The early rehabilitation index showed a negative correlation with medical nursing processes (r = − 0.46, p < 0.01). The AGnFP catalogue correlated with basic nursing processes (r = 0.69, p < 0.001). DISCUSSION: The AGnFP-catalogue is a tool to document nursing in early neurological rehabilitation. Further studies are strongly encouraged.

The RAFAELA system: a workforce planning tool for nurse staffing and human resource management.

The RAFAELA system was developed in Finland during the 1990s to help with the systematic and daily measurement of nursing intensity (NI) and allocation of nursing staff. The system has now been rolled out across almost all hospitals in Finland, and implementation has started elsewhere in Europe and Asia. This article describes the system, which aims to uphold staffing levels in accordance with patients' care needs, and its structure, which consists of three parts: the Oulu Patient Classification instrument; registration of available nursing resources; and the Professional Assessment of Optimal Nursing Care Intensity Level method, as an alternative to classical time studies. The article also highlights the benefits of using a systematic measurement of NI.

Cognitive workload classification of law enforcement officers using physiological responses.

Motor vehicle crashes (MVCs) are a leading cause of death for law enforcement officers (LEOs) in the U.S. LEOs and more specifically novice LEOs (nLEOs) are susceptible to high cognitive workload while driving which can lead to fatal MVCs. The objective of this study was to develop a machine learning algorithm (MLA) that can estimate cognitive workload of LEOs while performing secondary tasks in a patrol vehicle. A ride-along study was conducted with 24 nLEOs. Participants performed their normal patrol operations while their physiological responses such as heartrate, eye movement, and galvanic skin response were recorded using unobtrusive devices. Findings suggested that the random forest algorithm could predict cognitive workload with relatively high accuracy (>70%) given that it was entirely reliant on physiological signals. The developed MLA can be used to develop adaptive in-vehicle technology based on real-time estimation of cognitive workload, which can reduce the risk of MVCs in police operations.

The effects of day-to-day variability of physiological data on operator functional state classification.

The application of pattern classification techniques to physiological data has undergone rapid expansion. Tasks as varied as the diagnosis of disease from magnetic resonance images, brain-computer interfaces for the disabled, and the decoding of brain functioning based on electrical activity have been accomplished quite successfully with pattern classification. These classifiers have been further applied in complex cognitive tasks to improve performance, in one example as an input to adaptive automation. In order to produce generalizable results and facilitate the development of practical systems, these techniques should be stable across repeated sessions. This paper describes the application of three popular pattern classification techniques to EEG data obtained from asymptotically trained subjects performing a complex multitask across five days in one month. All three classifiers performed well above chance levels. The performance of all three was significantly negatively impacted by classifying across days; however two modifications are presented that substantially reduce misclassifications. The results demonstrate that with proper methods, pattern classification is stable enough across days and weeks to be a valid, useful approach.

Cross-subject workload classification with a hierarchical Bayes model.

Most of the current EEG-based workload classifiers are subject-specific; that is, a new classifier is built and trained for each human subject. In this paper we introduce a cross-subject workload classifier based on a hierarchical Bayes model. The cross-subject classifier is trained and tested with data from a group of subjects. In our work, it was trained and tested on EEG data collected from 8 subjects as they performed the Multi-Attribute Task Battery across three levels of difficulty. The accuracy of this cross-subject classifier is stable across the three levels of workload and comparable to a benchmark subject-specific neural network classifier.

Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification.

Adaptive training using neurophysiological measures requires efficient classification of mental workload in real time as a learner encounters new and increasingly difficult levels of tasks. Previous investigations have shown that artificial neural networks (ANNs) can accurately classify workload, but only when trained on neurophysiological exemplars from experienced operators on specific tasks. The present study examined classification accuracies for ANNs trained on electroencephalographic (EEG) activity recorded while participants performed the same (within task) and different (cross) tasks for short periods of time with little or no prior exposure to the tasks. Participants performed three working memory tasks at two difficulty levels with order of task and difficulty level counterbalanced. Within-task classification accuracies were high when ANNs were trained on exemplars from the same task or a set containing the to-be-classified task, (M=87.1% and 85.3%, respectively). Cross-task classification accuracies were significantly lower (average 44.8%) indicating consistent systematic misclassification for certain tasks in some individuals. Results are discussed in terms of their implications for developing neurophysiologically driven adaptive training platforms.

Leadership styles of nurse managers and registered sickness absence among their nursing staff.

BACKGROUND: Sickness absence leads to understaffing and interferes with nursing efficiency and quality. It has been reported in literature that managerial leadership is associated with self-reported sickness absence in the working population. PURPOSES: This study investigated the relationship between managerial leadership and sickness absence in health care by associating nurse managers' leadership styles with registered sickness absence among their nursing staff. METHODOLOGY: The cross-sectional study included 699 nurses working in six wards (staff range = 91-140 employees) of a Dutch somatic hospital employing a total of 1,153 persons. The nurse managers heading the wards were asked to complete the Leadership Effectiveness and Adaptability Description questionnaire for situational leadership. The Leadership Effectiveness and Adaptability Description scores were linked to employer-registered nursing staff sickness absence. FINDINGS: High relationship-high task behavior (odds ratio [OR] = 0.76, 95% confidence interval [CI] = 0.65-0.85) and high relationship-low task behavior (OR = 0.37, 95% CI = 0.14 -0.98) were inversely related to the number of short (one to seven consecutive days) episodes of sickness absence among the staff. Low relationship-high task styles (OR = 2.44, 95% CI = 1.14-5.22) as well as low relationship-low task styles (OR = 2.44, 95% CI = 1.26-4.71) were positively associated with the number of short episodes of sickness absence. However, the leadership styles only explained 10% of the variance in short episodes of sickness absence. PRACTICE IMPLICATIONS: Leadership styles are associated with registered sickness absence. The nursing staff of relationship-oriented nurse managers has fewer short episodes of sickness absence than the staff of task-oriented managers. Training nurse managers in relational leadership styles may reduce understaffing and improve nursing efficiency and quality.

[Evaluation of interventions intended to reduce psychosocial work stress. Proposal for a classification scheme]. / Evaluation von Maßnahmen zur Verringerung psychosozialer Arbeitsbelastungen. Vorschlag eines Kategorisierungs- und Punkteschemas.

Evidence for the effectiveness of measures aiming to reduce psychosocial work stress is sporadic. This is contradictory to the requirement identified by the German Social Security Code (SGB VII) that interventions constitute the most important method of maintaining and improving employees' health. Reasons for this can be seen in the complexity of the subject and methodological issues concerning scientific standards. In addition, agreed quality standards are nonexistent for the evaluation of intervention measures. For this reason, a synopsis of existing audit and evaluation schemes was performed, thus, resulting in refined and adapted quality standards for intervention measures aiming to reduce psychosocial work stress. The quality criteria presented in this paper comprise aims, effectiveness, and facilitators, each being composed of several indicators. The criteria are designed as quality indicators which translate the outcome of an evaluation into quality figures. The process is transparent and offers a rational basis for communication, planning, and decision-making in health promotion.

[Aspects and relevant relationship in the nursing workload conceptualization: literature review]. / Relazioni tra gli aspeti principali che definiscono il concetto di lavoro del personale infermieristico: revisione della letteratura.

The authors describe how the definition of work-load in nursing is susceptible to many variables such as the number of patients cared for and their specific needs, the mix of skills required and the activities performed by nurses not strictly pertinent to care. These features make it difficult to create a global method for allocation of nursing resources . The authors have reviewed the literature from 2000 to 2009 in an attempt to identify the relationships between the concepts at the basis of the definition of work-load in nursing. The concepts of complexity and intensity of care are analyzed together with the various systems used for allocation of resources in different countries. Their results show that work-load is an important element of nursing organization and can influence the outcome of care.

Physical effort of middle-aged women in a university hospital - Field tests in comparison to standardized cardiopulmonary exercise testing (CPET).

Physical effort of workload of female employees in the healthcare system is perceived to rise. Research focused mainly on physical activity levels of registered nurses (RN). Therefore, we investigated physical strain during daily routine of other hospital workers next to RN. O2uptake, heart rate, breathing frequency and ventilation were measured in 33 female medical (associate) professionals (MAP) and Cleaners&Helpers (Cl&H) using Oxycon Mobile. Physical work load was compared to standardized CPET. Wilcoxon-test and ANOVA were calculated using SPSS 20.0. Cl&H performed heavy-lifting and had the lowest power during CPET. The O2-uptake for heavy-lifting as strong effort, differed significantly from office work (p < 0.001) and bedside-patient-care (p = 0.001). Compared with metabolic equivalent (MET) cleaning, heavy-lifting, and office work are underestimated. Bedside-patient-care, walking, pushing performed by MAP are overestimated. While Cl&H had the lowest maximal power in CPET, their work required the highest O2-uptake. The tasks are underestimated in the MET-tables.