Measuring Work Demands in Hospital Nursing: A Feasibility Study

Decoding Nursing Job Demands: A Multicenter Cross-Sectional Descriptive Study Assessing Nursing Workload in Hospital Medical-Surgical Wards

Background

Nursing workload is largely studied but poorly explored under physical, mental, and emotional dimensions. Currently, only a limited number of variables have been linked to nursing workload and work contexts.

Purpose

The study aimed to investigate whether it is feasible to identify variables that consistently correlate with nursing workload and others that are specific to the context.

Methods

We employed a descriptive correlational analysis and a cross-sectional design. Data were collected through a survey distributed to registered nurses working across Italy, at the conclusion of randomly assigned morning or afternoon shifts.

Results

We received 456 surveys from 195 shifts, collected from nurses in four public and two private hospitals. Commonly associated variables with nursing workload dimensions included patient complexity of care, admission/discharge or transfer, informing patients/relatives, contacting physicians, and unscheduled activities. Variables categorized as setting-specific were patient isolation and specialties, nurse-to-patient ratio, adequacy of staff in the shift, peer collaboration, healthcare documentation, educating others, and medical urgency.

Conclusions

In summary, certain variables consistently correlate with nursing workload across settings, while others are specific to the context of care. It is imperative for nurses and nurse managers to measure the nursing workload in various dimensions, enabling the prompt implementation of improvement actions.

The better work, better care framework: 7 strategies for sustainable healthcare system process improvement

Healthcare systems are under pressure to control costs and improve performance. Efforts to apply improvement trends such as "Lean" and other industrial engineering approaches have led to degradation of the working environment for healthcare professionals. Research is increasingly demonstrating how poor working environments contribute to declines in care quality and has led to calls for a "quadruple aim" with a focus on the working environment alongside quality, cost, and patient experience factors. This paper contributes to the debate by using a "systems" perspective to propose seven strategies by which healthcare systems might be improved without compromising the working environment. This article presents a rationale for these strategies based on current organisational psychology and human factors research and how these strategies might be deployed in practice. The authors argue that better working conditions leads to better care for patients and presents a viable approach for both practitioners and researchers to pursue the "Better Work, Better Care" agenda.

What determines physical, mental and emotional workloads on nurses? A cross-sectional study

AIM

This study aimed to identify determinants of physical, mental and emotional nursing workloads.

BACKGROUND

Workload has a physical, mental and emotional dimension. It influences employees' well-being and quality of care. Nevertheless, studies of specific predictors for each dimension of nurses' workload are scarce.

METHODS

We used a cross-sectional prospective design based on the Job Demand-Resources theory. We asked nurses to describe workload perceived at the end of every shift over three consecutive weeks. Data were gathered from two academic hospitals, in seven medical-surgical wards. We received 259 responses and tested 2 multivariate regression models.

RESULTS

Physical workload was predicted from all variables tested; mental workload was determined by patient complexity or isolation, adequacy of nurse staffing and skill-mix, and unscheduled activities; and emotional workload was predicted by all variables except adequacy of staffing and other people's education.

CONCLUSIONS

Patient, nurse and workflow aspects influenced nurse's shift workload differently for each specific dimension.

IMPLICATIONS FOR NURSING MANAGEMENT

Measurement and definition of predictors of workload in the work environment are essential. Recognizing the determinants of specific dimensions of workload facilitates identification of the most appropriate interventions to improve nurses' well-being in health care settings.

Modelling the impacts of COVID-19 on nurse workload and quality of care using process simulation

Higher acuity levels in COVID-19 patients and increased infection prevention and control routines have increased the work demands on nurses. To understand and quantify these changes, discrete event simulation (DES) was used to quantify the effects of varying the number of COVID-19 patient assignments on nurse workload and quality of care. Model testing was based on the usual nurse-patient ratio of 1:5 while varying the number of COVID-19 positive patients from 0 to 5. The model was validated by comparing outcomes to a step counter field study test with eight nurses. The DES model showed that nurse workload increased, and the quality of care deteriorated as nurses were assigned more COVID-19 positive patients. With five COVID-19 positive patients, the most demanding condition, the simulant-nurse donned and doffed personal protective equipment (PPE) 106 times a shift, totaling 6.1 hours. Direct care time was reduced to 3.4 hours (-64% change from baseline pre-pandemic case). In addition, nurses walked 10.5km (+46% increase from base pre-pandemic conditions) per shift while 75 care tasks (+242%), on average, were in the task queue. This contributed to 143 missed care tasks (+353% increase from base pre-pandemic conditions), equivalent to 9.6 hours (+311%) of missed care time and care task waiting time increased to 1.2 hours (+70%), in comparison to baseline (pre-pandemic) conditions. This process simulation approach may be used as potential decision support tools in the design and management of hospitals in-patient care settings, including pandemic planning scenarios.

Defining nursing workload predictors: A pilot study

Aim

To explore predictors of perceived nursing workload in relation to patients, nurses and workflow.

Background

Nursing workload is important to health care organisations. It determines nurses' well‐being and quality of care. Nevertheless, its predictors are barely studied.

Methods

A cross‐sectional prospective design based on the complex adaptive systems theory was used. An online survey asked nurses to describe perceived workload at the end of every shift. Data were gathered from five medical‐surgical wards over three consecutive weeks. We received 205 completed surveys and tested multivariate regression models.

Results

Patient acuity, staffing resources, patient transfers, documentation, patient isolation, unscheduled activities and patient specialties were significant in predicting perceived workload. Nurse‐to‐patient ratio proved not to be a predictor of workload.

Conclusions

This study significantly contributed to literature by identifying some workload predictors. Complexity of patient care, staffing adequacy and some workflow aspects were prominent in determining the shift workload among nurses.

Implications for nursing management

Our findings provide valuable information for top and middle hospital management, as well as for policymakers. Identification of predictors and measurement of workload are essential for optimizing staff resources, workflow processes and work environment. Future research should focus on the appraisal of more determinants.

Predicting nurse fatigue from measures of work demands

Fatigue arising from excessive work demands is a known safety challenge in hospital nurses. This study aimed to determine which measures of work demands during nursing work are most predictive of hospital nurse fatigue levels at the end of the work shift. Measures of work demands of registered nurses from two hospital units in the United States were collected from organizational data sources, wearable sensors, and questionnaires. Fatigue levels were measured at the start and end of each shift using the Brief Fatigue Inventory. Multilevel linear regression analysis was used to predict end of shift fatigue based on work demand variables. The best fit model included multiple variables from organizational data sources and a physical activity variable measured by a wearable sensor. Organizational data can be used to create dynamic measures of work demands as they occur and predict end of shift fatigue levels in hospital nurses.