Predicting the effect of nurse–patient ratio on nurse workload and care quality using discrete event simulation

Effects of work environments on satisfaction of nurses working for integrated care system in South Korea: a multisite cross-sectional investigation

BACKGROUND

Nurses' satisfaction has an impact on organizational and patient outcomes. Integrated care system in South Korea was established in 2015 to improve care quality and decrease caregiving burden. Since then, nurses' satisfaction has increased due to an increase in nursing staffing. However, besides nurse staffing, various work environments still affect nurse satisfaction.

METHODS

Individual online surveys were conducted with participants to determine their personal characteristics, work environments, and hospital characteristics. We used mixed-effects linear regression equation contained both fixed and random effects.

RESULTS

This study included 2,913 nurses from 119 hospitals. Their average job satisfaction was less than 6 points out of 10 points. Age, shift type, perceived workload, and delegation criteria were significant factors influencing nurses' satisfaction. There was no significant factor among hospital characteristics. The satisfaction level of nurses was high for no-night rotating shift, low perceived workload, and clear delegation criteria.

CONCLUSIONS

Nurses' satisfaction is affected by several work environmental factors. Low nurse satisfaction has a substantial impact on both patients and nurses. Therefore, nurse managers and hospitals should determine factors influencing their satisfaction and develop strategies to improve their satisfaction.

The impact of stretch service goals on unethical behaviors of nurses: A three-wave cross-sectional study

BACKGROUND

Stretch service goals strive to motivate healthcare practitioners to maintain high quality in service provision. However, little is known about how stretch service goals trigger nurses' unethical behavior.

RESEARCH AIM

This study aimed to investigate the influence of stretch service goals on nurses' unethical behavior, as well as the mediating effects of patient entitlement and nurses' emotional dissonance.

RESEARCH DESIGN

A quantitative cross-sectional study is designed.

PARTICIPANTS AND RESEARCH CONTEXT

We sourced data by conducting a time-lagged three-wave survey study from March to September 2020. Random sampling was used, and data were collected from 422 nurse-patient pairs in Chinese hospitals. Bootstrapping method and structural equation modeling were employed to verify the conceptual model.

ETHICAL CONSIDERATIONS

The study was approved by the designated authority within hospitals and ethical committees.

RESULTS

Stretch service goals are not directly related to nurses' unethical behavior. Stretch service goals can trigger nurses' unethical behavior via patient entitlement. Patient entitlement and nurses' emotional dissonance play a chain-mediating role between stretch service goals and nurses' unethical behavior.

CONCLUSIONS

In the context of the healthcare industry, nurses may engage in unethical behavior due to the pressure of achieving stretch service goals. This study contributes to opening the "black box" of stretch service goals and nurses' unethical behavior by exploring the chain-mediating effect of patient entitlement and nurses' emotional dissonance.

Nurses' adherence to patient safety protocols and its relationship with adverse patient events

INTRODUCTION

Ensuring patient safety is a critical component of healthcare delivery, and nurses play a vital role in adhering to patient safety protocols to prevent adverse events. However, studies linking nurses' adherence to these protocols with patient outcomes is scarce. This study has two-fold purposes: (1) to examine determinants of nurses' adherence to patient safety protocols, and (2) to investigate the relationship between nurses' adherence to patient safety protocols and adverse patient events.

DESIGN

A cross-sectional design.

METHODS

A total of 343 clinical nurses from five hospitals in the Philippines were included in this study. Two standardized scales were used to collect data, including the Patient Safety Adherence Scale and the Adverse Patient Events Scale. Data collection took place from April 2022 to August 2022.

RESULTS

Adherence to patient safety protocols was generally moderate to high (M = 4.483), while nurse-reported adverse events occurred at a lower frequency (M = 1.150). Lower adherence rates were identified in areas such as reporting safety errors (M = 3.950), conducting fall risk assessments (M = 4.299), and adhering to pressure ulcer prevention guidelines (M = 3.979). Patients' and their families' complaints (M = 2.129) and abuses (M = 1.475) were the most frequently reported adverse events. Increased adherence to safety protocols was associated with higher reporting of adverse patient events (β = 0.115, p < 0.001).

CONCLUSION

Higher adherence to patient safety protocols resulted in an increased reporting of adverse patient events. The factors identified that contributed to nurses' adherence to patient safety protocols can be utilized in the development of strategies aimed at improving compliance, especially in areas where adherence is currently low.

CLINICAL RELEVANCE

Healthcare organizations should prioritize efforts to enhance adherence to patient safety protocols, particularly in areas with lower compliance rates, through relevant trainings, resource provision, and support systems. Promoting a culture of open communication and reporting can significantly contribute to reducing adverse events and improving patient safety.

Improving patient activation with a tailored nursing discharge teaching intervention for multimorbid inpatients: A quasi-experimental study

OBJECTIVE

Preliminary effectiveness test of a novel structured personalized discharge teaching intervention for multimorbid inpatients.

METHODS

Using a 2-group sequential pre/post-intervention design, the sample comprised 68 pre-intervention control group and 70 post- intervention group participants. The discharge teaching intervention by trained clinical nurses used structured tools to engage patients and individualize discharge teaching. Outcomes measures included Patient Activation Measure, Readiness for Hospital Discharge Scale, Discharge Care Experiences Survey, and readmission with 10 days post-discharge.

RESULTS

The intervention had a statistically significant positive effect on improving patient activation (M=4.8; p = 0.05) from admission to post-discharge. The participation subscale of the Discharge Care Experiences Survey was higher in the intervention (M=4.1, SD=0.7) than the control group (M=3.8, SD=0.7; t (127)= -2.79, p = .01, effect size= .34). There were no significant between-group differences in Readiness for Hospital Discharge Scale and readmission.

CONCLUSIONS

Our results suggest that a structured personalized discharge teaching intervention can improve patient activation and participation in discharge care. Further refinement of the intervention is needed to evaluate and improve specific components of the intervention.

PRACTICE IMPLICATIONS

Structured personalized discharge teaching should include patient engagement strategies in the teaching-learning process.

Impact of an emergency department nursing intervention on continuity of care, self-care, and psychological symptoms

BACKGROUND

The emergency department plays a crucial role in providing acute care to patients. Nursing interventions in this setting are essential for improving the continuity of care, enhancing patients' self-care abilities, and reducing psychological symp-toms.

AIM

To evaluate the effect of nursing interventions in the emergency department on these indicators in an emergency department.

METHODS

A retrospective analysis was conducted on 120 patients admitted to the emergency department between January 2022 and May 2023. The patients were divided into two groups: The control group (conventional nursing intervention) and the observation group (conventional nursing intervention + emergency department nursing intervention). The two groups were compared regarding continuity of care, self-care ability, psychological symptoms, and satisfaction with care.

RESULTS

The emergency department nursing interventions significantly improved the continuity of care, enhanced patients' self-care abilities, and reduced psychological symptoms such as anxiety and depression.

CONCLUSION

Nursing interventions in the emergency department positively impact continuity of care, self-care, and psychological symptoms. However, it is important to acknowledge the limitations of this study, including the small number of studies, variable methodological quality, and the heterogeneity of the study population. Future research should address these limitations and further explore the effects of different types of nursing interventions in the emergency department. Additionally, efforts should be made to enhance the application and evaluation of these interventions in clinical practice.

Toward ensuring care quality and safety across settings: examining time pressure in a nursing home with observational time motion study metrics based on the Omaha system

BACKGROUND

Meaningful data to determine safe and efficient nursing workload are needed. Reasoning a nurse can accomplish a finite number of interventions and location changes per hour, examination of time pressure using time motion study (TMS) methods will provide a comparable indication of safe and efficient workload for an individual nurse.

METHODS

An observer shadowed 11 nurses at a 250-bed nursing home in the Southeastern United States and recorded 160 h of observations using TimeCaT, web-based TMS data recording software. Predefined Omaha System nursing interventions (N = 57) and locations (N = 8) were embedded within TimeCaT. The time-stamped data were downloaded from TimeCaT and analyzed using descriptive and inferential statistics. Five time pressure metrics were derived from previous TMS findings in acute care settings.

RESULTS

Overall, nurses spent 66 s for each intervention, performed 65 interventions per hour, stayed 130 s at each location, changed locations 28 times per hour, and multitasked for 29% of working time. Computed hourly time pressure metrics enabled visualization of variability in time pressure metrics over time, with differences in multitasking by licensure, unit/role, and observation session time.

CONCLUSIONS

Nursing home nurses consistently experienced a high degree of time pressure, especially multitasking for one-third of their working time. To inform staffing decision making and improve the quality of care, resident outcomes, and nurse satisfaction, it is critical to identify ways to mitigate time pressure. Additional research is needed to refine and extend the use of the time pressure metrics.

The experience of nurses to reduce implicit rationing of nursing care: a phenomenological study

BACKGROUND

Implicit rationing of nursing care can adversely affect patient safety and the quality of care, and increase nurses' burnout and turnover tendency. Implicit rationing care occurs at the nurse-to-patient level (micro-level), and nurses are direct participants. Therefore, the strategies based on experience of nurses to reduce implicit rationing care have more reference value and promotion significance. The aim of the study is to explore the experience of nurses to reduce implicit rationing care, thereby to provide references for conducting randomized controlled trials to reduce implicit rationing care.

METHODS

This is a descriptive phenomenological study. Purpose sampling was conducted nationwide. There are 17 nurses were selected and semi-structured in-depth interviews were conducted. The interviews were recorded, transcribed verbatim and analyzed via thematic analysis.

RESULTS

Our study found that nurses' reported experience of coping with implicit rationing of nursing care contained three aspects: personal, resource, and managerial. Three themes were extracted from the results of the study: (1) improving personal literacy; (2) supplying and optimizing resources and (3) standardizing management mode. The improvement of nurses' own qualities are the prerequisites, the supply and optimization of resources is an effective strategy, and clear scope of work has attracted the attention of nurses.

CONCLUSION

The experience of dealing with implicit nursing rationing includes many aspects. Nursing managers should be grounded in nurses' perspectives when developing strategies to reduce implicit rationing of nursing care. Promoting the improvement of nurses' skills, improving staffing level and optimizing scheduling mode are promising measures to reduce hidden nursing rationing.

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.

Enhancement of Intra-hospital patient transfer in medical center hospital using discrete event system simulation

The intra-hospital transfer of critically ill patients are associated with complications at up to 70%. Numerous issues can be avoided with optimal pre-transport planning and communication. Simulation models have been demonstrated to be an effective method for modeling processes and enhancing on-time service and queue management. Discrete-event simulation (DES) models are acceptable for general hospital systems with increased variability. Herein, they are used to improve service effectiveness. A prospective observational study was conducted on 13 official day patient transfers, resulting in a total of 827 active patient transfers. Patient flow was simulated using discrete-event simulation (DES) to accurately and precisely represent real-world systems and act accordingly. Several patient transfer criteria were examined to create a more realistic simulation of patient flow. Waiting times were also measured to assess the efficiency of the patient transfer process. A simulation was conducted to identify 20 scenarios in order to discover the optimal scenario in which where the number of requests (stretchers or wheelchairs) was increased, while the number of staff was decreased to determine mean waiting times and confidence intervals. The most effective approach for decreasing waiting times involved prioritizing patients with the most severe symptoms. After a transfer process was completed, staff attended to the next transfer process without returning to base. Results show that the average waiting time was reduced by 21.78% which is significantly important for emergency cases. A significant difference was recorded between typical and recommended patient transfer processes when the number of requests increased. To decrease waiting times, the patient transfer procedure should be modified according to our proposed DES model, which can be used to analyze and design queue management systems that achieve optimal waiting times.

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.

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

A healthcare resource allocation generally plays a vital role in the number of patients treated (p_nt) and the patient waiting time (w_t) in healthcare institutions. This study aimed to estimate p_nt and w_t as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δ_i) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δ_i of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for p_nt; 0.9514, 0.9517, 0.9514, and 0.9514 for w_t, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ_0.2 (AB had a better accuracy at 0.8709 based on the value of δ_0.2 for p_nt) in the test stage. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for p_nt; 0.8820, 0.8821, 0.8819, and 0.8818 for w_t in the training phase, respectively. All scenarios created by the δ_i coefficient should be preferred for ED since the income provided by the p_nt value to the hospital was more than the cost of healthcare resources. On the contrary, the w_t estimation results of ML algorithms based on the δ_i coefficient differed. Although w_t values in all ML algorithms with δ_0.0 and δ_0.1 coefficients reduced the cost of the hospital, w_t values based on δ_0.2 and δ_0.3 increased the cost of the hospital.

Data-Driven Nurse Staffing in the Neonatal Intensive Care Unit

ABSTRACT

The challenge of nurse staffing is amplified in the acute care neonatal intensive care unit (NICU) setting, where a wide range of highly variable factors affect staffing. A comprehensive overview of infant factors (severity, intensity), nurse factors (education, experience, preferences, team dynamics), and unit factors (structure, layout, shift length, care model) influencing pre-shift NICU staffing is presented, along with how intra-shift variability of these and other factors must be accounted for to maintain effective and efficient assignments. There is opportunity to improve workload estimations and acuity measures for pre-shift staffing using technology and predictive analytics. Nurse staffing decisions affected by intra-shift factor variability can be enhanced using novel care models that decentralize decision-making. Improving NICU staffing requires a deliberate, systematic, data-driven approach, with commitment from nurses, resources from the management team, and an institutional culture prioritizing patient safety.

Patient Education Regarding Fasting Recommendations to Shorten Fasting Times in Patients Undergoing Esophagogastroduodenoscopy: A Controlled Pilot Study

This study evaluated the applicability and efficacy of patient education regarding fasting recommendations to shorten fasting times in patients undergoing esophagogastroduodenoscopy (EGD). A prospective nonrandomized controlled pilot study was performed. The intervention group (IG) was educated by nurses to eat until 6 hours and drink until 2 hours before EGD. The control group (CG) received usual care. Outcomes were applicability as perceived by patients, adherence to fasting recommendations, gastric visibility, and patients' comfort. A total of 109 patients were included of whom 42 were IG patients (37%). Patients' perspectives on fasting, their experienced discomfort, professional support, and circadian rhythm influenced application of fasting recommendations. Adherence to length of fasting from foods improved with 3:14 hours ( p < .001) and from liquids with 5:22 hours ( p < .001) in the IG compared with the CG. Gastric visibility during EGD was better in the IG than in the CG. The IG patients experienced significant less thirst, hunger, headache, and anxiety. To successfully reduce fasting times, fasting education should include positive, individual instructions, which help patients apply the fasting recommendations within their biorhythm. Positive, concrete instructions by nurses shortened fasting times before EGD, which improved gastric visibility and reduced patient discomfort.

Developing a simulation tool to quantify biomechanical load and quality of care in nursing

Nursing is a high musculoskeletal disorder (MSD) risk job with high workload demands. This study combines Digital Human Modelling (DHM) and Discrete Event Simulation (DES) to address the need for tools to better manage MSD risk. This novel approach quantifies physical-workload, work-performance, and quality-of-care, in response to varying geographical patient-bed assignments, patient-acuity levels, and nurse-patient ratios. Lumbar loads for 86 care-delivery tasks in an acute care hospital unit were used as inputs in a DES model of the care-delivery process, creating a shift-long time trace of the biomechanical load. Peak L4/L5 compression and moment were 3574 N and 111.58 Nm, respectively. This study reports trade-offs in all three experiments: (i) increasing geographical patient-bed assignment distance decreased L4/L5 compression (8.8%); (ii) increased patient-acuity decreased L4/L5 moment (4%); (iii) Increased nurse-patient ratio decreased L4/L5 compression (10%) and moment (17%). However, in all experiments, Quality of care indicators deteriorated (20, 19, and 29%, respectively). Practitioner Summary: This research has the potential to support decision-makers by developing a simulation tool that quantifies the impact of varying operational and design-policies in terms of biomechanical-load and quality of care. The demonstrator-model reports: as geographical patient-bed distance, patient-acuity levels, and nurse-patient ratios increase, biomechanical-load reduces, and quality of care deteriorates.

Effects of Implementing an Acuity Tool on a Psychiatric Intensive Care Unit

BACKGROUND

Staff shortages, reduced budgets, and high acuity of violent psychiatric patients can create challenges in psychiatric intensive care units (PICUs).

LOCAL PROBLEM

Staffing of the psychiatric unit was based on patient census rather than evidence-based practices.

METHODS

A pre-/postintervention design was used to examine changes in nursing satisfaction and patient outcomes as measured with the National Database of Nursing Quality Indicators (NDNQI) survey results.

INTERVENTIONS

A psychiatric specific acuity tool was implemented on the PICU of a Veterans Administration hospital.

RESULTS

After an initial decrease related to the COVID-19 pandemic, total acuity and the total number of nurses remained relatively stable while the unit census declined. NDNQI survey results improved with the largest being a 52-percentile increase for the quality-of-care summary measure.

CONCLUSIONS

An acuity tool can help standardize practice, determine fair patient assignments among staff, increase nurse satisfaction, and promote best practices for patient safety.

A comparison of Knowledge, attitude and practice (KAP) of nurses on nursing Post-stroke dysphagia patients between iii-A and ii-A hospitals in China: a propensity score-matched analysis

Background

Whether knowledge, attitude and practice of nurses on nursing post-stroke dysphagia patients varies between different ranking hospitals is still unknown. This study aimed to compare the knowledge, attitude and practice level of nurses on nursing post-stroke dysphagia patients between iii-A and ii-A hospitals in China.

Design

A cross-sectional study design was used.

Methods

Data were collected from eighteen hospitals in Wuhan, Hubei in May–July 2020, and a total of 824 nurses were recruited by convenient sampling. After propensity score matching, 205 participants in iii-A hospitals were matched with 205 participants in ii-A hospitals.

Results

There were no statistically differences in the socio-demographic characteristics between two groups after propensity score matching. Before matching, the regression coefficients between hospital ranking and knowledge, attitude, practice were -0.415, -0.718 and -1.855, respectively. After matching, the coefficients changed to -0.394, -0.824 and -1.278. Nurses from iii-A hospitals had higher knowledge and attitude scores than nurses from ii-A hospitals, but no significant practice scores difference was observed between various rankings of hospitals.

Conclusions

The KAP of nurses on nursing post-stroke dysphagia patients were different in iii-A and ii-A hospitals. Administrators should strengthen management, provide more learning resources and trainings to meet nurses’ needs about methods to deal with and recognize dysphagia, so as to further improve the quality of post-stroke dysphagia management.

A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation

BACKGROUND

Hospital cabins are a part and parcel of the healthcare system. Most patients admitted in hospital cabins reside in bedridden and immobile conditions. Though different kinds of systems exist to aid such patients, most of them focus on specific tasks like calling for emergencies, monitoring patient health, etc. while the patients' limitations are ignored. Though some patient interaction systems have been developed, only singular options like touch, hand gesture or voice based interaction were provided which may not be usable for bedridden and immobile patients.

METHODS

At first, we reviewed the existing literature to explore the prevailing healthcare and interaction systems developed for bedridden and immobile patients. Then, a requirements elicitation study was conducted through semi-structured interviews. Afterwards, design goals were established to address the requirements. Based on these goals and by using computer vision and deep learning technologies, a hospital cabin control system having multimodal interactions facility was designed and developed for hospital admitted, bedridden and immobile patients. Finally, the system was evaluated through an experiment replicated with 12 hospital admitted patients to measure its effectiveness, usability and efficiency.

RESULTS

As outcomes, firstly, a set of user-requirements were identified for hospital admitted patients and healthcare practitioners. Secondly, a hospital cabin control system was designed and developed that supports multimodal interactions for bedridden and immobile hospital admitted patients which includes (a) Hand gesture based interaction for moving a cursor with hand and showing hand gesture for clicking, (b) Nose teeth based interaction where nose is used for moving a cursor and teeth is used for clicking and (c) Voice based interaction for executing tasks using specific voice commands. Finally, the evaluation results showed that the system is efficient, effective and usable to the focused users with 100% success rate, reasonable number of attempts and task completion time.

CONCLUSION

In the resultant system, Deep Learning has been incorporated to facilitate multimodal interaction for enhancing accessibility. Thus, the developed system along with its evaluation results and the identified requirements provides a promising solution for the prevailing crisis in the healthcare sector.

TRIAL REGISTRATION

Not Applicable.

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Nurse staffing in Portuguese public hospitals

AIM

This study aims to contribute to the knowledge of nurse staffing in hospital settings in central and northern Portugal.

BACKGROUND

Nurse staffing is a critical factor for the quality and safety of health care and is still an understudied topic in Portugal.

METHOD

A cross-sectional study was conducted with a sample of 850 nurses from 12 public hospitals in the central and northern regions of Portugal.

RESULTS

Nurse staffing in these hospital units is insufficient, especially in internal medicine units and central hospitals. Nurses' perceptions are in line with the objective data.

CONCLUSIONS

The shortage of nurses is a horizontal issue that is especially serious in internal medicine units and central hospitals and a potential threat to the quality of care.

IMPLICATIONS FOR NURSING MANAGEMENT

The results stress the need for an urgent leadership intervention in nurse staffing levels in the hospitals analysed in this study. Contextual knowledge about nurse staffing is essential for decision-making and supporting health and human resource management policies.

Factors That Influence Nurse Staffing Levels in Acute Care Hospital Settings

PURPOSE

To identify which patient and hospital characteristics are related to nurse staffing levels in acute care hospital settings.

DESIGN

A cross-sectional design was used for this study.

METHODS

The sample comprised 1,004 patients across 10 hospitals in the Andalucian Health Care System (southern Spain) in 2015. The sampling was carried out in a stratified, consecutive manner on the basis of (a) hospital size by geographical location, (b) type of hospital unit, and (c) patients' sex and age group. Random criteria were used to select patients based on their user identification in the electronic health record system. The variables were grouped into two categories, patient and hospital characteristics. Multilevel linear regression models (MLMs) with random intercepts were used. Two models were fitted: the first was the null model, which contained no explanatory variables except the intercepts (fixed and random), and the second (explanatory) model included selected independent variables. Independent variables were allowed to enter the explanatory model if their univariate association with the nurse staffing level in the MLM was significant at p < .05.

RESULTS

Two hierarchical levels were established to control variance (patients and hospital). The model variables explained 63.4% of the variance at level 1 (patients) and 71.8% at level 2 (hospital). Statistically significant factors were the type of hospital unit (p = .002), shift (p < .001), and season (p < .001). None of the variables associated with patient characteristics obtained statistical significance in the model.

CONCLUSIONS

Nurse staffing levels were associated with hospital characteristics rather than patient characteristics.

CLINICAL RELEVANCE

This study provides evidence about factors that impact on nurse staffing levels in the settings studied. Further studies should determine the influence of patient characteristics in determining optimal nurse staffing levels.

Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed-methods approach

AIM

To identify, simulate and evaluate the formal and informal patient-level and unit-level factors that nurse managers use to determine the number of nurses for each shift.

BACKGROUND

Nurse staffing schedules are commonly set based on metrics such as midnight census that do not account for seasonality or midday turnover, resulting in last-minute adjustments or inappropriate staffing levels.

METHODS

Staffing schedules at a paediatric intensive care unit (PICU) were simulated based on nurse-to-patient assignment rules from interviews with nursing management. Multivariate regression modelled the discrepancies between scheduled and historical staffing levels and constructed rules to reduce these discrepancies. The primary outcome was the median difference between simulated and historical staffing levels.

RESULTS

Nurse-to-patient ratios underestimated staffing by a median of 1.5 nurses per shift. Multivariate regression identified patient turnover as the primary factor accounting for this difference and subgroup analysis revealed that patient age and weight were also important. New rules reduced the difference to a median of 0.07 nurses per shift.

CONCLUSION

Measurable, predictable indicators of patient acuity and historical trends may allow for schedules that better match demand.

IMPLICATIONS FOR NURSING MANAGEMENT

Data-driven methods can quantify what drives unit demand and generate nurse schedules that require fewer last-minute adjustments.

SARS/MERS/SARS-CoV-2 Outbreaks and Burnout Syndrome among Healthcare Workers. An Umbrella Systematic Review

The coronavirus-19 (COVID-19) pandemic is putting a severe strain on all healthcare systems. Several occupational risk factors are challenging healthcare workers (HCWs) who are at high risk of mental health outcomes, including Burnout Syndrome (BOS). BOS is a psychological syndrome characterized by emotional exhaustion, depersonalization, and low personal accomplishment. An umbrella review of systematic reviews and meta-analyses concerning BOS and coronavirus (SARS/MERS/SARS-CoV-2) outbreaks was carried out on PubMed Central/Medline, Cochrane Library, PROSPERO, and Epistemonikos databases. Data relating to COVID-19 is insufficient, but in previous SARS and MERS outbreaks about one-third of HCWs manifested BOS. This prevalence rate is similar to the figure recorded in some categories of HCWs exposed to chronic occupational stress and poor work organization during non-epidemic periods. Inadequate organization and worsening working conditions during an epidemic appear to be the most likely causes of BOS. Preventive care and workplace health promotion programs could be useful for protecting healthcare workers during pandemics, as well as during regular health activities.

The role of teamwork in the implicit rationing of nursing care: A systematic mixed-methods review

AIM

To systematically evaluate the role of teamwork in implicit rationing care and how to improve teamwork.

BACKGROUND

The implicit rationing of nursing leads to adverse effects for both patients and nurses. Therefore, how to reduce it has attracted increasing research attention. How teamwork may be an important factor in reducing implicit rationing care has become a focus of research.

METHODS

Data between May 2000 and May 2020 were collected from five databases. The study was guided by the framework of a mixed studies review.

RESULTS

Seventeen studies were chosen for review regarding efficient teamwork to reduce implicit rationing care. The following seven subthemes with positive effects that improve teamwork and reduce implicit rationing care were formed: (a) improving knowledge and skills; (b) promoting effective communication; (c) building mutual trust; (d) reducing turnover intention; (e) reasonable staffing; (f) division of responsibilities; and (g) cultivating team consciousness.

CONCLUSIONS

Teamwork can decrease implicit care and is influenced by many factors, but the intervention is singular. In the future, teamwork can be further improved to reduce implicit care.

IMPLICATIONS FOR NURSING MANAGEMENT

With more intervention research in the future, leadership and team-oriented roles can be used to complete all the care needed.

Stress, Burnout, and Low Self-Efficacy of Nursing Professionals: A Qualitative Inquiry

Nursing professionals face a high level of stress and burnout due to overloaded responsibilities, which may cause a low level of self-efficacy. From the perspective of nursing professionals, the research aims to understand what are the sources of stress and burnout which would reduce the self-efficacy and the unbalanced patient ratio and how would nursing professionals describe their experiences, sources of stress and burnout, and self-efficacy. Based on the snowball sampling strategy, 60 nursing professionals were invited for qualitative research data collection. Based on the lens of the self-efficacy approach, the results indicated that the environmental factors, including workplace bullying, family stress, misunderstanding of public members, and personal development and career enhancement took important roles in increasing their stress and burnout and in reducing their self-efficacy. The outcomes of this study discovered the social status and discrimination toward nursing professionals. Government leaders, policymakers, and researchers should take this research as an opportunity to reform their policy for human resource management and education for the respectfulness of medical and nursing professionals in the public health system.

GRASP Workload Nursing Measurement System at end of life

The purpose of this quality improvement initiative was to analyze how nurses record their workload in the GRASP Workload Measurement System and document the end-of-life nursing care provided to imminently dying patients. The analysis was done in conformity with the Comfort Measures Order Set in our hospital. Nursing documentation was examined (n = 4 patient records) covering 15 oncology nursing shifts. Nurses are expected to complete the GRASP tool after each shift for all the patients in their care. It is presumed that nurses' workload data will be reported accurately and reliably, as well as interrelate with their nursing documentation. Workload audits are conducted routinely to ensure accuracy. Interrater Reliability Monitoring was used to analyze the degree of agreement between the ratings performed on the audit of the completed GRASP tool and the nursing documentation on end-of-life care delivered. The GRASP compliance rate was 66.6% and GRASP-documentation accuracy rate was 60-70%. These observations were below the established target of 90%. The results provide insight regarding any gaps between documentation and GRASP at end of life.

Patient-Nurse Ratio is Related to Nurses' Intention to Leave Their Job through Mediating Factors of Burnout and Job Dissatisfaction

In healthcare settings, nurses’ workload, burnout, and job satisfaction are associated to the patient–nurse ratio. Whether this ratio also affects their intention to leave the nursing profession, along with the underlying stress pathway, remains unclear. This study aimed to investigate the effects of the patient–nurse ratio on nurses’ intention to leave and considering the mediating roles of burnout and job dissatisfaction. The study analyzed the data of two pooled cross-sectional surveys collected in 2013 and 2014. Measures were obtained by a structure questionnaire, which queried the average daily patient–nurse ratio (ADPNR), nurses’ personal burnout, client-related burnout, job dissatisfaction, intention to leave, and other demographics. ADPNRs were standardized according to hospital levels. Multiple regression models examined mediation hypotheses, and a percentile bootstrap confidence interval was applied to determine the significance of indirect effects. A total of 1409 full-time registered nurses in medical and surgical wards of 24 secondary or tertiary hospitals in Taiwan completed self-administered questionnaires. Most of the participants were female (97.2%), and the mean age was 29.9 years. The association between the standardized ADPNR and intention to leave their job was significantly mediated by personal burnout, client-related burnout, and job dissatisfaction. Higher standardized ADPNRs predicted higher levels of personal burnout, client-related burnout, and job dissatisfaction, each of which resulted in higher levels of intention to leave the current job. The results highlight that appropriate patient–nurse ratio standards may be further discussed by selecting personal burnout, client-related burnout, and job dissatisfaction as indicators.

Time of admission to intensive care unit, strained capacity, and mortality: A retrospective cohort study

PURPOSE

We sought to study the association between afterhours ICU admission and ICU mortality considering measures of strained ICU capacity.

MATERIALS AND METHODS

Retrospective analysis of 4141 admissions to 2 ICUs in Lisbon, Portugal (06/2016-06/2018). Primary exposure was ICU admission on 20:00 h-07:59 h. Primary outcome was ICU mortality. Measures of strained ICU capacity were: bed occupancy rate ≥ 90% and cluster of ICU admissions 2 h before or following index admission.

RESULTS

There were 1581 (38.2%) afterhours ICU admissions. Median APACHE II score (19 vs. 20) was similar between patients admitted afterhours and others (P = .27). Patients admitted afterhours had higher crude ICU mortality (15.4% vs. 21.9%; P < .001), but similar adjusted ICU mortality (aOR [95%CI] = 1.15 [0.97-1.38]; P = .12). While bed occupancy rate ≥ 90% was more frequent in patients admitted afterhours (23.1% vs. 29.1%) or deceased in ICU (23.6% vs. 33.7%), cluster of ICU admissions was more frequent in patients admitted during daytime hours (75.2% vs. 58.9%) or that survived the ICU stay (70.1% vs. 63.9%; P ≤ .001 for all). These measures of strained ICU capacity were not associated with adjusted ICU mortality (P ≥ .10 for both).

CONCLUSIONS

Afterhours ICU admission and measures of strained ICU capacity were associated with crude but not adjusted ICU mortality.