Nurse–patient assignment models considering patient acuity metrics and nurses’ perceived workload

Neonatal acuity tool-defined staffing ratios in a tertiary Australian neonatal intensive care unit

AIMS

There is well-established data linking the adequacy of nurse staffing to patient outcomes. Evidence-based standards for staffing are therefore critical to drive improvements in clinical care. One such evidence-based approach is the use of patient acuity-based tools. The objective of this study is to determine the performance of a neonatal acuity tool in an Australian tertiary neonatal health-care setting, focusing on the classification of patient acuity and nursing:patient staffing ratios compared to current practice.

METHODS

Acuity data were collected in a neonatal intensive care unit (NICU) and special care baby unit (SCBU) over a 10-week period in 2023. Patient data were scored in the 16 domains at two time points (prior to morning and evening nursing shift changeover) for all admitted newborns.

RESULTS

For ventilated newborns nursed with a nurse:patient staffing ratio of 1:1, 78% of scores were within the L4-high acuity (score ≥ 26) band, with the remaining scores within the L3-high acuity (18-25) band. For newborns on non-invasive respiratory support in NICU staffed 1:1, the proportion scoring within the L4 acuity band was higher in the nasal high-flow group compared to the nasal continuous positive airway pressure group (P = 0.032), an effect not seen for those nursed 1:2 in NICU or for those on nasal high-flow nursed in SCBU either 1:2 or 1:3.

CONCLUSION

This study of how a neonatal acuity classification system compares with current nurse:patient staffing allocations in an Australian tertiary NICU, suggests refinements in staffing ratios for specific patient groups on respiratory support are possible.

Is the National Early Warning Score able to identify nursing activity load? A prospective observational study

BACKGROUND

The National Early Warning Score scale correlates well with the intensity of the patient's acute condition. It could also correlate with the nursing activity load and prove useful in defining and redistributing nursing resources based on the acuity of patients.

AIM

To assess whether patients' National Early Warning Score at hospital admission correlates with objective nursing demands and can be used to optimize the distribution of available care resources.

METHODS

This single-center prospective study included patients admitted to the Department of Internal Medicine at the Civil Hospital in Altovicentino (Italy) between September 1 and December 31, 2022. Nursing activities were recorded for the first three days after admission and standardized to the daily mean as performance/5 min/patient/day. Linear regression was used to assess the correlation between nursing demands for different National Early Warning Scores.

RESULTS

This study included 333 patients. Their mean National Early Warning Score was 3.9 (standard deviation: 2.9), with 61 % (203/333) in the National Early Warning Score <5 category, 19.5 % (65/333) in the National Early Warning Score 5-6 category, and 19.5 % (65/333) in the National Early Warning Score >6 category. Their average daily care requirements increased from 22 (16-30) activities/5 min/patient/day in the low National Early Warning Score category to 30 (20-39) activities/5 min/patient/day in the intermediate National Early Warning Score category (p < 0.001) and 35 (23-45) activities/5 min/patient/day in the high National Early Warning Score category (p < 0.001).

CONCLUSION

The National Early Warning Score correlates with nursing care activities for patients with an acute condition and can be used to optimize the distribution of available care resources.

Beyond order-based nursing workload: A retrospective cohort study in intensive care units

INTRODUCTION

In order to be positioned to address the increasing strain of burnout and worsening nurse shortage, a better understanding of factors that contribute to nursing workload is required. This study aims to examine the difference between order-based and clinically perceived nursing workloads and to quantify factors that contribute to a higher clinically perceived workload.

DESIGN

A retrospective cohort study was used on an observational dataset.

METHODS

We combined patient flow, nurse staffing and assignment, and workload intensity data and used multivariate linear regression to analyze how various shift, patient, and nurse-level factors, beyond order-based workload, affect nurses' clinically perceived workload.

RESULTS

Among 53% of our samples, the clinically perceived workload is higher than the order-based workload. Factors associated with a higher clinically perceived workload include weekend or night shifts, shifts with a higher census, patients within the first 24 h of admission, and male patients.

CONCLUSIONS

The order-based workload measures tended to underestimate nurses' clinically perceived workload. We identified and quantified factors that contribute to a higher clinically perceived workload, discussed the potential mechanisms as to how these factors affect the clinically perceived workload, and proposed targeted interventions to better manage nursing workload.

CLINICAL RELEVANCE

By identifying factors associated with a high clinically perceived workload, the nurse manager can provide appropriate interventions to lighten nursing workload, which may further reduce the risk of nurse burnout and shortage.

Clinical decision support system for clinical nurses' decision-making on nurse-to-patient assignment: a scoping review protocol

INTRODUCTION

Optimal nurse-to-patient assignment plays a crucial role in healthcare delivery, with direct implications for patient outcomes and the workloads of nursing staff. However, this process is highly intricate, involving a multitude of factors that must be carefully considered. The application of a clinical decision support system (CDSS) to support nursing decision-making can have a positive impact not only on patient outcomes but also on nursing efficiency. This scoping review aims to explore the implementation of CDSS in the decision process of optimal nurse-patient assignment (NPA).

METHODS AND ANALYSIS

This scoping review will follow a stage of the Arksey and O'Malley framework. It will also be based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews' (PRISMA-ScR) guidelines. The research primarily aims to identify studies' findings on applying CDSSs in the NPA process. Hence, academic and grey literature articles from six international bibliographic databases (ie, MEDLINE via PubMed, EMBASE via Ovid, CINAHL via EBSCOhost, IEEE Xplore, Scopus, ProQuest Dissertations and Theses Global) will be considered, where search strategies will be tailored to each database. The literature search will be conducted in February 2024, and the identified studies will be independently screened by two primary reviewers. After extracting data, the qualitative data will be analysed thematically, and the quantitative data will be subjected to descriptive statistics. The research is scheduled to conclude in December 2024.

ETHICS AND DISSEMINATION

Ethical approval is not required as primary data will not be collected in this study. The findings of this study will be disseminated through peer-reviewed publications and conference presentations.

Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience

OBJECTIVES

The purpose of this study is to identify combinations of workplace conditions that uniquely differentiate high, medium, and low registered nurse (RN) ratings of appropriateness of patient assignment during daytime intensive care unit (ICU) work shifts.

METHODS

A collective case study design and coincidence analysis were employed to identify combinations of workplace conditions that link directly to high, medium, and low RN perception of appropriateness of patient assignment at a mid-shift time point. RN members of the study team hypothesized a set of 55 workplace conditions as potential difference makers through the application of theoretical and empirical knowledge. Conditions were derived from data exported from electronic systems commonly used in nursing care.

RESULTS

Analysis of 64 cases (25 high, 24 medium, and 15 low) produced three models, one for each level of the outcome. Each model contained multiple pathways to the same outcome. The model for "high" appropriateness was the simplest model with two paths to the outcome and a shared condition across pathways. The first path comprised of the absence of overtime and a before-noon patient discharge or transfer, and the second path comprised of the absence of overtime and RN assignment to a single ICU patient.

CONCLUSION

Specific combinations of workplace conditions uniquely distinguish RN perception of appropriateness of patient assignment at a mid-shift time point, and these difference-making conditions provide a foundation for enhanced observability of nurses' work experience during hospital work shifts. This study illuminates the complexity of assessing nursing work system status by revealing that multiple paths, comprised of multiple conditions, can lead to the same outcome. Operational decision support tools may best reflect the complex adaptive nature of the work systems they intend to support by utilizing methods that accommodate both causal complexity and equifinality.

Implementation of Lean Six Sigma in Improving Competitive Advantage through Patient Satisfaction and Nurse Resources to Face the Covid-19 Pandemic at USU Medan Hospital

BACKGROUND: Human resources (HR) are very important in a field, as well as hospitals which are service providers. Once the importance of HR to a development process, so it can be concluded that HR are the core of the development of an agency. The right strategy is needed to find what should be the priority of improvement so that the quality of service can be provided optimally. AIM: One way that can be used is the application of lean six sigma. The application of the Lean six sigma concept can be done in the service and manufacturing industries. One of the service industries that is interesting to apply the Lean six sigma concept is health services through hospitals. METHODS: This concept uses five stages, namely, define (problem identification), measure (measurement of quality performance), analyze (analyze the causes of defects), improvement (make improvement efforts to improve quality), and control or control. The method used in this research is action research with univariate and bivariate analysis. RESULTS: The results of the application of the lean six sigma concept indicate that there are problems in nursing services where nurse services should be a competitive value or competitive advantage from Universitas Sumatera Utara (USU) Hospital in accordance with the hospital’s motto. After the analysis, it was found that there was an influence between rewards on the performance of nurses. CONCLUSION: There needs to be more attention from the hospital on the rewards given to nurses. Because of the nurse is satisfied with the reward given, the performance will be better. and there is no effect of training on the performance of nurses at USU Hospital. This improvement will certainly increase the competitive value or competitive advantage of USU Hospital.

A New Patient Acuity Tool to Support Equitable Patient Assignments in a Progressive Care Unit

Making fair and equitable staffing decisions and patient assignments created complexities and undue nursing dissatisfaction on a 20-bed progressive care unit. Common themes shared by the nursing staff included inadequate staffing ratios, increased workload, and unease for patient safety. On the basis of these concerns, a unit-based needs assessment provided insight into what perceived and actual barriers exist preventing nurses from providing excellent care. Information gathered helped determine what could be done to overcome some barriers, and performing a comprehensive unit profile assessment allowed for better insight into how the unit was currently functioning. A comprehensive review of literature was undertaken to examine articles related to patient acuity utilizing the electronic databases CINAHL, PubMed, and MEDLINE. Key words and phrases included the following: acuity, patient acuity, acuity tools, progressive care acuity, nurse-patient assignment, workload, nursing assignments, and equitable staffing. Utilizing the results of the nurse survey, and information gained from articles gathered during the literature review, a patient acuity tool was created. The tool was believed to be an accurate representation of the patients' acuity, and the majority of charge nurses reported that they utilized the patient acuity tool score when making nurse-patient assignments. Overall staff satisfaction and perceptions of nurse-patient assignment equity were improved.

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management

This paper proposes a framework based on the collective intelligence principle to understand how the healthcare ecosystem is facing the challenges posed by the COVID-19 by using the Internet of Things (IoT) combined with other digital technologies. The underlying assumption is to consider the Healthcare ecosystem as a collective intelligence system in which the multitude of actors can be coordinated to address the pandemic-specific management challenges. The Italian healthcare ecosystem is analyzed as scenario taking in consideration the 'genes' of the collective intelligence: What is being done?, Who is doing it?, Why are they doing it? and How is it being done?. Our analysis introduces policy implications based on a unique decision support system (DSS) to allocate a limited set of IoT devices to a larger group of patients, to balance the alternative needs to improve the conditions of the most severe patients but to maximize the efficiency of device use.

An Early Screening Tool for Discharge Planning Shortened Length of Hospital Stay for Elderly Patients with Community-Acquired Pneumonia

Background

Community-acquired pneumonia is one of the most common diseases in elderly persons and usually results in a prolonged hospital stay. Discharge planning plays an important role in reducing the length of hospitalization. This study was designed to determine whether early screening for risk factors for delayed discharge could improve the quality of discharge planning.

Methods

This retrospective, observational study was conducted in two medical facilities from January 2016 to December 2018. Hospital A used a screening tool on admission (screening group): screening for risk factors for delayed discharge and initiating discharge planning immediately for those for whom it was applicable, and discharge planning in the stable phase for those for whom it was not applicable; and Hospital B initiated discharge planning without screening (usual group). Propensity score-matched pneumonia patients in the two groups were then compared. The primary outcome was length of hospital stay.

Results

A total of 648 patients were enrolled in this study. After adjusting for age, sex, aspiration, comorbidity, pneumonia severity index, and key person, 118 pairs underwent analysis. Length of stay was significantly different (20 days vs 13 days, p<0.001) between the groups. There were no differences in duration of antibiotic treatment, in-hospital mortality, and 30-day readmission (9 days vs 9 days, p=0.744; 10 (8.5%) vs 10 (8.5%), p=1.000; 10 (8.5%) vs 9 (7.6%), p=0.811, respectively).

Conclusion

Early screening for delayed discharge improved the quality of discharge planning by reducing the length of stay in pneumonia patients.

Changes to a Shift Reporting Sheet on a Critical Care Unit-Nurse Perceptions and Lessons Learned

PURPOSE

Improved perception and compliance with timely completion of the shift summary tool by bedside critical care nurses, and standardization of reported patient condition and treatment variables can be accomplished through collaboration and evidence-based modifications.

MATERIALS AND METHODS

The IOWA Model was used as a framework to implement change in the population of practicing bedside nurses. In accordance with the AACN Healthy Work Environment standards, the population was given opportunity to complete surveys on a Likert scale to construct a best-fit instrument specific to the nurses' home units.

RESULTS

Employee satisfaction with the report process increased, compliance with completion of the form increased, and satisfaction with consistency and facilitation was noted by nursing managers.

CONCLUSION

Focusing on frequently used items, or identifiable bottlenecks in communications, are likely to have the fastest and most impactful results for change. Workflows can be streamlined by using stakeholder input and feedback to clearly define the desired parameters and outcomes from the process.

Congress2019 poster winners

The following articles represent the first place and runner-up winners from the Nursing Management Congress2019 poster contest. Both posters were presented and recognized at last year's Congress in New Orleans. Congratulations to our winners!

Beyond the corrective action hierarchy: A systems approach to organizational change

BACKGROUND

Many patient safety organizations recommend the use of the action hierarchy (AH) to identify strong corrective actions following an investigative analysis of patient harm events. Strong corrective actions, such as forcing functions and equipment standardization, improve patient safety by either preventing the occurrence of active failures (i.e. errors or violations) or reducing their consequences if they do occur.

PROBLEM

We propose that the emphasis on implementing strong fixes that incrementally improve safety one event at a time is necessary, yet insufficient, for improving safety. This singular focus has detracted from the pursuit of major changes that transform systems safety by targeting the latent conditions which consistently underlie active failures. To date, however, there are no standardized models or methods that enable patient safety professionals to assess, develop and implement systems changes to improve patient safety.

APPROACH

We propose a multifaceted definition of 'systems change'. Based on this definition, various types and levels of systems change are described. A rubric for determining the extent to which a specific corrective action reflects a 'systems change' is provided. This rubric incorporates four fundamental dimensions of systems change: scope, breadth, depth and degree. Scores along these dimensions can then be used to classify corrective actions within our proposed systems change hierarchy (SCH).

CONCLUSION

Additional research is needed to validate the proposed rubric and SCH. However, when used in conjunction with the AH, the SCH perspective will serve to foster a more holistic and transformative approach to patient safety.

Matching Nursing Assignment to Patients' Acuity Level: The Road to Nurses' Satisfaction

BACKGROUND AND PURPOSE

Nurses face many healthcare challenges, such as high turnover, workload, and unfair assignments, that lead to nurses' dissatisfaction. Linking nursing shift assignment to patients' acuity scores may increase the workload balance, achieving equitable nursing assignment and nurses' satisfaction. This article aimed to (a) describe the effectiveness of the Perroca patient acuity tool (PAT) by measuring nurses' satisfaction with workload and standard of care pre and post the acuity tool application, and (b) measure nurses' satisfaction about the implemented patients' acuity tool.

METHODS

Quasi-experimental (one group pre- and postdesign) was used. Donabedian's structure-process-outcome (SPO) model was followed as a conceptual model to guide the implementation process of this study. A convenience sample of 64 nurses participated in the study. Two scales were used to measure nurses' satisfaction with workload and with standard of care. Perroca's scale was used to measure nurses' satisfaction about patient acuity level.

RESULTS

Significant differences were found on nurse's total satisfaction level, satisfaction with workload, and satisfaction with standard of care between the two times, pre- and post-Perroca PAT application. The majority of nurses had positive impressions of the tool.

CONCLUSION

This study concludes that linking PAT to nursing shift assignment has several positive outcomes. It increases nurses' satisfaction and serves as managers' voice for important staffing decisions like recruitment, assignment distribution, employing new staff, and improving quality of care.

Acuity, nurse staffing and workforce, missed care and patient outcomes: A cluster-unit-level descriptive comparison

AIM

To compare the patient acuity, nurse staffing and workforce, missed nursing care and patient outcomes among hospital unit-clusters.

BACKGROUND

Relationships among acuity, nurse staffing and workforce, missed nursing care and patient outcomes are not completely understood.

METHOD

Descriptive design with data from four unit-clusters: medical, surgical, combined and step-down units. Descriptive statistics were used to compare acuity, nurse staffing coverage, education and expertise, missed nursing care and selected nurse-sensitive outcomes.

RESULTS

Patient acuity in general (medical, surgical and combined) floors is similar to step-down units, with an average of 5.6 required RN hours per patient day. In general wards, available RN hours per patient day reach only 50% of required RN hours to meet patient needs. Workforce measures are comparable among unit-clusters, and average missed nursing care is 21%. Patient outcomes vary among unit-clusters.

CONCLUSION

Patient acuity is similar among unit-clusters, while nurse staffing coverage is halved in general wards. While RN education, expertise and missed care are comparable among unit-clusters, mortality, skin injuries and risk of family compassion fatigue rates are higher in general wards.

IMPLICATIONS FOR NURSING MANAGEMENT

Nurse managers play a pivotal role in hustling policymakers to address structural understaffing in general wards, to maximize patient safety outcomes.

One Team's Experience with Integrating Flexible Visitation in the Medical Intensive Care Unit

Integration of flexible visitation into a large health system requires concentrated effort. Evaluating impact on patient, family, and staff outcomes is important to facilitate changes and ensure visiting policy success. The medical intensive care unit staff participated in a collaborative quality improvement effort to encourage flexible visitation. The integration of flexible visitation spanned an 18-month period, timed to accompany a transition to a new setting with rooms designed to support visitor presence. This article details these efforts, outcomes, and important gaps for future work evaluating integration of flexible visitation in critical care.

The Effects of Implementing a Patient Acuity Tool on Nurse Satisfaction in a Pulmonary Medicine Unit

In response to nurses' concerns of equity and satisfaction with patient assignments in a pulmonary medicine unit, a Patient Acuity Tool (PAT) was implemented. The impact of the PAT on nurse satisfaction and perceived equity of patient assignments was measured using a pre-/postsurvey design. Findings of the investigation indicate that a PAT supports nurse satisfaction and equity. In addition, qualitative data suggested that the PAT improved perceived professional autonomy and nurse-to-nurse communication.

Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force

Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning.

Modeling nurse-patient assignments considering patient acuity and travel distance metrics

Balancing workload among nurses on a hospital unit is important for the satisfaction and safety of nurses and patients. To balance nurse workloads, direct patient care activities, indirect patient care activities, and non-patient care activities that occur throughout a shift must be considered. The layout of a hospital unit and the location of a nurse's assigned patients relative to other resources on the unit are also important factors in achieving workload balance. In most hospitals, a unit charge nurse is responsible for the shift assignment of patients to nurses based on experience and past practices. The nurse-patient assignment process is also often a manual process in which the charge nurse must sort through multiple decision criteria in a limited amount of time. In this paper, a methodology for the construction of balanced nurse-patient workload assignments is proposed. Through the illustration of this methodology new scoring metrics are developed using measures currently available on, or from, the hospital unit. It was demonstrated that the complex scheduling problem can be captured. While the methodology was illustrated for a scheduling problem commonly encountered on a hospital unit, the approach can be adapted to other workforce scheduling problems in which measures of workload are required and composed of elements imposed by the work environment, variability within the required tasks, and a measurable perception of the relative intensity of the work elements.