Consensus Development Project (CDP): An overview of staffing for safe and effective nursing care

What might make nurses stay? A protocol for discrete choice experiments to understand NHS nurses' preferences at early-career and late-career stages

INTRODUCTION

Like many countries, England has a national shortage of registered nurses. Employers strive to retain existing staff, to ease supply pressures. Disproportionate numbers of nurses leave the National Health Services (NHS) both early in their careers, and later, as they near retirement age. Research is needed to understand the job preferences of early-career and late-career nurses working in the NHS, so tailored policies can be developed to better retain these two groups.

METHODS AND ANALYSIS

We will collect job preference data for early-career and late-career NHS nurses, respectively using two separate discrete choice experiments (DCEs). Findings from the literature, focus groups, academic experts and stakeholder discussions will be used to identify and select the DCE attributes (ie, job features) and levels. We will generate an orthogonal, fractional factorial design using the experimental software Ngene. The DCEs will be administered through online surveys distributed by the regulator Nursing and Midwifery Council. For each group, we expect to achieve a final sample of 2500 registered NHS nurses working in England. For early-career nurses, eligible participants will be registered nurses who graduated in the preceding 5 years (ie, 2019-2023). Eligible participants for the late-career survey will be registered nurses aged 55 years and above. We will use conditional and mixed logit models to analyse the data. Specifically, study 1 will estimate the job preferences of early-career nurses and the possible trade-offs. Study 2 will estimate the retirement preferences of late-career NHS nurses and the potential trade-offs.

ETHICS AND DISSEMINATION

The research protocol was reviewed and approved by the host research organisation Ethics Committees Research Governance (University of Southampton, number 80610) (https://www.southampton.ac.uk/about/governance/regulations-policies/policies/ethics). The results will be disseminated via conference presentations, publications in peer-reviewed journals and annual reports to key stakeholders, the Department of Health and Social Care, and NHS England/Improvement retention leaders.

REGISTRATION DETAILS

Registration on OSF http://doi.org/10.17605/OSF.IO/RDN9G.

The relationship between nursing skill mix and severity of illness of patients admitted in Australian and New Zealand intensive care units

BACKGROUND

Critically ill patients in the intensive care environment require an appropriate nursing workforce to improve quality of care and patient outcomes. However, limited information exists as to the relationship between severity of illness and nursing skill mix in the intensive care.

OBJECTIVE

The aim of this study was to describe the variation in nursing skill mix across different hospital types and to determine if this was associated with severity of illness of critically ill patients admitted to adult intensive care units (ICUs) in Australia and New Zealand.

DESIGN & SETTING

A retrospective cohort study using the Australia and New Zealand Intensive Care Society Adult Patient Database (to provide information on patient demographics, severity of illness, and outcome) and the Critical Care Resources Registry (to provide information on annual nursing staffing levels and hospital type) from July 2014 to June 2020. Four hospital types (metropolitan, private, rural/regional, and tertiary) and three patient groups (elective surgical, emergency surgical, and medical) were examined.

MAIN OUTCOME MEASURE

The main outcome measure was the proportion of critical care specialist registered nurses (RNs) expressed as a percentage of the full-time equivalent (FTE) of total RNs working within each ICU each year, as reported annually to the Critical Care Resources Registry.

RESULTS

Data were examined for 184 ICUs in Australia and New Zealand. During the 6-year study period, 770 747 patients were admitted to these ICUs. Across Australia and New Zealand, the median percentage of registered nursing FTE with a critical care qualification for each ICU (n = 184) was 59.1% (interquartile range [IQR] = 48.9-71.6). The percentage FTE of critical care specialist RNs was highest in private [63.7% (IQR = 52.6-78.2)] and tertiary ICUs [58.1% (IQR = 51.2-70.2)], followed by metropolitan ICUs [56.0% (IQR = 44.5-68.9)] with the lowest in rural/regional hospitals [55.9% (IQR = 44.9-70.0)]. In ICUs with higher percentage FTE of critical care specialist RNs, patients had higher severity of illness, most notably in tertiary and private ICUs. This relationship was persistent across all hospital types when examining subgroups of emergency surgical and medical patients and in multivariable analysis after adjusting for the type of hospital and relative percentage of each diagnostic group.

CONCLUSIONS

In Australian and New Zealand ICUs, the highest acuity patients are cared for by nursing teams with the highest percentage FTE of critical care specialist RNs. The Australian and New Zealand healthcare system has a critical care nursing workforce which scales to meet the acuity of ICU patients across Australia and New Zealand.

The Road to Developing Standard Time for Efficient Nursing Care: A Time and Motion Analysis

(1) Background: The amount of time nurses spend with their patients is essential to improving the quality of patient care. Studies have shown that nurses spend a considerable amount of time on a variety of activities--which are often not taken into account while estimating nurse-to-patient care time allocation--that could potentially be eliminated, combined or delegated with greater productivity. The current study aimed to calculate standard time for each activity category by quantifying the amount of time required by nurses to complete an activity category and determine the adjustment time that can be given during work, as well as determine factors that can be altered to improve the efficiency of nursing care on inpatient general wards of a governmental hospital. (2) Method: A time and motion study was conducted over two weeks using 1-to-1 continuous observations of nurses as they performed their duties on inpatient general wards, while observers recorded each single activity, and specifically the time and movements required to complete those activities. (3) Result: There was 5100 min of observations over 10 working days. Nurses spent 69% (330 min) of time during their 8 h morning shift on direct patient care, (19.4%) ward/room activities (18%), documentation (14%), indirect patient care (12%) and professional communication (5%). Around 94 min of activities seem to be wasted and can be potentially detrimental to nurses' overall productivity and threaten patient care quality. The standard number of hours that represents the best estimate of a general ward nurse regarding the optimal speed at which the staff nurse can provide care related activities was computed and proposed. (4) Conclusions: The findings obtained from time-motion studies can help in developing more efficient and productive nursing work for more optimal care of patients.

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.

"More is not always better": Park's sweet spot theory-driven implementation strategy for viable optimal safe nurse staffing policy in practice

AIM

This paper aims to propose Park's sweet spot theory-driven implementation strategy, which makes optimal safe staffing policy really work in nursing practice.

BACKGROUND

For the last 40 years, mainstream nursing workforce research has emphasized that having more registered nurses leads to better patient outcomes, and yet staffing policies have failed to implement this crucial concept. Meanwhile, global nursing shortages have become rampant, a problem that only dilutes the skill-mix ratios in the nursing workforce. Low fertility and an aging population worldwide are then accelerating these shortages. These dire circumstances may be persisting because of unclear, unsubstantiated cost-efficiency in the nursing workforce. For this reason, there remains a dearth of well-researched evidence for a clear threshold on optimal safe staffing levels that could maximize quality of care relative to cost given limited healthcare financial budgets and which could also be fitted into each care setting. Along with that, an implementation strategy for optimal safe staffing levels is nonexistent.

SOURCES OF EVIDENCE

An implementation strategy has been developed through interdisciplinary consilience-based theory synthesis of both prospective theory and regulatory focus theory combined with Park's optimized nursing staffing (sweet spot) estimation theory.

DISCUSSION/CONCLUSIONS

A theory-driven novel implementation strategy is proposed, which functions as a nudge strategy that uses the synchronous balance of quality of care, nurse staffing, and cost. It illustrates (1) how to create shared value among patients, nurses, and stakeholders through robust evidence-based, informed shared decision-making rationales on the optimal safe nurse staffing levels and (2) how to induce stakeholders to overcome resistance to innovation and improve their nursing workforce through value chain in management science.

IMPLICATIONS FOR NURSING WORKFORCE POLICY

This novel implementation strategy may be a viable solution to mitigate the nursing shortage by leading stakeholders (1) to compete with each other (on the basis of nursing sufficiency) and (2) to competitively demonstrate the patient-centered value (patient-perceived care quality relative to cost) of their institutions.