Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit

Association of Shift-Level Organizational Factors with Nosocomial Infection in the Neonatal Intensive Care Unit

Objective

To evaluate the association between shift-level organizational data (unit occupancy, nursing overtime ratios [OTRs], and nursing provision ratios [NPRs]) with nosocomial infection (NI) among infants born very preterm in the neonatal intensive care unit (NICU).

Study design

This was a multicenter, retrospective cohort study, including 1921 infants 230/7-326/7 weeks of gestation admitted to 3 tertiary-level NICUs in Quebec between 2014 and 2018. Patient characteristics and outcomes (NIs) were obtained from the Canadian Neonatal Network database and linked to administrative data. For each shift, unit occupancy (occupied/total beds), OTR (nursing overtime hours/total nursing hours), and NPR (number of actual/number of recommended nurses) were calculated. Mixed-effect logistic regression models were used to calculate aOR for the association of organizational factors (mean over 3 days) with the risk of NI on the following day for each infant.

Results

Rate of NI was 11.5% (220/1921). Overall, median occupancy was 88.7% [IQR 81.0-94.6], OTR 4.4% [IQR 1.5-7.6], and NPR 101.1% [IQR 85.5-125.1]. A greater 3-day mean OTR was associated with greater odds of NI (aOR 1.08, 95% CI 1.02-1.15), a greater 3-day mean NPR was associated lower odds of NI (aOR 0.96, 95% CI 0.95-0.98), and occupancy was not associated with NI (aOR, 0.99, 95% CI 0.96-1.02). These findings were consistent across multiple sensitivity analyses.

Conclusions

Nursing overtime and nursing provision are associated with the adjusted odds of NI among infants born very preterm in the NICU. Further interventional research is needed to infer causality.

Effect of the postoperative pain management model on the psychological status and quality of life of patients in the advanced intensive care unit

OBJECTIVE

it was to explore the influence of the postoperative pain management mode on the psychological state, quality of life (QOL), and nursing satisfaction of late patients in the intensive care unit (ICU) and improve the nursing effect of late patients in the ICU.

METHODS

seventy patients who were admitted to the postoperative ICU for gastric cancer and received treatment in our hospital from March 2021 to May 2022 were selected. The patients were assigned into a research group and a control (Ctrl) group according to a random number table, with 70 cases in each group. The Ctrl group received routine nursing intervention, while research group received nursing intervention based on routine nursing intervention with postoperative pain management mode and received psychological care. Good communication was established with the patients, and the postoperative pain assessment was well conducted. The general information, state-trait anxiety (STAI) score, World Health Organization's Quality of Life Instrument (WHO QOL-BREF) score, and care satisfaction were compared.

RESULTS

the general information differed slightly, such as sex, age, and ward type, between groups, with comparability (P > 0.05). S-AI scores (13.15 ± 1.53 vs. 16.23 ± 1.24) and T-AI scores (14.73 ± 3.12 vs. 18.73 ± 3.16) in research group were inferior to those in Ctrl group (P < 0.05). The scores of patients in research group in the physiological field (78.9 ± 6.1 points vs. 72.3 ± 5.6 points), social relationship field (76.9 ± 4.5 points vs. 71.3 ± 4.8 points), psychological field (78.6 ± 6.2 points vs. 72.4 ± 5.3 points), environmental field (78.6 ± 6.7 points vs. 73.5 ± 6.4 points), and total QOL (79.5 ± 7.4 points vs. 71.6 ± 5.4 points) were higher than those in Ctrl group (P < 0.05). The total satisfaction rate with nursing care in research group (82.85%) was dramatically superior to that in Ctrl group (62.85%) (P < 0.05).

CONCLUSION

the adoption of a postoperative pain management model in postoperative nursing interventions for patients in advanced ICUs can alleviate anxiety and depression, improve patients' QOL and nursing satisfaction, and have clinical promotion value.

Characteristics of High Versus Low-Performing Hospitals for Very Preterm Infant Morbidity and Mortality

Objective

To ascertain organizational attributes, policies, and practices that differentiate hospitals with high versus low risk-adjusted rates of very preterm neonatal morbidity and mortality (NMM).

Methods

Using a positive deviance research framework, we conducted qualitative interviews of hospital leadership and frontline clinicians from September-October 2018 in 4 high-performing and 4 low-performing hospitals in New York City, based on NMM measured in previous research. Key interview topics included NICU physician and nurse staffing, professional development, standardization of care, quality measurement and improvement, and efforts to measure and report on racial/ethnic disparities in care and outcomes for very preterm infants. Interviews were audiotaped, professionally transcribed, and coded using NVivo software. In qualitative content analysis, researchers blinded to hospital performance identified emergent themes, highlighted illustrative quotes, and drew qualitative comparisons between hospital clusters.

Results

The following features distinguished high-performing facilities: 1) stronger commitment from hospital leadership to diversity, quality, and equity; 2) better access to specialist physicians and experienced nursing staff; 3) inclusion of nurses in developing clinical policies and protocols, and 4) acknowledgement of the influence of racism and bias in healthcare on racial-ethnic disparities. In both clusters, areas for improvement included comprehensive family engagement strategies, care standardization, and reporting of quality data by patient sociodemographic characteristics.

Conclusions and relevance

Our findings suggest specific organizational and cultural characteristics, from hospital leadership and clinician perspectives, that may yield better patient outcomes, and demonstrate the utility of a positive deviance framework to center equity in quality initiatives for high-risk infant care.

Is nurse staffing associated with critical deterioration events on acute and critical care pediatric wards? A literature review

Pediatric and neonatal patients admitted to acute and critical care wards may experience critical deterioration events that may lead to unexpected deaths if unrecognized and untreated promptly. Adequate levels and skill-mix of nurse staffing are essential for the quality of patient monitoring and response to deteriorating patients. Insufficient staffing may have an impact on the occurrence of missed care and consequently on critical deterioration events, increasing the risk of mortality and failure-to-rescue. To review the literature to explore the association between nurse staffing levels or skill-mix and pediatric and neonatal critical deterioration events, such as mortality, pediatric intensive care unit (PICU)/neonatal intensive care unit (NICU) unplanned admissions, cardiac arrests, and failure-to-rescue. A structured narrative literature review was performed. Pubmed, Cinhal, and Web of Science were searched from January 2010 to September 2022. Four independent reviewers conducted the study screening and data extraction. The quality of the studies included was evaluated using the Joanna Briggs Institute critical appraisal tools. Out of a total of 2319 studies, 15 met the inclusion criteria. A total of seven studies were performed in PICU, six in NICU, and two in general pediatric wards. Nurse staffing measures and outcomes definitions used were heterogeneous. Most studies suggested nursing skill-mix, increased working experience, or higher nursing degrees were associated with increased survival in PICU. Decreased nursing staffing levels were associated with increased mortality in NICU and mechanically ventilated patients in PICU.

CONCLUSION

Evidence on the association of nurse staffing and critical deterioration events in PICU and NICU is limited, while there is no evidence reported for pediatric wards. Future research is needed to determine adequate levels of nurse/patient ratios and proportion of registered nurses in the skill-mix for pediatric acute and critical care nursing to improve outcomes on in-patient wards.

WHAT IS KNOWN

• Adult nursing skill-mix, staffing ratios, and level of education are associated with patient mortality and failure to rescue. • In children, nurse staffing levels are associated with clinical outcomes.

WHAT IS NEW

• Evidence on the association of nurse staffing levels or skill-mix with pediatric or neonatal mortality is limited. • There is some evidence regarding the association of nursing work experience, certification, higher level degree with in-hospital survival in PICU.

Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020

The present study referred to the technology-based learning model to conduct a systematic review of the dimensions of nursing activities, research samples, research methods, roles of artificial intelligence, applied artificial intelligence algorithms, evaluation measure of algorithms, and research foci. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedure, this study obtained and analyzed a total of 102 high-quality artificial intelligence-associated nursing activities studies published from 2001 to 2020 in the Web of Science database. The results showed: (1) In terms of nursing activities, nursing management was explored the most, followed by nursing assessment; (2) quantitative methods were most frequently adopted in artificial intelligence-associated nursing activities studies to investigate issues related to patients, followed by nursing staff; (3) the most adopted roles of artificial intelligence in artificial intelligence-associated nursing activities studies were profiling and prediction, followed by assessment and evaluation; (4) artificial intelligence-associated nursing activities studies frequently mixed applied artificial intelligence algorithms and evaluation measure of algorithms; (5) in the dimension of research foci, most studies mainly paid attention to the design or evaluation of the artificial intelligence systems/instruments, followed by investigating the correlation and affect issues. Based on the findings, several recommendations are raised as a reference for future researchers, educators, and policy makers.

Informatics and data science perspective on Future of Nursing 2020-2030: Charting a pathway to health equity

The Future of Nursing 2020 to 2030 report explicitly addresses the need for integration of nursing expertise in designing, generating, analyzing, and applying data to support initiatives focused on social determinants of health (SDOH) and health equity. The metrics necessary to enable and evaluate progress on all recommendations require harnessing existing data sources and developing new ones, as well as transforming and integrating data into information systems to facilitate communication, information sharing, and decision making among the key stakeholders. We examine the recommendations of the 2021 report through an interdisciplinary lens that integrates nursing, biomedical informatics, and data science by addressing three critical questions: (a) what data are needed?, (b) what infrastructure and processes are needed to transform data into information?, and (c) what information systems are needed to "level up" nurse-led interventions from the micro-level to the meso- and macro-levels to address social determinants of health and advance health equity?

Nurse-Reported Staffing Guidelines and Exclusive Breast Milk Feeding

BACKGROUND

Nursing care is essential to overall quality of healthcare experienced by patients and families-especially during childbearing. However, evidence regarding quality of nursing care during labor and birth is lacking, and established nurse-sensitive outcome indicators have limited applicability to maternity care. Nurse-sensitive outcomes need to be established for maternity care, and prior research suggests that the initiation of human milk feeding during childbirth hospitalization is a potentially nurse-sensitive outcome.

OBJECTIVE

The aim of this study was to determine the relationship between nurse-reported staffing, missed nursing care during labor and birth, and exclusive breast milk feeding during childbirth hospitalization as a nurse-sensitive outcome.

METHODS

2018 Joint Commission PC-05 Exclusive Breast Milk Feeding rates were linked to survey data from labor nurses who worked in a selected sample of hospitals with both PC-05 data and valid 2018 American Hospital Association Annual Survey data. Nurse-reported staffing was measured as the perceived compliance with Association of Women's Health, Obstetric and Neonatal Nurses staffing guidelines by the labor and delivery unit. Data from the nurse survey were aggregated to the hospital level. Bivariate linear regression was used to determine associations between nurse and hospital characteristics and exclusive breast milk feeding rates. Generalized structural equation modeling was used to model relationships between nurse-reported staffing, nurse-reported missed care, and exclusive breast milk feeding at the hospital level.

RESULTS

The sample included 184 hospitals in 29 states and 2,691 labor nurses who worked day, night, or evening shifts. Bivariate analyses demonstrated a positive association between nurse-reported staffing and exclusive breast milk feeding and a negative association between missed nursing care and exclusive breast milk feeding. In structural equation models controlling for covariates, missed skin-to-skin mother-baby care and missed breastfeeding within 1 hour of birth mediated the relationship between nurse-reported staffing and exclusive breast milk feeding rates.

DISCUSSION

This study provides evidence that hospitals' nurse-reported compliance with Association of Women's Health, Obstetric and Neonatal Nurses staffing guidelines predicts hospital-exclusive breast milk feeding rates and that the rates are a nurse-sensitive outcome.

Fewer Patients per Nurse Does Not Offset Increased Nurse Stress Related to Treatment Uncertainty and Mortality in the Neonatal Intensive Care Unit

BACKGROUND

Many inpatient healthcare institutions' nurse staffing plans systematically assign fewer patients to nurses when patient acuity is high, but the impact of this strategy on components of nurse stress has not been thoroughly investigated.

PURPOSE

To examine the relationship between nurse-to-patient ratio assigned based on NICU patient acuity with the Nurse Stress Scale (NSS) subscales Death and Dying, Conflict with Physicians, Inadequate Preparation, Lack of Support, Conflict with Other Nurses, Work Load, and Uncertainty Concerning Treatment.

METHODS

A survey including the NSS tool items, demographic questions, and a question about nurse-to-patient ratio during the shift was administered. Cronbach's α, linear regression, and Spearman's correlation were used for data analysis.

RESULTS

Analysis of the 72 participating NICU nurses' survey responses showed fewer patients per nurse during the shift was negatively correlated with stress related to Death and Dying ( P < .001) and Uncertainty Concerning Treatment ( P = .002) subscale scores. This inverse relationship remained significant after controlling for education and years of experience.

IMPLICATIONS FOR PRACTICE

The observed higher stress can be inferred to be due to high patient acuity since fewer patients are assigned to nurses caring for high-acuity patients. Improvements in communication to nurses about patients' medical condition, treatment rationale, and information that should be conveyed to the family could reduce nurse stress from treatment uncertainty. Targeted education and counseling could help nurses cope with stress due to patient deaths.

IMPLICATIONS FOR RESEARCH

Interventions to reduce stress related to treatment uncertainty and death of patients among NICU nurses caring for high-acuity infants should be developed and evaluated.

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.

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

Background

Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios.

Objective

This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.

Methods

Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria.

Results

The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail.

Conclusions

The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

Racial and Economic Neighborhood Segregation, Site of Delivery, and Morbidity and Mortality in Neonates Born Very Preterm

OBJECTIVE

To assess the influence of racial and economic residential segregation of home or hospital neighborhood on very preterm birth morbidity and mortality in neonates born very preterm.

STUDY DESIGN

We constructed a retrospective cohort of n = 6461 infants born <32 weeks using 2010-2014 New York City vital statistics-hospital data. We calculated racial and economic Index of Concentration at the Extremes for home and hospital neighborhoods. Neonatal mortality and morbidity was defined as death and/or severe neonatal morbidity. We estimated relative risks for Index of Concentration at the Extremes measures and neonatal mortality and morbidity using log binomial regression and the risk-adjusted contribution of delivery hospital using Fairlie decomposition.

RESULTS

Infants whose mothers live in neighborhoods with the greatest relative concentration of Black residents had a 1.6 times greater risk of neonatal mortality and morbidity than those with the greatest relative concentration of White residents (95% CI 1.2-2.1). Delivery hospital explained more than one-half of neighborhood differences. Infants with both home and hospital in high-concentration Black neighborhoods had a 38% adjusted risk of neonatal mortality and morbidity compared with 25% of those with both home and hospital high-concentration White neighborhoods (P = .045).

CONCLUSIONS

Structural racism influences very preterm birth neonatal mortality and morbidity through both the home and hospital neighborhood. Quality improvement interventions should incorporate a framework that includes neighborhood context.

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.

Development and use of an adjusted nurse staffing metric in the neonatal intensive care unit

OBJECTIVE

To develop a nurse staffing prediction model and evaluate deviation from predicted nurse staffing as a contributor to patient outcomes.

DATA SOURCES

Secondary data collection conducted 2017-2018, using the California Office of Statewide Health Planning and Development and the California Perinatal Quality Care Collaborative databases. We included 276 054 infants born 2008-2016 and cared for in 99 California neonatal intensive care units (NICUs).

STUDY DESIGN

Repeated-measures observational study. We developed a nurse staffing prediction model using machine learning and hierarchical linear regression and then quantified deviation from predicted nurse staffing in relation to health care-associated infections, length of stay, and mortality using hierarchical logistic and linear regression.

DATA COLLECTION METHODS

We linked NICU-level nurse staffing and organizational data to patient-level risk factors and outcomes using unique identifiers for NICUs and patients.

PRINCIPAL FINDINGS

An 11-factor prediction model explained 35 percent of the nurse staffing variation among NICUs. Higher-than-predicted nurse staffing was associated with decreased risk-adjusted odds of health care-associated infection (OR: 0.79, 95% CI: 0.63-0.98), but not with length of stay or mortality.

CONCLUSIONS

Organizational and patient factors explain much of the variation in nurse staffing. Higher-than-predicted nurse staffing was associated with fewer infections. Prospective studies are needed to determine causality and to quantify the impact of staffing reforms on health outcomes.