Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field.
Systematic review of prospective studies.
CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol.
Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes.
Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health.
We have registered our review in PROSPERO (registration number: CRD 42023415232).
A whole person approach to healthy aging can provide insight into social factors that may be critical. Digital technologies, such as mobile health (mHealth) applications, hold promise to provide novel insights for healthy aging and the ability to collect data between clinical care visits. Machine learning/artificial intelligence methods have the potential to uncover insights into healthy aging. Nurses and nurse informaticians have a unique lens to shape the future use of this technology.
The purpose of this research was to apply machine learning methods to MyStrengths+MyHealth de-identified data (N = 988) for adults 45 years of age and older. An exploratory data analysis process guided this work.
Overall (n = 988), the average Strength was 66.1% (SD = 5.1), average Challenges 66.5% (SD = 7.5), and average Needs 60.06% (SD = 3.1). There was a significant difference between Strengths and Needs (p < 0.001), between Challenges and Needs (p < 0.001), and no significant differences between average Strengths and Challenges. Four concept groups were identified from the data (Thinking, Moving, Emotions, and Sleeping). The Thinking group had the most statistically significant challenges (11) associated with having at least one Thinking Challenge and the highest average Strengths (66.5%) and Needs (83.6%) compared to the other groups.
This retrospective analysis applied machine learning methods to de-identified whole person health resilience data from the MSMH application. Adults 45 and older had many Strengths despite numerous Challenges and Needs. The Thinking group had the highest Strengths, Challenges, and Needs, which aligns with the literature and highlights the co-occurring health challenges experienced by this group. Machine learning methods applied to consumer health data identify unique insights applicable to specific conditions (e.g., cognitive) and healthy aging. The next steps involve testing personalized interventions with nurses leading artificial intelligence integration into clinical care.
The uptake of research evidence on staffing issues in nursing by nursing leadership, management and into organizational policies seems to vary across Europe. This study wants to assess this uptake of research evidence.
Scoping survey.
The presidents of twelve country specific Sigma Chapters within the European Region answered written survey questions about work organisation, national staffing levels, national skill mix levels, staff characteristics, and education.
Seven of the 12 chapters could not return complete data, reported that data was unavailable, there was no national policy or only guidance related to some settings.
Enhancing the awareness of nursing research and of nursing leaders and managers regarding staffing level evidence is not enough. It seems necessary to encourage nurse leaders to lobby for staffing policies.
Research evidence on staffing issues in nursing and how it benefits health care is available. In Europe this evidence should be used more to lobby for change in staffing policies.
To identify the current presence of stereotypes about the nursing profession in Italy and to understand how gendered processes and modalities are regulated and expressed in the physician-nurse dyad, and the implications for professional identity and autonomy.
Qualitative multimethod design.
Forty-five interviews were conducted with nurses and physicians. The collected qualitative data underwent automatic textual data analysis using a multidimensional exploratory approach and a gender framework analysis.
In Italy, nurses' roles are still associated with gender stereotypes stemming from the predominant male culture, which affects sexual and gender identity, the division of labor, and access to career paths. This leads to disadvantages in the nursing profession, which is heavily dominated by women.
Biological differences between sexes generate an unconscious yet shared symbolic gender order composed of negative stereotypes that influence nurses' professional roles and activities. They follow behaviors that enter the work routine and institutionalize organizational processes. These effects are also seen in the asymmetric, limited, and reciprocal interprofessional relationships between male physicians and female nurses, where the former hinders the latter's professional autonomy and access to top positions.
This survey raises awareness of gender issues and stimulates reflection. It also enables health and nursing organizations to take action to raise gender awareness and education by countering the image of a non-autonomous profession. The analysis of gender processes allows us to identify interventions that can counteract forms of oppression in the work environment that lead to the emergence of nursing as a non-autonomous profession.
Nurses in southern Israel's public hospitals were exposed to unusual traumatic events following the October 7, 2023, Hamas attack on Israel, and the ensuing Swords of Iron War. This study aimed to clarify the complexity of wartime nursing by identifying profiles based on risk factors (i.e., psychological distress and adjustment disorders) and protective factors (i.e., positive affect (PA), resilience, and perceived social support [PSS]).
This study utilizes a cross-sectional design.
Two hundred nurses at a major public hospital in southern Israel completed self-report questionnaires. A latent profile analysis (LPA) was conducted to identify distinct profiles based on nurses' risk and protective factors. Differences in profiles were examined alongside sociodemographic and occupational variables and traumatic event exposure. The LPA was conducted using MPlus 8.8 Structural Equation Modeling (SEM) software.
Two distinct profiles were identified: “reactive” and “resilient.” The “reactive” group included nurses who had higher risk factor scores (psychological distress and adjustment disorder), whereas the “resilient” group included nurses who had higher protective factor scores (PA, resilience, and PSS). Furthermore, nurses in the “reactive” group were younger, with greater seniority, worse self-rated health, and a higher frequency of kidnapped family members compared to nurses from the “resilient” group.
Nurses in wartime are at risk if identified as “reactive.” Identifying these profiles can assist in developing effective support practices to help nurses cope with wartime challenges and maintain their mental well-being.
Healthcare organizations should tailor interventions to prepare and support nurses of various ages and experience levels, during and after conflicts. This approach aims to reduce risk factors and promote protective factors among nurses during wartime.
Non-ventilator hospital-acquired pneumonia (NV HAP) is a common complication for hospitalized patients. NV HAP develops when patients aspirate oral secretions containing pathogenic bacteria. Appropriate oral hygiene can help mitigate NV HAP development. Hospital staff, including nursing assistants, play an important role in ensuring that these cares are completed.
A quasi-experimental pre–post design was used to evaluate outcomes before and after implementation of a structured oral hygiene education program.
A structured oral hygiene program was developed and implemented in a large quaternary hospital. Change in NA knowledge, attitudes, and behaviors before and after implementation of the oral hygiene program was evaluated. Retrospective patient outcomes before and after the intervention were analyzed to detect changes in NV HAP rates.
Following the education, nursing assistant knowledge of recommended frequency of oral care for patients who are NPO increased (67.2% vs. 82.1%, p = 0.003). NAs were more likely to report oral hygiene tools including oral suctioning (80.8% vs. 90.2%, p = 0.005) and toothbrushes (89.3% vs. 95.3%, p = 0.031). The unadjusted incidence of NV HAP was significantly lower in the post-intervention cohort (0.25%) compared to the pre-intervention cohort (0.74%), p < 0.001. In the adjusted model, non-invasive positive pressure ventilation increased the odds of NV HAP by nearly sevenfold (AOR = 6.88, 95% CI: 3.99, 11.39).
Focused education for NAs is an effective strategy to increase knowledge related to oral hygiene. Implementing a structured oral hygiene program for NAs appears to be a promising practice to decrease NV HAP.
The effectiveness of health interventions delivered via a combination of in-person and electronic social networking services for caregivers of stroke survivors remains uncertain. This study evaluates the feasibility of implementing educational and peer support programs for these caregivers through such platforms.
Quasi-experimental design.
This study included 105 caregiver–survivor dyads, with 54 dyads allocated to the intervention group and the remaining 51 to the control group. The LINE intervention comprised a combination of in-person and electronic social networking services including stroke and rehabilitation education, problem-solving skills training, long-term care information support, and 24-h peer and professional support for caregivers. The outcomes were assessed at baseline, after 1 month, and after 3 months, and encompassed caregivers' care burden, depressive symptoms, perceived social support, and quality of life, as well as the rehabilitation adherence and depressive symptoms of stroke survivors. Generalized estimating equations were used to examine group differences. The data were collected between August 2021 and October 2022.
The average age of the caregivers was 48.3 years. Caregivers in the intervention group reported reduced care burdens and enhanced perceptions of social support and quality of life as compared to those in the control group. Additionally, stroke survivors in the intervention group were less likely to exhibit high-risk depressive symptoms.
Delivering a stroke caregiver support intervention via in-person and electronic social networking services, such as LINE, effectively reduced the care burden for caregivers of stroke survivors. Additionally, it enhanced caregivers' perceived social support and quality of life.
This study demonstrated that caregiver education and peer support programs administered through a combination of in-person and electronic social networking services can serve as an effective support system for the psychosocial health of stroke caregivers. These findings support the integration of such interventions into standard clinical practice by healthcare providers or governmental bodies.
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