Introduction: Precision Health (PH) holds the promise of revolutionizing healthcare by enabling personalized disease prevention and management through the integration of genomic data, lifestyle factors, environmental influences, and other social determinants of health (SDoH). However, the absence of a baseline assessment of knowledge, skills, and attitudes (KSAs) of practicing nurses' capacity for PH hinders its integration. The purpose of this study is to determine the capacity of practicing Registered Nurses (RNs) for PH across the United States and to assess the validity and reliability of a tool designed for this use-the Precision Health Nurse Capacity Scale (PHNCS).
Design/method: A descriptive exploratory study was conducted to evaluate the capacity of practicing RNs for this evolving phenomenon, PH, using a convenience sample. The survey was sent via email and made available to all members of the American Nurses Association (ANA) who work in a variety of practice environments. The ANA represents the over 4 million nurses practicing in the United States.
Results: The majority of nurse respondents felt it is important for nurses to become more educated about all aspects of PH including SDoH but they lack confidence in the integration of PH. The PHNCS was found to be a valid and reliable tool in measuring the capacity of nurses to practice PH.
Conclusion: The incorporation of PH into nursing practice suffers an immediate impediment: the lack of know-how of the US nursing workforce. This inaugural data on KSAs for PH establishes a logical baseline from which the requisite education and training should commence.
Clinical relevance: Precision Health is an emerging healthcare approach in the United States and globally. Enabling it will require a nursing workforce prepared with the requisite KSAs. Determining the capacity of the nursing workforce is a foundational step to begin this process.
Introduction: Given that stroke is a leading cause of disability and mortality worldwide, there is an urgent need for a coordinated healthcare approach to mitigate its effects. The objectives of this study were to perform a systematic review and meta-analysis of stroke integrated care models and develop recommendations for a representative model.
Design: A systematic review and meta-analysis.
Methods: The literature search identified randomized controlled trials comparing integrated care models with standard care for stroke patients. The included studies followed PICOs inclusion criteria. The qualitative analysis included creating a flowchart for the literature screening process, and tables detailing the basic characteristics of the included studies, the adherence to the ten principles and the results of the quality assessments. Subsequently, quantitative meta-analytical procedures were conducted to statistically pool the data and quantify the effects of the integrated care models on stroke patients' health-related quality of life, activities of daily living, and depression. The China National Knowledge Infrastructure (CNKI), Wanfang Data, Chongqing VIP Chinese Science and Technology Periodical Database (VIP), China Biology Medicine Disc (CBMDISC), Cochrane Library, Cumulated Index to Nursing and Allied Health Literature (CINAHL), PubMed, Web of Science, Embase, Google Scholar, and Clinical Trials were searched from inception to March 13, 2024.
Results: Of the 2547 obtained articles, 19 were systematically reviewed and 15 were included in the meta-analysis. The integrated care models enhanced stroke patients' health-related quality of life, ability to perform activities of daily living, and reduced depression. Adherence to the 10 principles varied: comprehensive services, patient focus, and standardized care delivery had strong implementation, while gaps were noted in geographic coverage, information systems, governance structures, and financial management.
Conclusion: Integrated care models improve outcomes for stroke patients and adherence to the 10 principles is vital for their implementation success. This study's findings call for a more standardized approach to implementing integrated care models, emphasizing the need for integrated services, patient-centred care, and interdisciplinary collaboration, while also addressing the identified gaps in terms of integration efforts.
Clinical relevance: This study provides evidence-based recommendations on the most effective integrated care approaches for stroke patients, potentially leading to better patient outcomes, reduced healthcare costs, and improved quality of life.
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.
Introduction: 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.
Design: Scoping survey.
Method: 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.
Results: 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.
Conclusion: 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.
Clinical relevance: 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.
Aims: 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.
Design: Qualitative multimethod design.
Methods: 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.
Results: 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.
Conclusion: 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.
Implications for the profession: 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.
Introduction: 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]).
Design: This study utilizes a cross-sectional design.
Method: 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.
Findings: 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.
Conclusion: 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.
Clinical relevance: 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.
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