Hasil untuk "Nursing"

E. Hodgman

M. Rogers

R. Hughes, M. Huby

J. Gurwitz, T. Field, J. Avorn et al.

B. Sjöström, L. Dahlgren

Hongyan Cheng, Chengzhang Yu, Yanshu Shi et al.

The post-pandemic surge in healthcare demand, coupled with critical nursing shortages, has placed unprecedented pressure on medical triage systems, necessitating innovative AI-driven solutions. We present a multi-agent interactive intelligent system for medical triage that addresses three fundamental challenges in current AI-based triage systems: inadequate medical specialization leading to misclassification, heterogeneous department structures across healthcare institutions, and inefficient detail-oriented questioning that impedes rapid triage decisions. Our system employs three specialized agents--RecipientAgent, InquirerAgent, and DepartmentAgent--that collaborate through Inquiry Guidance mechanism and Classification Guidance Mechanism to transform unstructured patient symptoms into accurate department recommendations. To ensure robust evaluation, we constructed a comprehensive Chinese medical triage dataset from "Ai Ai Yi Medical Network", comprising 3,360 real-world cases spanning 9 primary departments and 62 secondary departments. Experimental results demonstrate that our multi-agent system achieves 89.6% accuracy in primary department classification and 74.3% accuracy in secondary department classification after four rounds of patient interaction. The system's dynamic matching based guidance mechanisms enable efficient adaptation to diverse hospital configurations while maintaining high triage accuracy. We successfully developed this multi-agent triage system that not only adapts to organizational heterogeneity across healthcare institutions but also ensures clinically sound decision-making.

Abu Sufian, Cosimo Distante, Marco Leo et al.

Text-to-image (T2I) generative models are largely used in AI-powered real-world applications and value creation. However, their strategic deployment raises critical concerns for responsible AI management, particularly regarding the reproduction and amplification of race- and gender-related stereotypes that can undermine organizational ethics. In this work, we investigate whether such societal biases are systematically encoded within the pretrained latent spaces of state-of-the-art T2I models. We conduct an empirical study across the five most popular open-source models, using ten neutral, profession-related prompts to generate 100 images per profession, resulting in a dataset of 5,000 images evaluated by diverse human assessors representing different races and genders. We demonstrate that all five models encode and amplify pronounced societal skew: caregiving and nursing roles are consistently feminized, while high-status professions such as corporate CEO, politician, doctor, and lawyer are overwhelmingly represented by males and mostly White individuals. We further identify model-specific patterns, such as QWEN-Image's near-exclusive focus on East Asian outputs, Kandinsky's dominance of White individuals, and SDXL's comparatively broader but still biased distributions. These results provide critical insights for AI project managers and practitioners, enabling them to select equitable AI models and customized prompts that generate images in alignment with the principles of responsible AI. We conclude by discussing the risks of these biases and proposing actionable strategies for bias mitigation in building responsible GenAI systems. The code and Data Repository: https://github.com/Sufianlab/T2IBias

Chibuike E. Ugwu, Roschelle Fritz, Diane J. Cook et al.

Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don't know") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.

V Gimeno Hernan, I Duran-Muños, MR Del Pino- Jurado et al.

Objetive: In the care of renal patients, prioritising their quality of life and nursing care is essential. Research links patients' perceptions of care quality to improved outcomes such as safety, clinical efficacy, treatment adherence, and preventive practices. This study aimed to evaluate the quality of life and care perception in these patients and explore potential associations between these dimensions. Material and methods: A cross-sectional descriptive study was conducted with 43 patients attending an advanced CKD clinic. Quality of life was assessed using the KDQOL-36 questionnaire, while the IECPAX questionnaire measured perceived care quality. Sociodemographic and clinical data were collected from patient records. Participants completed the questionnaires during routine visits, with scores analysed to identify associations between variables. Results: The study included 60% men (n=28) and 32% women (n=15), with a mean age of 78 years . Among participants, 45% were diabetic, 79% hypertensive, and 58% took more than five medications daily. Mean scores were 78.76 for KDQOL-36 and 5.54 for IECPAX. Significant differences were found in the physical role domain between men and women (p=0.01) and for individuals over 65 years (p=0.04). Higher IECPAX scores were associated with taking more than five medications (p=0.05). However, no correlation was observed between KDQOL-36 and IECPAX scores. Conclusions: The findings suggest that quality of life and perceived care quality are independent in advanced CKD patients. While this study provides insights, larger multicentre studies are needed to validate these results. These findings highlight the importance of addressing both aspects separately to improve outcomes in this population.

Hanne Marie Rostad, Lucille Xiang, Elizabeth M. White

Abstract Background Health disparities exist across healthcare settings, including nursing homes, contributing to preventable differences in care quality. Health disparities are a global issue, yet most studies on nursing home health disparities have been conducted in the United States. In this scoping review, our objective was to synthesize methods used in U.S. nursing home disparities research to gain insights to inform similar research in other countries. Specifically, we summarized different approaches for conceptualizing and measuring health disparities, available data sources, study designs, and analytic strategies. Methods We employed two parallel search strategies across five databases, targeting specific aspects of health disparities and broader concepts. Study selection was conducted independently by two reviewers. Using a numerical and analytic approach, we categorized and summarized the results. Results The search yielded 6,817 records, with 82 unique studies meeting the inclusion criteria. All studies used quantitative methods, with only two incorporating mixed methods. Most were observational cross-sectional studies (n = 60), while 21 were longitudinal studies, and 1 was a randomized controlled trial. Most studies used administrative data (n = 62). The majority (n = 65) measured differences in health outcomes across nursing homes. A significant number of studies (n = 71) focused on racial and/or ethnic health disparities, and a few studied clinical conditions (n = 7), rural–urban location of the nursing home (n = 4), socioeconomic factors (n = 4), age (n = 1), and sex (n = 1) as characteristics to measure disparity. Outcomes were grouped into five domains: 1) Quality of care measures (n = 54), 2) Infection and infection prevention (n = 22), 3) Transitions and acute care utilization (n = 19), 4) Behavioral and mental health (n = 18), and 5) Palliative care, end-of-life and death (n = 13). Across the five domains, the most prevalent outcome category studied was ‘Hospitalization and emergency room use’ (n = 15). Conclusion This review highlights key issues for future research on health disparities in nursing homes, including the need to: 1) clarify concepts of health disparities and health equity; 2) move beyond mere descriptions of disparities to identify underlying factors contributing to those disparities; 3) broaden examination of disparities beyond a single axis such as race or sex; 4) integrate more qualitative data to capture nuances that cannot be measured from quantitative data; and 5) specify whether within or across nursing home differences are studied.

Zilin Zhao, Hejia Wan, Xiaohui Liu et al.

Yuexing Hao, Jason M. Holmes, Jared Hobson et al.

In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a patient's care journey. However, responding to these patients' inquiries has become a significant burden on healthcare workflows, consuming considerable time for clinical care teams. To address this, we introduce RadOnc-GPT, a specialized Large Language Model (LLM) powered by GPT-4 that has been designed with a focus on radiotherapeutic treatment of prostate cancer with advanced prompt engineering, and specifically designed to assist in generating responses. We integrated RadOnc-GPT with patient electronic health records (EHR) from both the hospital-wide EHR database and an internal, radiation-oncology-specific database. RadOnc-GPT was evaluated on 158 previously recorded in-basket message interactions. Quantitative natural language processing (NLP) analysis and two grading studies with clinicians and nurses were used to assess RadOnc-GPT's responses. Our findings indicate that RadOnc-GPT slightly outperformed the clinical care team in "Clarity" and "Empathy," while achieving comparable scores in "Completeness" and "Correctness." RadOnc-GPT is estimated to save 5.2 minutes per message for nurses and 2.4 minutes for clinicians, from reading the inquiry to sending the response. Employing RadOnc-GPT for in-basket message draft generation has the potential to alleviate the workload of clinical care teams and reduce healthcare costs by producing high-quality, timely responses.

Judith Owokuhaisa, Eleanor Turyakira, Frank Ssedyabane et al.

Abstract Background Cervical cancer continues to threaten women’s health, especially in low-resource settings. Regular follow-up after screening and treatment is an effective strategy for monitoring treatment outcomes. Consequently, understanding the factors contributing to patient non-attendance of scheduled follow-up visits is vital to providing high-quality care, reducing morbidity and mortality, and unnecessary healthcare costs in low-resource settings. Methods A descriptive qualitative study was done among healthcare providers and patients who attended the cervical cancer screening clinic at Mbarara Regional Referral Hospital in southwestern Uganda. In-depth interviews were conducted using a semi-structured interview guide. Interviews were audio-recorded, transcribed verbatim, and thematically analysed in line with the social-ecological model to identify barriers and facilitators. Results We conducted 23 in-depth interviews with 5 healthcare providers and 18 patients. Health system barriers included long waiting time at the facility, long turnaround time for laboratory results, congestion and lack of privacy affecting counselling, and healthcare provider training gaps. The most important interpersonal barrier among married women was lacking support from male partners. Individual-level barriers were lack of money for transport, fear of painful procedures, emotional distress, and illiteracy. Inadequate and inaccurate information was a cross-cutting barrier across the individual, interpersonal, and community levels of the socio-ecological model. The facilitators were social support, positive self-perception, and patient counselling. Conclusions Our study revealed barriers to retention in care after cervical cancer screening, including lack of partner support, financial and educational constraints, and inadequate information. It also found facilitators that included social support, positive self-perception, and effective counselling.

Terri Kean, Patricia Charlton, Rima Azar et al.

Objective To describe stakeholder characteristics and perspectives about experiences, challenges and information needs related to the use of environmental scans (ESs).Design Cross-sectional study.Setting and participants A web-based survey platform was used to disseminate an online survey to stakeholders who had experience with conducting ESs in a health services delivery context (eg, researchers, policy makers, practitioners). Participants were recruited through purposive and snowball sampling. The survey was disseminated internationally, was available in English and French, and remained open for 6 weeks (15 October to 30 November 2022).Analysis Descriptive statistics were used to describe the characteristics and experiences of stakeholders. Thematic analysis was used to analyse the open-text questions.Results Of 47 participants who responded to the survey, 94% were from Canada, 4% from the USA and 2% from Australia. Respondents represented academic institutions (57%), health agency/government (32%) and non-government organisations or agencies (11%). Three themes were identified: (a) having a sense of value and utility; (b) experiencing uncertainty and confusion; and (c) seeking guidance. The data suggest stakeholders found value and utility in ESs and conducted them for varied purposes including to: (a) enhance knowledge, understanding and learning about the current landscape or state of various features of health services delivery (eg, programmes, practices, policies, services, best practices); (b) expose needs, service barriers, challenges, gaps, threats, opportunities; (c) help guide action for planning, policy and programme development; and (d) inform recommendations and decision-making. Stakeholders also experienced conceptual, methodological and practical barriers when conducting ESs, and expressed a need for methodological guidance delivered through published guidelines, checklists and other means.Conclusion ESs have value and utility for addressing health services delivery concerns, but conceptual and methodological challenges exist. Further research is needed to help advance the ES as a distinct design that provides a systematic approach to planning and conducting ESs.

Caroline E. Brown, Mary A. Wickline, L. Ecoff et al.

Oliver Eberle, Ilias Chalkidis, Laura Cabello et al.

Contrastive explanations, where one decision is explained in contrast to another, are supposed to be closer to how humans explain a decision than non-contrastive explanations, where the decision is not necessarily referenced to an alternative. This claim has never been empirically validated. We analyze four English text-classification datasets (SST2, DynaSent, BIOS and DBpedia-Animals). We fine-tune and extract explanations from three different models (RoBERTa, GTP-2, and T5), each in three different sizes and apply three post-hoc explainability methods (LRP, GradientxInput, GradNorm). We furthermore collect and release human rationale annotations for a subset of 100 samples from the BIOS dataset for contrastive and non-contrastive settings. A cross-comparison between model-based rationales and human annotations, both in contrastive and non-contrastive settings, yields a high agreement between the two settings for models as well as for humans. Moreover, model-based explanations computed in both settings align equally well with human rationales. Thus, we empirically find that humans do not necessarily explain in a contrastive manner.9 pages, long paper at ACL 2022 proceedings.

Cameron Zachreson, Ruarai Tobin, Camelia Walker et al.

Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness. We developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated} under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations. Our simulations showed that general isolation of residents did not provide substantial benefits beyond those achieved through screening, isolation of confirmed cases, and deployment of PPE. Our conclusions are sensitive to assumptions about the proportion of total contacts in a facility accounted for by casual interactions between residents.

Rodi Laanen, Maedeh Nasri, Richard van Dijk et al.

Advanced real-time location systems (RTLS) allow for collecting spatio-temporal data from human movement behaviours. Tracking individuals in small areas such as schoolyards or nursing homes might impose difficulties for RTLS in terms of positioning accuracy. However, to date, few studies have investigated the performance of different localisation systems regarding the classification of human movement patterns in small areas. The current study aims to design and evaluate an automated framework to classify human movement trajectories obtained from two different RTLS: Global Navigation Satellite System (GNSS) and Ultra-wideband (UWB), in areas of approximately 100 square meters. Specifically, we designed a versatile framework which takes GNSS or UWB data as input, extracts features from these data and classifies them according to the annotated spatial patterns. The automated framework contains three choices for applying noise removal: (i) no noise removal, (ii) Savitzky Golay filter on the raw location data or (iii) Savitzky Golay filter on the extracted features, as well as three choices regarding the classification algorithm: Decision Tree (DT), Random Forest (RF) or Support Vector Machine (SVM). We integrated different stages within the framework with the Sequential Model-Based Algorithm Configuration (SMAC) to perform automated hyperparameter optimisation. The best performance is achieved with a pipeline consisting of noise removal applied to the raw location data with an RF model for the GNSS and no noise removal with an SVM model for the UWB. We further demonstrate through statistical analysis that the UWB achieves significantly higher results than the GNSS in classifying movement patterns.

Zhiqiang Wang, Yiran Pang, Cihan Ulus et al.

Manatees are aquatic mammals with voracious appetites. They rely on sea grass as the main food source, and often spend up to eight hours a day grazing. They move slow and frequently stay in group (i.e. aggregations) in shallow water to search for food, making them vulnerable to environment change and other risks. Accurate counting manatee aggregations within a region is not only biologically meaningful in observing their habit, but also crucial for designing safety rules for human boaters, divers, etc., as well as scheduling nursing, intervention, and other plans. In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input. Because manatees have unique shape and they often stay in shallow water in groups, water surface reflection, occlusion, camouflage etc. making it difficult to accurately count manatee numbers. To address the challenges, we propose to use Anisotropic Gaussian Kernel (AGK), with tunable rotation and variances, to ensure that density functions can maximally capture shapes of individual manatees in different aggregations. After that, we apply AGK kernel to different types of deep neural networks primarily designed for crowd counting, including VGG, SANet, Congested Scene Recognition network (CSRNet), MARUNet etc. to learn manatee densities and calculate number of manatees in the scene. By using generic low quality images extracted from surveillance videos, our experiment results and comparison show that AGK kernel based manatee counting achieves minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed method works particularly well for counting manatee aggregations in environments with complex background.

Tanja Tolar

This workshop aimed to explore the value and importance of reflective practices in academic writing among nursing students at the University of Bradford. The presentation is based around case studies of students who presented to academic skills for support following their failed attempts in assessed reflective essays. In guiding students through their academic writing development, it becomes apparent students often underestimate the value of critical and analytical approaches towards academic writing process when they reflect on their own practical experience. Analysis of the students’ understanding focuses on key stages of learning as outlined in Honey and Mumford (1992) and the application of a process of reflection that is based on Gibbs’ model of reflection but emphasises the importance of involvement of critical reflection. Students’ comments and evaluations of their reflective writing processes were considered and matched with the expectations course leaders hold for their students. This is in line with the importance of dialogue within this approach that McDrury and Alterio have explored (2002). Responses were gathered through a set of open questions given to students after their assignment submission and further insights through a subsequent discussion with their tutors. Through this process, students were supported to gain insight and thus bring their stages of reflective learning to a close by learning that reflection is integral part of their learning patterns as well as their professional development.