Hasil untuk "Nursing"

J. Teno, B. Clarridge, Virginia Casey et al.

R. Spitzer

J. A. N. Eedleman, P. E. B. Uerhaus, M. S. Tewart

Divine Darlington Logo, Prakash B. Kodali, Judith Anaman-Torgbor et al.

Introduction Tobacco use among adolescents is a concern in the Upper East Region of Ghana. We estimated the prevalence and identified factors contributing to single and multiple use of tobacco products among junior high school students in Ghana. Methods We conducted a cross-sectional analysis of a baseline survey of a schoolbased tobacco control intervention among adolescents in the Upper East Region of Ghana in 2022. A multi-stage cluster sampling approach was employed to identify the study sample, and data were collected using self-administered questionnaires. Current use of single tobacco products (at least one: cigarette, e-cigarette, shisha, or smokeless tobacco products) and multiple products (≥2 products) in the past 30 days was assessed. Multinomial logistic regression was used to assess the association of sociodemographic characteristics, perceptions towards tobacco’s health risks, and exposure to tobacco products with single and multiple product use. Adjusted relative risk ratios (ARRR) and their corresponding 95% confidence intervals (CI) were computed. Results We surveyed 1328 adolescents, comprising an equal proportion of males (49.8%) and females (50.4%). One in five (21.7%) reported using tobacco products, with 11.5% using single products and 13.0% using multiple products. Shisha (13.6%), cigarettes (10.6%), e-cigarettes (8.2%), and smokeless tobacco (6.0%) were used. A number of factors were identified to be associated with tobacco use among adolescents. Conclusions One in five junior high school students used at least one form of tobacco product. Adolescent tobacco use is impacted by demographic factors and risk perceptions. Further studies are needed to better understand these associations.

Tassallah Abdullahi, Shrestha Ghosh, Hamish S Fraser et al.

Persona conditioning can be viewed as a behavioral prior for large language models (LLMs) and is often assumed to confer expertise and improve safety in a monotonic manner. However, its effects on high-stakes clinical decision-making remain poorly characterized. We systematically evaluate persona-based control in clinical LLMs, examining how professional roles (e.g., Emergency Department physician, nurse) and interaction styles (bold vs.\ cautious) influence behavior across models and medical tasks. We assess performance on clinical triage and patient-safety tasks using multidimensional evaluations that capture task accuracy, calibration, and safety-relevant risk behavior. We find systematic, context-dependent, and non-monotonic effects: Medical personas improve performance in critical care tasks, yielding gains of up to $\sim+20\%$ in accuracy and calibration, but degrade performance in primary-care settings by comparable margins. Interaction style modulates risk propensity and sensitivity, but it's highly model-dependent. While aggregated LLM-judge rankings favor medical over non-medical personas in safety-critical cases, we found that human clinicians show moderate agreement on safety compliance (average Cohen's $κ= 0.43$) but indicate a low confidence in 95.9\% of their responses on reasoning quality. Our work shows that personas function as behavioral priors that introduce context-dependent trade-offs rather than guarantees of safety or expertise. The code is available at https://github.com/rsinghlab/Persona\_Paradox.

Sangjun Eom, Tianyi Hu, Wenyi Xu et al.

Early mobilization is a structured protocol designed to facilitate motor recovery in intensive care unit (ICU) patients with ICU-acquired weakness. This process is typically implemented by an interdisciplinary team of nurses, physical therapists, and other healthcare professionals. However, its application is often constrained by the patients' critical conditions, limited mobility, and the challenges of coordinating care within resource-intensive ICU environments. In this study, we developed a patient-centered virtual reality (VR) exergame through an interdisciplinary design process involving clinicians and therapists, tailored to the constraints of critical care. The exergame incorporates progressive mobility levels that mirror early mobilization practices, and includes an embodied avatar to provide guidance and motivation. Using Meta Quest 3 body tracking, the system captures and visualizes patients' movements, thereby providing motivational engagement and quantifiable mobility metrics. We evaluated the exergame in two stages: a dual-user study involving healthy participants and healthcare professionals or students (N = 13), and a subsequent study with cardiothoracic ICU patients (N = 18) to assess feasibility, design validity, and clinical acceptance. Across both studies, participants reported high enjoyment and engagement without discomfort or stress. Furthermore, patients demonstrated increases in movement speed, range of motion, and workspace volume of the upper body across game levels. Physiological monitoring further indicated that the exergame elicited exertion without inducing excessive cardiovascular responses. These findings highlight the feasibility of VR exergames as a clinically acceptable and engaging adjunct to early mobilization in critical care, offering a novel pathway to improve rehabilitation outcomes for ICU patients.

Huy Trinh, Elliot Creager, George Shaker

Radar-based sensing is a promising privacy-preserving alternative to cameras and wearables in settings such as long-term care. Yet detecting quasi-static presence (lying, sitting, or standing with only subtle micro-motions) is difficult for low-resolution SIMO FMCW radar because near-zero Doppler energy is often buried under static clutter. We present Respiratory-Amplification Semi-Static Occupancy (RASSO), an invertible Doppler-domain non-linear remapping that densifies the slow-time FFT (Doppler) grid around 0 m/s before adaptive Capon beamforming. The resulting range-azimuth (RA) maps exhibit higher effective SNR, sharper target peaks, and lower background variance, making thresholding and learning more reliable. On a real nursing-home dataset collected with a short-range 1Tx-3Rx radar, RASSO-RA improves classical detection performance, achieving AUC = 0.981 and recall = 0.920/0.947 at FAR = 1%/5%, outperforming conventional Capon processing and a recent baseline. RASSO-RA also benefits data-driven models: a frame-based CNN reaches 95-99% accuracy and a sequence-based CNN-LSTM reaches 99.4-99.6% accuracy across subjects. A paired session-level bootstrap test confirms statistically significant macro-F1 gains of 2.6-3.6 points (95% confidence intervals above zero) over the non-warped pipeline. These results show that simple Doppler-domain warping before spatial processing can materially improve semi-static occupancy detection with low-resolution radar in real clinical environments.

Jiuyang Liu, Ruizhe Zhang, Lang Ma et al.

Abstract The origin of breast cancer (BC) is widely considered to be a result of multiple factors, including both genetic and dietary influences. Dietary patterns shaped by calorie restriction—defined as reduced energy intake without inducing malnutrition, and varying ratios of the three major nutrients are thought to influence tumorigenesis. However, the complex interplay between caloric restriction, carbohydrate intake ratios, and genetic predisposition in influencing BC risk remains inadequately understood. This study aimed to explore these relationships in greater depth. A prospective cohort study which included 139,829 participants aged 40–72 years was conducted. We evaluated the association between dietary carbohydrate ratio under caloric restriction and the BC risk in a genetic risk group by using Cox proportional hazards regression models. The analysis also included a calculation of polygenic risk score (PRS) based on 304 breast cancer-associated genetic loci. A high dietary carbohydrate ratio pattern under caloric restriction was significantly associated with a 21% reduction in BC risk, respectively (HRG4 VS G1 = 0.80, 95% CI 0.66–0.97, P = 0.021), whereas a low PRS (lowest tertile) was associated with 0.84-fold decrease in risk (HRlow VS high = 0.84, 95%CI 0.72–0.98, P = 0.032). Compared with other participants, those at intermediate genetic risk with low carbohydrate ratio above caloric restriction showed a higher risk of BC (HR = 1.39, 95% CI 1.01–1.90, P = 0.041). In this cohort study, a diet pattern characterized by a high ratio of carbohydrates under conditions of caloric restriction may attenuate the impact of genetic factors on BC risk in individuals of European ancestry.

Jie Pan, Seungwon Lee, Cheligeer Cheligeer et al.

Objectives: Administrative data is commonly used to inform chronic disease prevalence and support health informatics research. This study assessed the validity of coding comorbidity in the International Classification of Diseases, 10th Revision (ICD-10) administrative data. Methods: We analyzed three chart review cohorts (4,008 patients in 2003, 3,045 in 2015, and 9,024 in 2022) in Alberta, Canada. Nurse reviewers assessed the presence of 17 clinical conditions using a consistent protocol. The reviews were linked with administrative data using unique identifiers. We compared the accuracy in coding comorbidity by ICD-10, using chart review data as the reference standard. Results: Our findings showed that the mean difference in prevalence between chart reviews and ICD-10 for these 17 conditions was 2.1% in 2003, 7.6% in 2015, and 6.3% in 2022. Some conditions were relatively stable, such as diabetes (1.9%, 2.1%, and 1.1%) and metastatic cancer (0.3%, 1.1%, and 0.4%). For these 17 conditions, the sensitivity ranged from 39.6-85.1% in 2003, 1.3-85.2% in 2015, and 3.0-89.7% in 2022. The C-statistics for predicting in-hospital mortality using comorbidities by ICD-10 were 0.84 in 2003, 0.81 in 2015, and 0.78 in 2022. Discussion: The under-coding could be primarily due to the increase of hospital patient volumes and the limited time allocated to coders. There is a potential to develop artificial intelligence methods based on electronic health records to support coding practices and improve coding quality. Conclusion: Comorbidities were increasingly under-coded over 20 years. The validity of ICD-10 decreased but remained relatively stable for certain conditions mandated for coding. The under-coding exerted minimal impact on in-hospital mortality prediction.

Divya Mereddy, Marcos Quinones-Grueiro, Ashwin T S et al.

This study examines how Critical Care Air Transport Team (CCATT) members are trained using mixed-reality simulations that replicate the high-pressure conditions of aeromedical evacuation. Each team - a physician, nurse, and respiratory therapist - must stabilize severely injured soldiers by managing ventilators, IV pumps, and suction devices during flight. Proficient performance requires clinical expertise and cognitive skills, such as situational awareness, rapid decision-making, effective communication, and coordinated task management, all of which must be maintained under stress. Recent advances in simulation and multimodal data analytics enable more objective and comprehensive performance evaluation. In contrast, traditional instructor-led assessments are subjective and may overlook critical events, thereby limiting generalizability and consistency. However, AI-based automated and more objective evaluation metrics still demand human input to train the AI algorithms to assess complex team dynamics in the presence of environmental noise and the need for accurate re-identification in multi-person tracking. To address these challenges, we introduce a systematic, data-driven assessment framework that combines Cognitive Task Analysis (CTA) with Multimodal Learning Analytics (MMLA). We have developed a domain-specific CTA model for CCATT training and a vision-based action recognition pipeline using a fine-tuned Human-Object Interaction model, the Cascade Disentangling Network (CDN), to detect and track trainee-equipment interactions over time. These interactions automatically yield performance indicators (e.g., reaction time, task duration), which are mapped onto a hierarchical CTA model tailored to CCATT operations, enabling interpretable, domain-relevant performance evaluations.

Andrea E. Davidson, Jessica M. Ray, Ayush K. Patel et al.

This study reports the findings of qualitative interview sessions conducted with ICU clinicians for the co-design of a system user interface of an artificial intelligence (AI)-driven clinical decision support (CDS) system. This system integrates medical record data with wearable sensor, video, and environmental data into a real-time dynamic model that quantifies patients' risk of clinical decompensation and risk of developing delirium, providing actionable alerts to augment clinical decision-making in the ICU setting. Co-design sessions were conducted as semi-structured focus groups and interviews with ICU clinicians, including physicians, mid-level practitioners, and nurses. Study participants were asked about their perceptions on AI-CDS systems, their system preferences, and were asked to provide feedback on the current user interface prototype. Session transcripts were qualitatively analyzed to identify key themes related to system utility, interface design features, alert preferences, and implementation considerations. Ten clinicians participated in eight sessions. The analysis identified five themes: (1) AI's computational utility, (2) workflow optimization, (3) effects on patient care, (4) technical considerations, and (5) implementation considerations. Clinicians valued the CDS system's multi-modal continuous monitoring and AI's capacity to process large volumes of data in real-time to identify patient risk factors and suggest action items. Participants underscored the system's unique value in detecting delirium and promoting non-pharmacological delirium prevention measures. The actionability and intuitive interpretation of the presented information was emphasized. ICU clinicians recognize the potential of an AI-driven CDS system for ICU delirium and acuity to improve patient outcomes and clinical workflows.

Farah Luthfi Kaulina, Sukihananto Sukihananto

Non-communicable diseases (NCDs) are still a morbidity and mortality problem in Southeast Asia. However, NCD in Southeast Asia still needs to be handled faster. WHO recommends the use of digital in treating NCDs in Southeast Asia. Therefore, this literature review study aims to describe how mHealth is utilized to overcome the problem of NCDs in Southeast Asian countries. The author collected articles using Google Scholar and Proquest, which were published in 2019-2023. The focus of the search was articles published in English-language Research Journals. Researchers used advanced search with the keywords NCD, Non-communicable diseases, mHealth, Mobile Health, Nursing, and Health Promotion. Keywords are combined using Boolean and/or the online database that the researcher chose. Articles that have been filtered are filtered again by selecting research locations in Southeast Asian countries. Ten articles obtained came from research in Southeast Asian countries Indonesia (n=4), Malaysia (n=1), Singapore (n=1), Vietnam (n=1), Thailand (1), Cambodia (n=1), Philippines (n=1). All articles discussed the use of mHealth for NCD management in their countries and aimed to determine the barriers (n=3), feasibility (n=1), effectiveness (n=2), impact (n=2), potential (n=1), perception (n=1), and perspective (n=1) of service providers, as well as the experience of using mHealth in remote areas (n=1). It can be concluded that mHealth can be used for independent screening for PTM, providing education about NCDs, and can be applied in rural areas as a comprehensive effort to handle NCDs.

Jenna Keane, Ciara Ryan, Ruth Usher

Background National standards for nursing homes in Ireland require that residents are offered a choice of recreational and stimulating activities to meet their needs and preferences.Aims/Objectives To investigate residents’ perceptions of leisure and social occupational choice in nursing homes in Ireland to determine if occupational choice is facilitated.Materials and method Qualitative-descriptive design – nursing home residents completed two semi-structured interviews that explored their experiences of leisure and social occupational engagement.Results Two overarching themes with six associated sub-themes emerged. From residents’ perspectives, social and leisure occupational choice was dependent on: Environmental factors (nursing homes’ Cultural, Social, Physical, and Temporal Environments) and Personal factors (residents’ Health Status and Personal Attitudes).Conclusion The cultural environment had the most significant influence on residents’ leisure and social occupational choice, highlighting the importance of person-centred care within nursing homes, to promote occupational choice. Resident’s health status was also identified as a contributing factor.Significance Occupational therapists could play a critical role in supporting the leisure and social occupational choices of nursing home residents by developing residents’ skills, educating staff and adapting tasks and the environment to limit/reduce occupational deprivation.

Ritam Ghosh, Nibraas Khan, Miroslava Migovich et al.

Apathy impairs the quality of life for older adults and their care providers. While few pharmacological remedies exist, current non-pharmacologic approaches are resource intensive. To address these concerns, this study utilizes a user-centered design (UCD) process to develop and test a set of dyadic activities that provide physical, cognitive, and social stimuli to older adults residing in long-term care (LTC) communities. Within the design, a novel framework that combines socially assistive robots and non-immersive virtual reality (SAR-VR) emphasizing human-robot interaction (HRI) and human-computer interaction (HCI) is utilized with the roles of the robots being coach and entertainer. An interdisciplinary team of engineers, nurses, and physicians collaborated with an advisory panel comprising LTC activity coordinators, staff, and residents to prototype the activities. The study resulted in four virtual activities: three with the humanoid robot, Nao, and one with the animal robot, Aibo. Fourteen participants tested the acceptability of the different components of the system and provided feedback at different stages of development. Participant approval increased significantly over successive iterations of the system highlighting the importance of stakeholder feedback. Five LTC staff members successfully set up the system with minimal help from the researchers, demonstrating the usability of the system for caregivers. Rationale for activity selection, design changes, and both quantitative and qualitative results on the acceptability and usability of the system have been presented. The paper discusses the challenges encountered in developing activities for older adults in LTCs and underscores the necessity of the UCD process to address them.

Ankan Mullick, Sombit Bose, Rounak Saha et al.

In an ever-expanding world of domain-specific knowledge, the increasing complexity of consuming, and storing information necessitates the generation of summaries from large information repositories. However, every persona of a domain has different requirements of information and hence their summarization. For example, in the healthcare domain, a persona-based (such as Doctor, Nurse, Patient etc.) approach is imperative to deliver targeted medical information efficiently. Persona-based summarization of domain-specific information by humans is a high cognitive load task and is generally not preferred. The summaries generated by two different humans have high variability and do not scale in cost and subject matter expertise as domains and personas grow. Further, AI-generated summaries using generic Large Language Models (LLMs) may not necessarily offer satisfactory accuracy for different domains unless they have been specifically trained on domain-specific data and can also be very expensive to use in day-to-day operations. Our contribution in this paper is two-fold: 1) We present an approach to efficiently fine-tune a domain-specific small foundation LLM using a healthcare corpus and also show that we can effectively evaluate the summarization quality using AI-based critiquing. 2) We further show that AI-based critiquing has good concordance with Human-based critiquing of the summaries. Hence, such AI-based pipelines to generate domain-specific persona-based summaries can be easily scaled to other domains such as legal, enterprise documents, education etc. in a very efficient and cost-effective manner.

Teerath Kumar, Alessandra Mileo, Malika Bendechache

Data augmentation has become a pivotal tool in enhancing the performance of computer vision tasks, with the KeepOriginalAugment method emerging as a standout technique for its intelligent incorporation of salient regions within less prominent areas, enabling augmentation in both regions. Despite its success in image classification, its potential in addressing biases remains unexplored. In this study, we introduce an extension of the KeepOriginalAugment method, termed FaceKeepOriginalAugment, which explores various debiasing aspects-geographical, gender, and stereotypical biases-in computer vision models. By maintaining a delicate balance between data diversity and information preservation, our approach empowers models to exploit both diverse salient and non-salient regions, thereby fostering increased diversity and debiasing effects. We investigate multiple strategies for determining the placement of the salient region and swapping perspectives to decide which part undergoes augmentation. Leveraging the Image Similarity Score (ISS), we quantify dataset diversity across a range of datasets, including Flickr Faces HQ (FFHQ), WIKI, IMDB, Labelled Faces in the Wild (LFW), UTK Faces, and Diverse Dataset. We evaluate the effectiveness of FaceKeepOriginalAugment in mitigating gender bias across CEO, Engineer, Nurse, and School Teacher datasets, utilizing the Image-Image Association Score (IIAS) in convolutional neural networks (CNNs) and vision transformers (ViTs). Our findings shows the efficacy of FaceKeepOriginalAugment in promoting fairness and inclusivity within computer vision models, demonstrated by reduced gender bias and enhanced overall fairness. Additionally, we introduce a novel metric, Saliency-Based Diversity and Fairness Metric, which quantifies both diversity and fairness while handling data imbalance across various datasets.

Gina M. Gehling, Keesha Powell-Roach, Diana J. Wilkie et al.

BackgroundScientists have speculated genetic variants may contribute to an individual's unique pain experience. Although research exists regarding the relationship between single nucleotide polymorphisms and sickle cell disease-related pain, this literature has not been synthesized to help inform future precision health research for sickle cell disease-related pain. Our primary aim of this systematic review was to synthesize the current state of scientific literature regarding single nucleotide polymorphisms and their association with sickle cell disease-related pain.MethodsUsing the Prisma guidelines, we conducted our search between December 2021–April 2022. We searched PubMed, Web of Science, CINAHL, and Embase databases (1998–2022) and selected all peer-reviewed articles that included reports of associations between single nucleotide polymorphisms and sickle cell disease-related pain outcomes.ResultsOur search yielded 215 articles, 80 of which were duplicates, and after two reviewers (GG, JD) independently screened the 135 non-duplicate articles, we retained 22 articles that met the study criteria. The synthesis of internationally generated evidence revealed that this scientific area remains predominantly exploratory in nature, with only three studies reporting sufficient power for genetic association. Sampling varied across studies with a range of children to older adults with SCD. All of the included articles (n = 22) examined acute pain, while only nine of those studies also examined chronic pain.ConclusionCurrently, the evidence implicating genetic variation contributing to acute and chronic sickle cell disease-related pain is characterized by modestly powered candidate-gene studies using rigorous SCD-pain outcomes. Effect sizes and directions vary across studies and are valuable for informing the design of future studies. Further research is needed to replicate these associations and extend findings with hypothesis-driven research to inform precision health research.

Yonghao Long, Wang Wei, Tao Huang et al.

Surgical robot automation has attracted increasing research interest over the past decade, expecting its potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied intelligence has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant research. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how the human demonstrations would affect policy learning. In this work, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. We showcase the improvement of our simulation environment with the designed new features, and validate effectiveness of incorporating human factors in embodied intelligence through the use of human demonstrations and reinforcement learning as a representative example. Promising results are obtained in terms of learning efficiency. Lastly, five new surgical robot training tasks are developed and released, with which we hope to pave the way for future research on surgical embodied intelligence. Our learning platform is publicly released and will be continuously updated in the website: https://med-air.github.io/SurRoL.

Majid Farhadloo, Arun Sharma, Shashi Shekhar et al.

We consider the problem of reducing the time needed by healthcare professionals to understand patient medical history via the next generation of biomedical decision support. This problem is societally important because it has the potential to improve healthcare quality and patient outcomes. However, navigating electronic health records is challenging due to the high patient-doctor ratios, potentially long medical histories, the urgency of treatment for some medical conditions, and patient variability. The current electronic health record systems provides only a longitudinal view of patient medical history, which is time-consuming to browse, and doctors often need to engage nurses, residents, and others for initial analysis. To overcome this limitation, we envision an alternative spatial representation of patients' histories (e.g., electronic health records (EHRs)) and other biomedical data in the form of Atlas-EHR. Just like Google Maps allows a global, national, regional, and local view, the Atlas-EHR may start with an overview of the patient's anatomy and history before drilling down to spatially anatomical sub-systems, their individual components, or sub-components. Atlas-EHR presents a compelling opportunity for spatial computing since healthcare is almost a fifth of the US economy. However, the traditional spatial computing designed for geographic use cases (e.g., navigation, land-surveys, mapping) faces many hurdles in the biomedical domain. This paper presents a number of open research questions under this theme in five broad areas of spatial computing.

Varun Nagaraj Rao, Aleksandra Korolova

Targeted advertising platforms are widely used by job advertisers to reach potential employees; thus issues of discrimination due to targeting that have surfaced have received widespread attention. Advertisers could misuse targeting tools to exclude people based on gender, race, location and other protected attributes from seeing their job ads. In response to legal actions, Facebook disabled the ability for explicit targeting based on many attributes for some ad categories, including employment. Although this is a step in the right direction, prior work has shown that discrimination can take place not just due to the explicit targeting tools of the platforms, but also due to the impact of the biased ad delivery algorithm. Thus, one must look at the potential for discrimination more broadly, and not merely through the lens of the explicit targeting tools. In this work, we propose and investigate the prevalence of a new means for discrimination in job advertising, that combines both targeting and delivery -- through the disproportionate representation or exclusion of people of certain demographics in job ad images. We use the Facebook Ad Library to demonstrate the prevalence of this practice through: (1) evidence of advertisers running many campaigns using ad images of people of only one perceived gender, (2) systematic analysis for gender representation in all current ad campaigns for truck drivers and nurses, (3) longitudinal analysis of ad campaign image use by gender and race for select advertisers. After establishing that the discrimination resulting from a selective choice of people in job ad images, combined with algorithmic amplification of skews by the ad delivery algorithm, is of immediate concern, we discuss approaches and challenges for addressing it.