Hasil untuk "Nursing"

Marlene Kramer

R. Stone

Mary S. Mittelman, S. Ferris, E. Shulman et al.

L. Rubenstein, K. Josephson, A. Robbins

Tina Koch

L. Desantis, D. Ugarriza

D. Aarsland, J. Larsen, E. Tandberg et al.

L. Gorski

Xinyu Li, Linxuan Zhao, Roberto Martinez-Maldonado et al.

This study examined whether a single ceiling-mounted camera could be used to capture fine-grained learning behaviours in co-located practical learning. In undergraduate nursing simulations, teachers first identified seven observable behaviour categories, which were then used to train a YOLO-based detector. Video data were collected from 52 sessions, and analyses focused on Scenario A because it produced greater behavioural variation than Scenario B. Annotation reliability was high (F1=0.933). On the held-out test set, the model achieved a precision of 0.789, a recall of 0.784, and an mAP@0.5 of 0.827. When only behaviour frequencies were compared, no robust differences were found between high- and low-performing groups. However, when behaviour labels were analysed together with spatial context, clear differences emerged in both task and collaboration performance. Higher-performing teams showed more patient interaction in the primary work area, whereas lower-performing teams showed more phone-related activity and more activity in secondary areas. These findings suggest that behavioural data are more informative when interpreted together with where they occur. Overall, the study shows that a single-camera computer vision approach can support the analysis of teamwork and task engagement in face-to-face practical learning without relying on wearable sensors.

Hoang Khang Phan, Quang Vinh Dang, Noriyo Colley et al.

Endotracheal suctioning (ES) is an invasive yet essential clinical procedure that requires a high degree of skill to minimize patient risk - particularly in home care and educational settings, where consistent supervision may be limited. Despite its critical importance, automated recognition and feedback systems for ES training remain underexplored. To address this gap, this study proposes a unified, LLM-centered framework for video-based activity recognition benchmarked against conventional machine learning and deep learning approaches, and a pilot study on feedback generation. Within this framework, the Large Language Model (LLM) serves as the central reasoning module, performing both spatiotemporal activity recognition and explainable decision analysis from video data. Furthermore, the LLM is capable of verbalizing feedback in natural language, thereby translating complex technical insights into accessible, human-understandable guidance for trainees. Experimental results demonstrate that the proposed LLM-based approach outperforms baseline models, achieving an improvement of approximately 15-20\% in both accuracy and F1 score. Beyond recognition, the framework incorporates a pilot student-support module built upon anomaly detection and explainable AI (XAI) principles, which provides automated, interpretable feedback highlighting correct actions and suggesting targeted improvements. Collectively, these contributions establish a scalable, interpretable, and data-driven foundation for advancing nursing education, enhancing training efficiency, and ultimately improving patient safety.

Edouard Lansiaux, Ramy Azzouz, Emmanuel Chazard et al.

Emergency departments struggle with persistent triage errors, especially undertriage and overtriage, which are aggravated by growing patient volumes and staff shortages. This study evaluated three AI models [TRIAGEMASTER (NLP), URGENTIAPARSE (LLM), and EMERGINET (JEPA)] against the FRENCH triage scale and nurse practice, using seven months of adult triage data from Roger Salengro Hospital in Lille, France. Among the models, the LLM-based URGENTIAPARSE consistently outperformed both AI alternatives and nurse triage, achieving the highest accuracy (F1-score 0.900, AUC-ROC 0.879) and superior performance in predicting hospitalization needs (GEMSA). Its robustness across structured data and raw transcripts highlighted the advantage of LLM architectures in abstracting patient information. Overall, the findings suggest that integrating LLM-based AI into emergency department workflows could significantly enhance patient safety and operational efficiency, though successful adoption will depend on addressing limitations and ensuring ethical transparency.

Yichen Zhao, Yuhua Wang, Xi Cheng et al.

Metabolic syndrome (MetS) is a medication condition characterized by abdominal obesity, insulin resistance, hypertension and hyperlipidemia. It increases the risk of majority of chronic diseases, including type 2 diabetes mellitus, and affects about one quarter of the global population. Therefore, early detection and timely intervention for MetS are crucial. Standard diagnosis for MetS components requires blood tests conducted within medical institutions. However, it is frequently underestimated, leading to unmet need for care for MetS population. This study aims to use the least physiological data and free texts about exercises related activities, which are obtained easily in daily life, to diagnosis MetS. We collected the data from 40 volunteers in a nursing home and used data augmentation to reduce the imbalance. We propose a deep learning framework for classifying MetS that integrates natural language processing (NLP) and exercise monitoring. The results showed that the best model reported a high positive result (AUROC=0.806 and REC=76.3%) through 3-fold cross-validation. Feature importance analysis revealed that text and minimum heart rate on a daily basis contribute the most in the classification of MetS. This study demonstrates the potential application of data that are easily measurable in daily life for the early diagnosis of MetS, which could contribute to reducing the cost of screening and management for MetS population.

Alvin Combrink, Stephie Do, Kristofer Bengtsson et al.

The effects of personnel scheduling on the quality of care and working conditions for healthcare personnel have been thoroughly documented. However, the ever-present demand and large variation of constraints make healthcare scheduling particularly challenging. This problem has been studied for decades, with limited research aimed at applying Satisfiability Modulo Theories (SMT). SMT has gained momentum within the formal verification community in the last decades, leading to the advancement of SMT solvers that have been shown to outperform standard mathematical programming techniques. In this work, we propose generic constraint formulations that can model a wide range of real-world scheduling constraints. Then, the generic constraints are formulated as SMT and MILP problems and used to compare the respective state-of-the-art solvers, Z3 and Gurobi, on academic and real-world inspired rostering problems. Experimental results show how each solver excels for certain types of problems; the MILP solver generally performs better when the problem is highly constrained or infeasible, while the SMT solver performs better otherwise. On real-world inspired problems containing a more varied set of shifts and personnel, the SMT solver excels. Additionally, it was noted during experimentation that the SMT solver was more sensitive to the way the generic constraints were formulated, requiring careful consideration and experimentation to achieve better performance. We conclude that SMT-based methods present a promising avenue for future research within the domain of personnel scheduling.

ChengZhang Yu, YingRu He, Hongyan Cheng 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.

Snježana Mirilović, Sanda Franković

Dolazak Družbe sestara milosrdnica sv. Vinka Paulskog na naše prostore označio je početak pružanja organizirane skrbi bolesnima i potrebitima, ali i podizanja stupnja obrazovanja prvenstveno ženske populacije. Osnivanjem brojnih odgojno-obrazovnih i zdravstveno-karitativnih ustanova na području jugoistočne Europe, sve do kraja Drugoga svjetskog rata, sestre milosrdnice su imale važnu i vodeću ulogu u promicanju i realizaciji svoje misije. U Karlovcu su do sredine 19. stoljeća skrb za bolesne i potrebite pružali vojni liječnici, franjevci i priučeno osoblje. Izgradnja Gradske javne bolnice 1846. godine označava važan korak u razvoju zdravstvene skrbi u Karlovcu. U bolnici su uz liječnike radile sestre milosrdnice sv. Vinka Paulskog i priučeno osoblje. Svrha je ovoga rada istražiti doprinos i djelokrug rada Družbe sestara milosrdnica sv. Vinka Paulskog u Gradskoj javnoj bolnici u Karlovcu u razdoblju od 1. travnja 1888. do 12. listopada 1978. godine.

LI Peiru, ZHANG Wenjie

This paper reviewed the current situation,assessment tools,influencing factors and intervention strategies of sexual dysfunction in patients after acute myocardial infarction,so as to to meet the nursing needs of patients after acute myocardial infarction and improve the long⁃term prognosis, and to provide reference for medical staff to formulate relevant intervention measures in the future.

Stefan Fritsch, Matthias Tschoepe, Vitor Fortes Rey et al.

Medical procedures such as venipuncture and cannulation are essential for nurses and require precise skills. Learning this skill, in turn, is a challenge for educators due to the number of teachers per class and the complexity of the task. The study aims to help students with skill acquisition and alleviate the educator's workload by integrating generative AI methods to provide real-time feedback on medical procedures such as venipuncture and cannulation.

Javier Sánchez-Gálvez, Javier Sánchez-Gálvez, Santiago Martínez-Isasi et al.

IntroductionSilver-releasing dressings are used in the treatment of infected wounds. Despite their widespread use, neither the amount of silver released nor the potential in vivo toxicity is known. The aim of this study was to evaluate the cytotoxic effects and the amount of silver released from commercially available dressings with infected wounds.MethodsThe review was conducted according to the PRISMA statement. The Web of Science, PubMed, Embase, Scopus, and CINAHL databases were searched for studies from 2002 through December 2022. The criteria were as follows: population (human patients with infected wounds); intervention (commercial dressings with clinical silver authorized for use in humans); and outcomes (concentrations of silver ions released into tissues and plasma). Any study based on silver-free dressings, experimental dressings, or dressings not for clinical use in humans should be excluded. According to the type of study, systematic reviews, experimental, quasi-experimental, and observational studies in English, Spanish, or Portuguese were considered. The quality of the selected studies was assessed using the JBI critical appraisal tools. Studies that assessed at least 65% of the included items were included. Data were extracted independently by two reviewers.Results740 articles were found and five were finally selected (all of them quasi-experimental). Heterogeneity was found in terms of study design, application of silver dressings, and methods of assessment, which limited the comparability between studies.ConclusionIn vivo comparative studies of clinical dressings for control of infection lack a standardized methodology that allows observation of all the variables of silver performance at local and systemic levels, as well as evaluation of its cytotoxicity. It cannot be concluded whether the assessed concentrations of released silver in commercial dressings for the topical treatment of infected wounds are cytotoxic to skin cells.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022351041, PROSPERO [CRD42022351041].

Vladimir Tesar, Conor Judge, John Kelleher et al.

Objective ANCA-associated vasculitis (AAV) is a relapsing-remitting disease, resulting in incremental tissue injury. The gold-standard relapse definition (Birmingham Vasculitis Activity Score, BVAS>0) is often missing or inaccurate in registry settings, leading to errors in ascertainment of this key outcome. We sought to create a computable phenotype (CP) to automate retrospective identification of relapse using real-world data in the research setting.Methods We studied 536 patients with AAV and >6 months follow-up recruited to the Rare Kidney Disease registry (a national longitudinal, multicentre cohort study). We followed five steps: (1) independent encounter adjudication using primary medical records to assign the ground truth, (2) selection of data elements (DEs), (3) CP development using multilevel regression modelling, (4) internal validation and (5) development of additional models to handle missingness. Cut-points were determined by maximising the F1-score. We developed a web application for CP implementation, which outputs an individualised probability of relapse.Results Development and validation datasets comprised 1209 and 377 encounters, respectively. After classifying encounters with diagnostic histopathology as relapse, we identified five key DEs; DE1: change in ANCA level, DE2: suggestive blood/urine tests, DE3: suggestive imaging, DE4: immunosuppression status, DE5: immunosuppression change. F1-score, sensitivity and specificity were 0.85 (95% CI 0.77 to 0.92), 0.89 (95% CI 0.80 to 0.99) and 0.96 (95% CI 0.93 to 0.99), respectively. Where DE5 was missing, DE2 plus either DE1/DE3 were required to match the accuracy of BVAS.Conclusions This CP accurately quantifies the individualised probability of relapse in AAV retrospectively, using objective, readily accessible registry data. This framework could be leveraged for other outcomes and relapsing diseases.

Hussein Mozannar, Yuria Utsumi, Irene Y. Chen et al.

A high-risk pregnancy is a pregnancy complicated by factors that can adversely affect the outcomes of the mother or the infant. Health insurers use algorithms to identify members who would benefit from additional clinical support. This work presents the implementation of a real-world ML-based system to assist care managers in identifying pregnant patients at risk of complications. In this retrospective evaluation study, we developed a novel hybrid-ML classifier to predict whether patients are pregnant and trained a standard classifier using claims data from a health insurance company in the US to predict whether a patient will develop pregnancy complications. These models were developed in cooperation with the care management team and integrated into a user interface with explanations for the nurses. The proposed models outperformed commonly used claim codes for the identification of pregnant patients at the expense of a manageable false positive rate. Our risk complication classifier shows that we can accurately triage patients by risk of complication. Our approach and evaluation are guided by human-centric design. In user studies with the nurses, they preferred the proposed models over existing approaches.