搜索结果：Orthopedic nursing

OBJECTIVE: To explore the effect of fast-track surgery (FTS) based high quality nursing on orthopedic trauma. METHODS: In this retrospective study, 94 patients who received orthopedic trauma surgery in our hospital from December 2018 to November 2020 were included. The patients were assigned to a research group (n=47) or a control group (n=47) according to which nursing method they received. The control group received routine nursing, while the research group also received FTS-based high-quality nursing. Perioperative situation, quality of life score (SF-36) before and after operation, incidence of complications, pain score (VAS) at different time periods after operation, and nursing satisfaction were compared between the two groups. RESULTS: There was no significant difference in operation time or blood loss between groups (P>0.05). The time to getting out of bed for the first time, time to drainage tube removal, and length of hospital stay in the research group were shorter than those in the control group (P<0.001). Repeated measurement analysis of variance revealed that the VAS score of the research group was lower than that of the control group at 1 h, 3 h, 6 h, 24 h and 48 h after operation (P<0.05). Intra-group comparison manifested that the VAS scores of both groups decreased at 1 h, 3 h, 6 h, 24 h and 48 h after operation (P<0.05). Comparison at different time points revealed that the difference was statistically significant (P<0.05). The incidence of complications in the research group (4.26%) was lower than that in the control group (17.02%; P<0.05). The satisfaction rate of nursing in the research group (93.62%) was higher than that in the control group (78.72%; P<0.05). After intervention, the level of superoxide dismutase (SOD) and glutathione (GSH) in both groups decreased with a lesser decrease in the research group. The contents of reactive oxygen species (ROS) and malondialdehyde (MDA) in groups after intervention were higher than those before intervention with a milder increase in the research group. CONCLUSION: FTS mode can shorten the recovery time, reduce the degree of pain and the reduce the time of analgesia. It also promotes the recovery and shortens the hospital stay of patients, and improves their quality of life, with high satisfaction. This may be related to an expeditedd surgical process and reduced oxidative stress response of patients undergoing surgery under the rapid recovery surgical model.

Descriptive and exploratory study conducted in a hospital school in Londrina, Paraná, with the objective of identifying the frequency of the main nursing diagnoses according to the North American Nursing Diagnoses Association in male patients admitted at an orthopedic ward. The sample consisted of 60 patients with an average age of 40.6 years. Data was collected through interview and physical examination. The diagnoses were interpreted based on defining characteristics, risk factors and situations. The average number of nursing diagnoses by patient was 11.5. The most frequent diagnoses were: Risk of infection, Skin integrity, Tissue integrity, Severe pain, Self-care deficit relating to bathing and basic hygiene, Impaired physical mobility, Lack of knowledge, Risk of peripheral neurovascular dysfunction. This study identified patients' needs of care and it helped to establish the relevance of different clinical focuses for orthopedic nursing.

Section One: Concepts in Nursing Practice * Contemporary Nursing Practice * Health Disparities and Culturally Competent Care * Health History and Physical Examination * Patient and Caregiver Teaching * Chronic Illness and Older Adults * Community-Based Nursing and Home Care * Complementary and Alternative Therapies * Stress and Stress Management * Sleep and Sleep Disorders NEW CHAPTER! * Pain * Palliative Care at End of Life * Addictive Behaviors Section Two: Pathophysiologic Mechanisms of Disease * Inflammation and Would Healing * Genetics, Altered Immune Responses, and Transplantation * Infection * Cancer * Fluid, Electrolyte, and Acid-Base Imbalances Section Three: Perioperative Care * Nursing Management: Preoperative Care * Nursing Management: Intraoperative Care * Nursing Management: Postoperative Care Section Four: Problems Related to Altered Sensory Input * Nursing Assessment: Visual and Auditory Systems * Nursing Management: Visual and Auditory Problems * Nursing Assessment: Integumentary System * Nursing Management: Integumentary Problems * Nursing Management: Burns Section Five: Problems of Oxygenation: Ventilation * Nursing Assessment: Respiratory System * Nursing Management: Upper Respiratory Problems * Nursing Management: Lower Respiratory Problems * Nursing Management: Obstructive Pulmonary Diseases Section Six: Problems of Oxygenation: Transport * Nursing Assessment: Hematologic System * Nursing Management: Hematologic Problems Section Seven: Problems of Oxygenation: Perfusion * Nursing Assessment: Cardiovascular System * Nursing Management: Hypertension * Nursing Management: Coronary Artery Disease and Acute Coronary Syndrome * Nursing Management: Heart Failure * Nursing Management: Dysrhythmias * Nursing Management: Inflammatory and Structural Heart Disorders * Nursing Management: Vascular Disorders Section Eight: Problems of Ingestion, Digestion, Absorption, and Elimination * Nursing Assessment: Gastrointestinal System * Nursing Management: Nutritional Problems * Nursing Management: Obesity * Nursing Management: Upper Gastrointestinal Problems * Nursing Management: Lower Gastrointestinal Problems * Nursing Management: Liver, Pancreas, and Biliary Tract Problems Section Nine: Problems of Urinary Function * Nursing Assessment: Urinary System * Nursing Management: Renal and Urologic Problems * Nursing Management: Acute Kidney Injury and Chronic Kidney Disease Section Ten: Problems Related to Regulatory and Reproductive Mechanisms * Nursing Assessment: Endocrine System * Nursing Management: Diabetes Mellitus * Nursing Management: Endocrine Problems * Nursing Assessment: Reproductive System * Nursing Management: Breast Disorders * Nursing Management: Sexually Transmitted Diseases * Nursing Management: Female Reproductive Problems * Nursing Management: Male Reproductive Problems Section Eleven: Problems Related to Movement and Coordination * Nursing Assessment: Nervous System * Nursing Management: Acute Intracranial Problems * Nursing Management: Stroke * Nursing Management: Chronic Neurologic Problems * Nursing Management: Alzheimer's Disease, Dementia, and Delirium * Nursing Management: Peripheral Nerve and Spinal Cord Problems * Nursing Assessment: Musculoskeletal System * Nursing Management: Musculoskeletal Trauma and Orthopedic Surgery * Nursing Management: Musculoskeletal Problems * Nursing Management: Arthritis and Connective Tissue Diseases Section Twelve: Nursing Care in Specialized Settings * Nursing Management: Critical Care * Nursing Management: Shock and Multiple Organ Dysfunction Syndrome * Nursing Management: Respiratory Failure and Acute Respiratory Distress Syndrome * Nursing Management: Emergency, Terrorism, and Disaster Nursing Appendixes: A. Cardiopulmonary Resuscitation (CPR) and Basic Life Support B. Nursing Diagnoses C. Laboratory Values

Recent advancements in large language models (LLMs) have significantly transformed medical systems. However, their potential within specialized domains such as nursing remains largely underexplored. In this work, we introduce NurseLLM, the first nursing-specialized LLM tailored for multiple choice question-answering (MCQ) tasks. We develop a multi-stage data generation pipeline to build the first large scale nursing MCQ dataset to train LLMs on a broad spectrum of nursing topics. We further introduce multiple nursing benchmarks to enable rigorous evaluation. Our extensive experiments demonstrate that NurseLLM outperforms SoTA general-purpose and medical-specialized LLMs of comparable size on different benchmarks, underscoring the importance of a specialized LLM for the nursing domain. Finally, we explore the role of reasoning and multi-agent collaboration systems in nursing, highlighting their promise for future research and applications.

Multilingual orthopedic decision support remains challenging in low-resource healthcare settings, where clinical narratives contain specialized terminology, mixed scripts, incomplete evidence, label imbalance and language-dependent documentation patterns. This article presents a reliability-oriented framework for classifying free-text orthopedic notes in English, Hindi and Punjabi. We compare task-aligned multilingual transformer encoders, a task-fine-tuned DistilBERT baseline, zero-shot instruction-tuned large language models (LLMs) and a domain-adaptive encoder, IndicBERT-HPA. IndicBERT-HPA augments IndicBERT with language-aware orthopedic adapter heads to support clinically relevant multilingual representation learning. Evaluation extends beyond aggregate accuracy to per-class performance, ROC-AUC, AUPRC, expected calibration error, cross-language stability and robustness under controlled balanced and natural-prevalence distributions. The evaluated zero-shot LLMs remain substantially less effective than task-adapted encoders for closed-set classification, with language-dependent instability. Under natural clinical prevalence, IndicBERT-HPA achieves the strongest overall performa

Canine gait analysis using wearable inertial sensors is gaining attention in veterinary clinical settings, as it provides valuable insights into a range of mobility impairments. Neurological and orthopedic conditions cannot always be easily distinguished even by experienced clinicians. The current study explored and developed a deep learning approach using inertial sensor readings to assess whether neurological and orthopedic gait could facilitate gait analysis. Our investigation focused on optimizing both performance and generalizability in distinguishing between these gait abnormalities. Variations in sensor configurations, assessment protocols, and enhancements to deep learning model architectures were further suggested. Using a dataset of 29 dogs, our proposed approach achieved 96% accuracy in the multiclass classification task (healthy/orthopedic/neurological) and 82% accuracy in the binary classification task (healthy/non-healthy) when generalizing to unseen dogs. Our results demonstrate the potential of inertial-based deep learning models to serve as a practical and objective diagnostic and clinical aid to differentiate gait assessment in orthopedic and neurological conditio

As the aging population increases and the shortage of healthcare workers increases, the need to examine other means for caring for the aging population increases. One such means is the use of humanoid robots to care for social, emotional, and physical wellbeing of the people above 65. Understanding skilled and long term care nursing home administrators' perspectives on humanoid robots in caregiving is crucial as their insights shape the implementation of robots and their potential impact on resident well-being and quality of life. This authors surveyed two hundred and sixty nine nursing homes executives to understand their perspectives on the use of humanoid robots in their nursing home facilities. The data was coded and results revealed that the executives were keen on exploring other avenues for care such as robotics that would enhance their nursing homes abilities to care for their residents. Qualitative analysis reveals diverse perspectives on integrating humanoid robots in nursing homes. While acknowledging benefits like improved engagement and staff support, concerns persist about costs, impacts on human interaction, and doubts about robot effectiveness. This highlights compl

For patients experiencing cancer, nurse navigation can ease the burden of complex care by enhancing coordination of health services and patient outcomes. However, in under-resourced areas, trained nurse navigators may be limited or non-existent. In the United States, artificial intelligence (AI)-enabled digital health tools are increasingly available and may help address gaps in care coordination; however, most are not designed to specifically support nursing. This perspective piece discusses a human-centered AI framework that integrates empathic and agentic approaches grounded in the American Nurses Association's code of ethics to support nurses in the United States in cancer care navigation. The framework could augment, not replace, human empathy and agency while improving nurse workflow, patient-clinician relationships, and care coordination services in under-resourced areas.

Consistent high-quality nursing care is essential for patient safety, yet current nursing education depends on subjective, time-intensive instructor feedback in training future nurses, which limits scalability and efficiency in their training, and thus hampers nursing competency when they enter the workforce. In this paper, we introduce a video-language model (VLM) based framework to develop the AI capability of automated procedural assessment and feedback for nursing skills training, with the potential of being integrated into existing training programs. Mimicking human skill acquisition, the framework follows a curriculum-inspired progression, advancing from high-level action recognition, fine-grained subaction decomposition, and ultimately to procedural reasoning. This design supports scalable evaluation by reducing instructor workload while preserving assessment quality. The system provides three core capabilities: 1) diagnosing errors by identifying missing or incorrect subactions in nursing skill instruction videos, 2) generating explainable feedback by clarifying why a step is out of order or omitted, and 3) enabling objective, consistent formative evaluation of procedures.

Large Language Models (LLMs) are increasingly proposed for clinical decision support including multilingual diagnosis in low-resource settings. However, their reliability, calibration and safety characteristics remain insufficiently understood for structured, high-risk tasks. We present a system-level analysis of multilingual orthopedic diagnosis from free-text clinical notes in English, Hindi and Punjabi. We evaluate three modeling regimes: (i) task-aligned multilingual transformer encoders, (ii) a task-fine-tuned baseline (DistilBERT), and (iii) a domain-adaptive architecture tailored to orthopedic text (IndicBERT-HPA). These models are compared with zero-shot, instruction-tuned LLMs to assess suitability for structured diagnostic classification. Results indicate that while LLMs exhibit strong linguistic fluency, they show unstable calibration and reduced reliability under structured multilingual conditions, particularly in low-resource languages. These findings are specific to zero-shot evaluation and do not imply limitations of fine-tuned models. Domain-adaptive specialization substantially improves cross-lingual discrimination and confidence behavior. IndicBERT-HPA, with langu

Nursing notes, an important part of Electronic Health Records (EHRs), track a patient's health during a care episode. Summarizing key information in nursing notes can help clinicians quickly understand patients' conditions. However, existing summarization methods in the clinical setting, especially abstractive methods, have overlooked nursing notes and require reference summaries for training. We introduce QGSumm, a novel query-guided self-supervised domain adaptation approach for abstractive nursing note summarization. The method uses patient-related clinical queries for guidance, and hence does not need reference summaries for training. Through automatic experiments and manual evaluation by an expert clinician, we study our approach and other state-of-the-art Large Language Models (LLMs) for nursing note summarization. Our experiments show: 1) GPT-4 is competitive in maintaining information in the original nursing notes, 2) QGSumm can generate high-quality summaries with a good balance between recall of the original content and hallucination rate lower than other top methods. Ultimately, our work offers a new perspective on conditional text summarization, tailored to clinical app

Nursing homes and other long term-care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in U.S. nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, Washington to other skilled nursing facilities. The full extent of staff connections between nursing homes---and the crucial role these connections serve in spreading a highly contagious respiratory infection---is currently unknown given the lack of centralized data on cross-facility nursing home employment. In this paper, we perform the first large-scale analysis of nursing home connections via shared staff using device-level geolocation data from 30 million smartphones, and find that 7 percent of smartphones appearing in a nursing home also appeared in at least one other facility---even after visitor restrictions were imposed. We construct network measures of nursing home connectedness and estimate that nursing homes have, on average, connections

To analyze the effect of comprehensive nursing based on evidence-based nursing during the perioperative period on reducing the incidence of pressure ulcers in patients undergoing posterior orthopedic surgery. Data on 120 patients who underwent orthopedic posterior surgery in our hospital from February 2021 to December 2022 were retrospectively analyzed. The patients were divided into an observation group (n = 60) and a control group (n = 60) based on different nursing methods. Patients in the control group received routine nursing, whereas those in the observation group received comprehensive nursing under the guidance of the concept of evidence-based nursing. The incidence of postoperative pressure ulcer was also recorded. Fasting venous blood (5 mL) was collected from patients before and after surgery and used to measure levels of myeloperoxidase (MPO) and superoxide dismutase (SOD) using enzyme-linked immunosorbent assay. Ulcer tissue samples of patients with pressure ulcers were collected and used to detect the expression of caspase-3 protein, vascular endothelial growth factor (VEGF) mRNA, tumor necrosis factor-α (TNF-α) mRNA, and interleukin-1β (IL-1β) mRNA. The incidence of postoperative pressure ulcers was 8% in the observation group and 23% in the control group (P = .024). The scores of sensory perceptions of the patients in the observation group were significantly lower than those in the control group (P < .001), as were the scores for moisture (P < .001), activity (P = .008), mobility (P < .001), nutrition (P = .003), friction, and shear (P < .001). After surgery, the serum MPO level in the observation group was significantly lower than that in the control group (P < .001), whereas the SOD level in the observation group was significantly higher than that in the control group (P < .001). The expression of TNF-α, IL-1β, VEGF mRNA, and caspase-3 protein in pressure ulcer tissues in the observation group was significantly lower than that in the control group. Comprehensive nursing based on the concept of evidence-based nursing can significantly reduce the incidence of postoperative pressure ulcers following posterior orthopedic surgery.

Nursing documentation in intensive care units (ICUs) provides essential clinical intelligence but often suffers from inconsistent terminology, informal styles, and lack of standardization, challenges that are particularly critical in heart failure care. This study applies Direct Preference Optimization (DPO) to adapt Mistral-7B, a locally deployable language model, using 8,838 heart failure nursing notes from the MIMIC-III database and 21,210 preference pairs derived from expert-verified GPT outputs, model generations, and original notes. Evaluation across BLEU, ROUGE, BERTScore, Perplexity, and expert qualitative assessments demonstrates that DPO markedly enhances documentation quality. Specifically, BLEU increased by 84% (0.173 to 0.318), BERTScore improved by 7.6% (0.828 to 0.891), and expert ratings rose across accuracy (+14.4 points), completeness (+14.5 points), logical consistency (+14.1 points), readability (+11.1 points), and structural clarity (+6.0 points). These results indicate that DPO can align lightweight clinical language models with expert standards, supporting privacy-preserving, AI-assisted documentation within electronic health record systems to reduce administ

PURPOSE: The purpose of this study was to develop and to apply the NANDA, NOC, and NIC (NNN) linkages into a computerized nursing process program using the classification systems of nursing diagnoses, nursing outcomes and nursing interventions. METHOD: The program was developed with planning, analysis, design and performance stages. The program was applied to 117 patients who were admitted to orthopedic surgery nursing units from January to February, 2004. RESULTS: Thirty-five of fifty-three nursing diagnoses were identified. Five nursing diagnoses in order of frequency were: Acute pain (28.4%), Impaired physical mobility (15.6%), Impaired walking (8.7%), Chronic pain (5.5%) and Risk for disuse syndrome (5.0%). The nursing outcomes of the 'Acute pain' nursing diagnosis tended to have higher frequencies (cumulative) in order of Pain management (95.2%), Comfort level (35.5%) and Pain level (17.7%). The nursing interventions of the 'Acute pain' nursing diagnosis tended to have higher frequencies (cumulative) in order of Pain management (71.0%), Splinting (24.2%) and Analgesic administration (17.7%). In comparison of outcome indicator scores between before and after the intervention according to the 61 nursing outcomes, the mean scores of 52 outcome indicators after the intervention were significantly higher than before the intervention. CONCLUSION: It is expected that this program will help nurses perform their nursing processes more efficiently.

Latest advances in the field of natural language processing (NLP) enable new use cases for different domains, including the medical sector. In particular, transcription can be used to support automation in the nursing documentation process and give nurses more time to interact with the patients. However, different challenges including (a) data privacy, (b) local languages and dialects, and (c) domain-specific vocabulary need to be addressed. In this case study, we investigate the case of home care nursing documentation in Switzerland. We assessed different transcription tools and models, and conducted several experiments with OpenAI Whisper, involving different variations of German (i.e., dialects, foreign accent) and manually curated example texts by a domain expert of home care nursing. Our results indicate that even the used out-of-the-box model performs sufficiently well to be a good starting point for future research in the field.

Surgical training integrates several years of didactic learning, simulation, mentorship, and hands-on experience. Challenges include stress, technical demands, and new technologies. Orthopedic education often uses static materials like books, images, and videos, lacking interactivity. This study compares a new interactive photorealistic 3D visualization to 2D videos for learning total hip arthroplasty. In a randomized controlled trial, participants (students and residents) were evaluated on spatial awareness, tool placement, and task times in a simulation. Results show that interactive photorealistic 3D visualization significantly improved scores, with residents and those with prior 3D experience performing better. These results emphasize the potential of the interactive photorealistic 3D visualization to enhance orthopedic training.