دانلود و نمایش مقالات مرتبط با Emergency department::صفحه 1
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نتیجه جستجو - Emergency department

تعداد مقالات یافته شده: 17
ردیف عنوان نوع
1 IMPROVING PAIN REASSESSMENT AND DOCUMENTATION RATES: A QUALITY IMPROVEMENT PROJECT IN A TEACHING HOSPITAL’S EMERGENCY DEPARTMENT
بهبود نرخ مستند سازی و ارزیابی مجدد : یک طرح ارتقاء کیفی در بخش آمادگی دانشگاه علوم پزشکی-2020
ED pain score reassessment and documentation rates were drastically low according to sampled data from the St. Margaret Hospital Emergency Department leading to difficult pain management encounters for clinicians. The purpose of this project was to improve pain score reassessment rates in ED patients who were discharged with extremity pain. Methods: This project was an 8-month, prepostinterventional (preintervention: September-November 2018, intervention: December 2018-January 2019, and postintervention: February-April 2019) quality improvement project that took place in a community hospital emergency department. Emergency nurses participated in 6 focus groups, allowing for the creation of focus group-themed interventions at the request of the nursing staff. Daily audits of pain reassessment and documentation rates for individual nurses took place during the month of January 2019. In addition, a weekly newsletter was created and reported the ED pain reassessment and documentation rates. Results: All patient encounters (581) were reviewed over the 8-month period. Baseline pain score reassessment and documentation rates were 36.2% (confidence interval, 30.3%-42.3%) in the emergency department. Pain reassessment and documentation rates increased to 62.3% (confidence interval, 56.8%-67.6%) during the 3-month postintervention period. Discussion: Implementing daily audits and weekly newsletters that created transparency of individual and group performances increased pain score reassessment and documentation rates.
Key words: Pain reassessment | Pain documentation | Practice improvement | Quality improvement | Pain management
مقاله انگلیسی
2 Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review
سیستم های پشتیبانی از تصمیم گیری بالینی برای آزمایش در بخش اورژانس با استفاده از سیستم های هوشمند: یک مرور-2020
Motivation: Emergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage. Objectives: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation. Methods: We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency room” and concepts within the field of intelligent systems. Results: From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patients prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage. Conclusions: In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals’ decision-making thereby leading to better clinical management and patients’ outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.
Keywords: Triage | CDSS | EHR | Machine learning | Critical care
مقاله انگلیسی
3 Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019
Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to establish methodological best practices for the application of machine learning (ML) to Triage in pediatric ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our work is among the first attempts in this direction. Following very recent works in the literature, we use the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain labels provided by experts. The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission, therefore our dataset is highly class imbalanced. Our reported performance comparison results focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use different well known metrics to evaluate performance of ML models in three different experimental settings: (a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high versus low case severity according to its clinical outcome, and (c) comparison of the number of patients assigned to each standard Triage urgency level against the Triage rule based expert system currently in use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the dychotomic classification experiments. On the implementation side, our study shows that ML predictive models trained according to clinical outcomes, provide better Triage performance than the current rule based expert system in operation at the hospital.
Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems
مقاله انگلیسی
4 Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis
پیش بینی بهبود عصبی خوب بعد از ایست قلبی خارج از بیمارستان: تجزیه و تحلیل یادگیری ماشین-2019
Background: This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in OHCA patients. Methods: Adult OHCA patients who had a presumed cardiac etiology and a sustained return of spontaneous circulation between 2013 and 2016 were analyzed; 80% of the individuals were analyzed for training and 20% were analyzed for validation. We developed using six machine learning algorithms: logistic regression (LR), extreme gradient boosting (XGB), support vector machine, random forest, elastic net (EN), and neural network. Variables that could be obtained within 24 hours of the emergency department visit were used. The area under the receiver operation curve (AUROC) was calculated to assess the discrimination. Calibration was assessed by the Hosmer–Lemeshow test. Reclassification was assessed by using the continuous net reclassification index (NRI). Results: A total of 19,860 OHCA patients were included in the analysis. Of the 15,888 patients in the training group, 2228 (14.0%) had a good neurological recovery; of the 3972 patients in the validation group, 577 (14.5%) had a good neurological recovery. The LR, XGB, and EN models showed the highest discrimination powers (AUROC (95% CI)) of 0.949 (0.941–0.957) for all), and all three models were well calibrated (Hosmer–Lemeshow test: p >0.05). The XGB model reclassified patients according to their true risk better than the LR model (NRI: 0.110), but the EN model reclassified patients worse than the LR model (NRI: 1.239). Conclusion: The best performing machine learning algorithm was the XGB and LR algorithm .
Keywords: Out-of-hospital cardiac arrest | Outcome | Machine learning analysis
مقاله انگلیسی
5 Setting up standards: A methodological proposal for pediatric Triage machine learning model construction based on clinical outcomes
تنظیم استانداردها: یک پیشنهاد روش شناختی برای ساخت مدل یادگیری ماشین تراشی کودکان براساس نتایج بالینی-2019
Triage is a critical process in hospital emergency departments (ED). Specifically, we consider how to achieve fast and accurate patient Triage in the ED of a pediatric hospital. The goal of this paper is to establish methodological best practices for the application of machine learning (ML) to Triage in pediatric ED, providing a comprehensive comparison of the performance of ML techniques over a large dataset. Our work is among the first attempts in this direction. Following very recent works in the literature, we use the clinical outcome of a case as its label for supervised ML model training, instead of the more uncertain labels provided by experts. The experimental dataset contains the records along 3 years of operation of the hospital ED. It consists of 189,718 patients visits to the hospital. The clinical outcome of 9271 cases (4.98%) wa hospital admission, therefore our dataset is highly class imbalanced. Our reported performance comparison results focus on four ML models: Deep Learning (DL), Random Forest (RF), Naive Bayes (NB) and Support Vector Machines (SVM). Data preprocessing includes class imbalance correction, and case re-labeling. We use different well known metrics to evaluate performance of ML models in three different experimental settings: (a) classification of each case into the standard five Triage urgency levels, (b) discrimination of high versus low case severity according to its clinical outcome, and (c) comparison of the number of patients assigned to each standard Triage urgency level against the Triage rule based expert system currently in use at the hospital. RF achieved greater AUC, accuracy, PPV and specificity than the other models in the dychotomic classification experiments. On the implementation side, our study shows that ML predictive models trained according to clinical outcomes, provide better Triage performance than the current rule based expert system in operation at the hospital.
Keywords: Machine learning | Emergency department | Triage | Data science | Clinical decision support systems
مقاله انگلیسی
6 PALLIATIVE CARE SYMPTOM MANAGEMENT IN THE EMERGENCY DEPARTMENT: THE ABC’S OF SYMPTOM MANAGEMENT FOR THE EMERGENCY PHYSICIAN
مدیریت علائم مراقبت از بیمار در بخش اورژانس: مدیریت علائم ABC برای بیمارستانی-2018
Background: Palliative care is a rapidly evolving area of emergency medicine. With an estimated 5,000 to 10,000 baby boomers per day reaching retirement age, emergency departments (EDs) are treating more patients with chronic and serious disease. Palliative care offers comprehensive care for patients with advanced medical illness, aims to alleviate suffering and improve quality of life, and plays an important role in caring for these patients in the ED. Objectives: We sought to increase the emergency physician’s knowledge of and comfort with symptom control in palliative and hospice patients. Discussion: Having the skills to deliver efficient and appropriate palliative and hospice care is imperative for emergency physicians. Palliative care should be considered in any patient suffering from symptoms of a life-limiting illness, whereas hospice care should be considered in the patient with likely <6 months left to live. Palliative care is appropriate earlier in the course of disease, and is appropriate when the practitioner would not be surprised if the patient died in the next 2 years (‘‘The Surprise Question’’). This article discusses management in the ED of pain, nausea, dyspnea, agitation, and oral secretions in patients appropriate for hospice and palliative care. Conclusion: The need for palliative and hospice care in the ED is increasing, requiring that emergency physicians be familiar with palliative and hospice care and competent in the delivery of rapid symptom management in patients with severe and life-limiting disease.  2017 Elsevier Inc. All rights reserved.
Keywords: emergency medicine; end of life; palliative care; symptom management
مقاله انگلیسی
7 Crisis in the Emergency Department The Evaluation and Management of Acute Agitation in Children and Adolescents
بحران در بخش اورژانس ارزیابی و مدیریت هیجان حاد در کودکان و نوجوانان-2018

KEYWORDS : Agitation ، Aggression ، Restraint/seclusion ، Emergency department ، Delirium
مقاله انگلیسی
8 Recurrent use of inpatient withdrawal management services: Characteristics, service use, and cost among Medicaid clients
استفاده مجدد از خدمات مدیریت بسته های بستری: مشخصات، استفاده از خدمات و هزینه در میان مشتریان خدمات درمانی-2018
Reducing repeat use of costly inpatient services, such as inpatient withdrawal management, among Medicaid members is a target of healthcare reform. However, characteristics of frequent users of inpatient withdrawal management are understudied. We described the characteristics, service utilization, and costs of New York Medicaid clients who use withdrawal management services by analyzing data from Medicaid records from 2008. We examined follow-up care for individuals with different levels of repeat withdrawal management. We found 32,196 Medicaid withdrawal management patients with a total of 67,073 episodes and we divided patients into low (1 episode, n = 19,602), medium (2–3 episodes, n = 8619) and high (≥4 episodes, n = 3978) use cate gories. High users had almost 8 times the withdrawal management cost of low users. Similarly, they had 5 times more emergency department visits than low users. High users had high levels of homelessness (75%), 20% had HIV/AIDS, and 40% had Hepatitis. High withdrawal management users were less likely than low users to receive any follow-up treatment services. Medicaid clients with high utilization of inpatient withdrawal management are a small but costly population with poor follow-up rates to subsequent treatment services. They are a socially disenfranchised group that may benefit from targeted services to address their complex clinical needs.
Keywords: Withdrawal management ، High utilizers ، Medicaid ، Service utilization ، Cost
مقاله انگلیسی
9 Sickle Cell Disease in the Emergency Department: Complications and Management
بیماری سلولی سقط در بخش اورژانس: عوارض و مدیریت-2018
Sickle cell disease is the most com mon blood disorder in the United States, affecting 100 000 people. A genetic mutation creates hemoglobin S. In the deoxygenated state, hemo globin S polymerizes, creating sickled hemoglobin. Sickled hemoglobin causes a cascade of complex patho physiologic events that lead to hemo lysis, chronic anemia and endothelial damage. This results in clinical com plications, end organ dysfunction and a shortened life expectancy. The acute nature of many sickle cell complica tions makes the emergency depart ment a common setting where sickle cell patients present. Common com plications (vaso-occlusive episode, fe ver, acute chest syndrome, stroke) and less common complications (splenic sequestration, priapism, aplastic cri sis, ocular emergencies) will be dis cussed. Public health implications will be discussed briefly.
Keywords: sickle cell disease; anemia; compli cations; vaso occlusive crisis; vaso occlusive episode; acute chest syn drome; stroke; splenic sequestra tion; priapism; aplastic crisis
مقاله انگلیسی
10 Preventing Emergency Department Violence through Design
جلوگیری از خشونت گروهی اورژانس از طریق طراحی-2017
T rends and news reports highlight a growing concern about random violence in public venues. Health care settings traditionally have been considered sacred ground for vulnerable ill or injured patients, as well as care providers, who are considered part of the public safety net. However, not all hospital or health-system leaders fully appreciate the dynamic situations that arise when fear, pain, drug use, or mental-health behaviors put patients, staff, and visitors in harm’s way. It is crucial that staff partner with administrators, facility leaders, and safety officers to design emergency departments with evidence based concepts to minimize or eliminate risks to safety and security. This article provides a comprehensive review of best design practices to help guide clinical user groups in meetings with hospital leaders, architects, and engineers.
مقاله انگلیسی
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