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1 |
Predictors of Traumatic Suicide Attempts in Youth Presenting to Hospitals with Level I Trauma Centers
پیش بینی اقدام به خودکشی آسیب زا در جوانان مراجعه کننده به بیمارستان های مراکز ترومای سطح یک-2020 Background: Limited research exists examining
the predictors of suicide attempts by mechanism.
Objective: The purpose of this study was to examine predictors
of traumatic suicide attempts in youth. Methods: Data
came from patients 5–18 years of age presenting because
of a suicide attempt at 2 hospitals in Central Texas with level
I trauma centers. Univariate logistic regression examined
the association between traumatic suicide attempts and variables
describing the patient’s demographic, mental health,
and social information. We used the Mann–Whitney U test
to examine the association between traumatic suicide attempts
and the continuous variable of age. Results: Of 231
patients included in this study, most were female (75.8%),
non-Hispanic white (48.1%), and had a median age of
15.0 years (interquartile range 14–16). Compared with patients
presenting because of an intentional overdose, patients
presenting because of traumatic suicide attempts were associated
with a reported criminal history (odds ratio [OR]
14.50 [95% confidence interval {CI} 3.84–54.82]), reported
Child Protective Services history (OR 3.26 [95% CI 0.99–
10.77]), being publicly insured or uninsured (OR 1.80
[95% CI 1.02–3.19]), male (OR 2.37 [95% CI 1.28–4.38]),
and identifying as Hispanic (OR 2.01 [95% CI 1.10–3.68).
Conclusions: Our findings inform targeted preventative resources
and education efforts to populations of greatest
need. Keywords: emergency department | suicide attempt | trauma center | traumatic suicide attempt | youth |
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 |
Cross-sector collaboration for vulnerable populations reduces utilization and strengthens community partnerships
همکاری بین بخشی برای جمعیت های آسیب پذیر باعث کاهش استفاده و تقویت مشارکت های جامعه می شود-2020 Adventist Health Clear Lake is located in one of the poorest counties in California, with health rankings in the
lowest decile of the state. Fire devastation, lack of affordable housing, modest employment opportunities, and
widespread addiction have stretched the limited resources of the response system in this rural community. An
innovative, cross-sector, interprofessional collaborative was formed to address the needs of community members
who were high utilizers of the police, emergency, criminal justice, and healthcare systems.
The collaborative approach was associated with a 44% reduction in hospital utilization, an 83% reduction in
community response system usage, and a 71% reduction in costs for the population. Cross-sector, interprofessional
collaboration between community agencies that share a select group of community members is an effective
way to stabilize care, decrease healthcare and community system overutilization, improve care delivery,
and reduce the costs of associated care. Keywords: Cross-sector | Cross-continuum care collaboration | Cross-sector collaboration | Competing health systems | Integrated care | High need patient | igh frequency patient | Complex patient | Chronic patient | Emergency department | Individualized care | Decrease readmissions | Preventable hospitalization | Cost reduction | Root cause | Interprofessional team | Interorganizational team | Integrated behavioral health | Overuse | Overutilization | Super utilizers |
مقاله انگلیسی |
4 |
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 |
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 |
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 |
9 |
Presentations for hypoglycemia associated with diabetes mellitus to emergency departments in a Canadian province: A database and epidemiological analysis
ارائه برای هیپوگلیسمی مرتبط با دیابت به بخش اورژانس در استان کانادا: پایگاه داده و تجزیه و تحلیل اپیدمیولوژیک-2017 Aims: The prevalence of diabetes mellitus was reportedly 9% in 2014, making it one of the
most common global chronic conditions. Hypoglycemia is an important complication of
diabetes treatment. The objective of this study was to quantify and characterize hypo
glycemia presentations associated with type 1 or 2 diabetes made to emergency depart
ments (EDs) by adults in a Canadian province.
Methods: A retrospective cohort study was conducted using reliable administrative data
from Alberta for a five-year period (2010/11–2014/15). Records of interest were those with
an ICD-10-CA diagnosis of diabetes-associated hypoglycemia (e.g., E10.63). A descriptive
analysis was conducted.
Results: Data extraction yielded 7835 presentations by 5884 patients. The majority (56.2%)
of presentations were made by males, median patient age was 62, and 60.5% had type 2 dia
betes. These episodes constituted 0.08% of presentations to Alberta EDs. The annual rate of
presentations decreased by 11.8% during the five-year period. Most presentations (63.4%)
involved transportation to ED via ambulance. Median length-of-stay was four hours. For
27.5% of presentations, an X-ray was obtained. Most hypoglycemic episodes (65.2%) were
considered to be moderate, while 34.3% were considered to be severe.
Conclusions: Diabetes-associated hypoglycemia presentations to Alberta EDs are more com
monly made by patients with type 2 diabetes, who are more likely to be transported via
ambulance and also admitted. Each year, approximately one percent of Albertans with
diabetes presented with a hypoglycemia episode; however, knowledge of the variation
across regions can guide a strategy for improved care.
Keywords: Diabetes mellitus | Hypoglycemia | Emergency department | Epidemiology | Administrative database |
مقاله انگلیسی |
10 |
Effect of Certificate of Need Law on the intensity of competition: The market for emergency care
تأثیر قانون صدور گواهینامه بر کثرت رقابت: بازار خدمات اورژانسی-2017 Purpose: This article aims to contribute to the academic literature in better understanding the impact of
Certificate of Need (CON) Law on Emergency Department (ED) care. Impact of CON Law on ED compe
tition remains an unanswered empirical question.
Methods: We examine the impact of CON Law and its stringency on the intensity of competition (rivalry
among competitors) between EDs measured by the Herfindahl-Hirschman Index (HHI). We then esti
mate the effects of CON Law on HHI by treating CON as an exogenous (endogenous) variable.
Findings: On average the CON legislation enhances ED competition. A possible reason is that the law
hinders predatory behavior, and therefore acts as an effective anti-trust tool. Other findings indicate that
competition is found to be positively related to a states population size and median income and
negatively related with the prevalence of employer provided insurance and magnitude of illegal
immigration in a state.
Practical implications: This article sheds some light on how political regulations could affect healthcare
market and hence may provide public policy makers some insights on reducing healthcare cost.
Originality: Our analysis of the impact of CON regulation on ED competition significantly contributes to
the healthcare and strategy literature. The law potentially serves as an anti-trust tool in the hands of the
government. We extend the empirical literature by treating CON Law and its stringency as exogenous
(endogenous). Our comprehensive analysis considers a host of control variables such as population
demographics, their health status and access to health care, healthcare facilities, political environment, in
addition to the CON features.
Keywords: CON Law | Health policy | Competition | Herfindahl-Hirschman Index | Emergency department |
مقاله انگلیسی |