Emergency physician-based intensive care unit for critically ill patients visiting emergency department

Jeong, Hwain; Jung, Young-Seok; Suh, Gil Joon; Kwon, Woon Yong; Kim, Kyung Soo; Kim, Taegyun; Shin, So Mi; Kang, Min Woo; Lee, Min Sung

doi:10.1016/j.ajem.2019.09.021

Cited by 6 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scores were selected that are validated, easy-to-apply in a prehospital setting and that use single parameter or multiple parameter systems, or aggregated weighting systems based on determinations of standard vital signs and/or simple clinical observations. Analysis was not performed on scores using complex parameters (e.g., analytical determinations, diuresis, anatomical injuries) such as the Sequential Organ Failure Assessment score [21] or Acute Physiology and Chronic Health Evaluation [22]. Four EWS were selected (see Appendix A Table A1) with the following validation values: [26], alarm triggers 14 points.…”

Section: Selection Of Early Warning Scoresmentioning

confidence: 99%

Identification of Serious Adverse Events in Patients with Traumatic Brain Injuries, from Prehospital Care to Intensive-Care Unit, Using Early Warning Scores

Martin-Rodríguez

López-Izquierdo

Mohedano-Moriano

et al. 2020

IJERPH

View full text Add to dashboard Cite

Traumatic brain injuries are complex situations in which the emergency medical services must quickly determine the risk of deterioration using minimal diagnostic methods. The aim of this study is to analyze whether the use of early warning scores can help with decision-making in these dynamic situations by determining the patients who need the intensive care unit. A prospective, multicentric cohort study without intervention was carried out on traumatic brain injury patients aged over 18 given advanced life support and taken to the hospital. Our study included a total of 209 cases. The total number of intensive-care unit admissions was 50 cases (23.9%). Of the scores analyzed, the National Early Warning Score2 was the best result presented with an area under the curve of 0.888 (0.81–0.94; p < 0.001) and an odds ratio of 25.4 (95% confidence interval (CI):11.2–57.5). The use of early warning scores (and specifically National Early Warning Score2) can help the emergency medical services to differentiate traumatic brain injury patients with a high risk of deterioration. The emergency medical services should use the early warning scores routinely in all cases for the early detection of high-risk situations.

show abstract

Section: Selection Of Early Warning Scoresmentioning

confidence: 99%

Identification of Serious Adverse Events in Patients with Traumatic Brain Injuries, from Prehospital Care to Intensive-Care Unit, Using Early Warning Scores

Martin-Rodríguez

López-Izquierdo

Mohedano-Moriano

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…The South Korean government launched a similar program in 2004, designating 12 regional emergency medical centers across the country that featured 12 ED-ICU beds and was officially referred to as Emergency Intensive Care Units (EICU); these beds were dedicated solely to patients admitted from Eds [15]. In July 2023, 44 regional emergency medical centers with 20 ED-ICU beds were established throughout the country [16].…”

Section: Introductionmentioning

confidence: 99%

The development and validation of a novel deep-learning algorithm to predict in-hospital cardiac arrest in ED-ICU (emergency department-based intensive care units): a single center retrospective cohort study

2024

View full text Add to dashboard Cite

Over recent years, the escalation of patient volumes in emergency departments (ED) worldwide has posed to the delivery of timely critical care. Intensive Care Unit (ICU) services became essential due to increasing acuity in EDs, and previous studies revealed a strong association between prolonged boarding times and unfavorable outcomes. Innovative strategies such as Emergency Department-based Intensive Care Units (ED-ICUs) have been introduced to optimize critical care delivery. Given the higher acuity and mortality rates in ED-ICU patients, the prediction of certain events, such as In-Hospital Cardiac Arrest (IHCA), has become abstruse. Conventional Early Warning Scores (EWSs) were developed to stratify the risk of conventional ICUs, but have never been validated in ED-ICU patients with higher acuity. Moreover, EWSs are predominantly focused on forecasting mortality and lack capability for real-time prediction. Our study aimed to develop and validate a deep-learning-based model to predict IHCA within 24 h in ED-ICU. We included 1975 patients admitted to ED-ICU. The study period was from 01 January 2019 to 31 December 2020. Our model, the Deep-ICU CMS (Central Monitoring System), uses four classic vital signs (blood pressure, heart rate, respiratory rate, and body temperature) as input. The model outperformed conventional EWSs in predicting IHCA and maintained performance even with extended prediction windows; it provided robust prediction within a 24-h window, setting it apart from models with restricted prediction horizons. It achieved notably high sensitivity and specificity, overcoming the alarm fatigue issue that is common in EWSs. This study pioneered IHCA risk stratification in ED-ICU and showcases Deep-ICU CMS as a robust prediction tool that overcomes the limitations of conventional EWSs. Prospective and external validation are now warranted to confirm the impact of Deep-ICU CMS in real-world practice. Given the scarcity of research in ED-ICU, our findings contribute valuable insights to optimizing critical care delivery.

show abstract

“…Besides, the installation conducts an emergency care service, which is the first service for patients to come to the hospital, especially in case of emergency medical. Therefore, in improving the excellent healthcare services for users of healthcare in hospitals, the emergency unit is undoubtedly indispensable to address health problems [21], [22]. The expected goal is to save the lives of people who are experiencing serious problems with health…”

Section: Introductionmentioning

confidence: 99%

The Impact of Nurses Workload, Humanities and Health Policy: Exploratory Analysis of the Patient Experience Satisfaction

Nasirin¹

2021

Proceedings of the 2nd Annual Conference on Education and Social Science (ACCESS 2020)

View full text Add to dashboard Cite

The ability of nurse professionals to provide quality healthcare services is necessary for consumers when conducting health care in hospitals. The optimal health services that are expected by the patients using health care services, and it's certainly an obstacle for the hospital in providing quality services for the whole community. The purpose of this study is to identify workloads for healthcare professionals and the desire of consumers to improve health quality during the period of medical care and treatment. The methods in this study used an exploratory analysis. And the sampling technique is performed using non-probability using questionnaires and observations to get the study results significantly. The result showed that there was an impact on the workload carried out by healthcare professionals with the satisfaction of patients during hospital treatment. Thus, the quality of health services will also impact the workload for healthcare professionals. Health institutions such as general hospitals certainly need the planning of professional health personnel sufficient to improve the quality of service. Therefore, the number of consumers who do health care should undoubtedly comply with the ratio of health personnel available.

show abstract

Emergency physician-based intensive care unit for critically ill patients visiting emergency department

Cited by 6 publications

References 22 publications

Identification of Serious Adverse Events in Patients with Traumatic Brain Injuries, from Prehospital Care to Intensive-Care Unit, Using Early Warning Scores

Identification of Serious Adverse Events in Patients with Traumatic Brain Injuries, from Prehospital Care to Intensive-Care Unit, Using Early Warning Scores

The development and validation of a novel deep-learning algorithm to predict in-hospital cardiac arrest in ED-ICU (emergency department-based intensive care units): a single center retrospective cohort study

The Impact of Nurses Workload, Humanities and Health Policy: Exploratory Analysis of the Patient Experience Satisfaction

Contact Info

Product

Resources

About