سیستم های تصمیم گیری بالینی برای تریاژ در بخش اورژانس
ترجمه نشده

سیستم های تصمیم گیری بالینی برای تریاژ در بخش اورژانس

عنوان فارسی مقاله: سیستم های تصمیم گیری بالینی برای تریاژ در بخش اورژانس با استفاده از سیستم های هوشمند: مرور
عنوان انگلیسی مقاله: Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review
مجله/کنفرانس: هوش مصنوعی در پزشکی – Artificial Intelligence In Medicine
رشته های تحصیلی مرتبط: پزشکی، مهندسی کامپیوتر، مهندسی پزشکی
گرایش های تحصیلی مرتبط: طب اورژانس، هوش مصنوعی، فوریت های پزشکی
کلمات کلیدی فارسی: تریاژ، CDSS، EHR، یادگیری ماشین، مراقبت های ویژه
کلمات کلیدی انگلیسی: Triage, CDSS, EHR, Machine learning, Critical care
نوع نگارش مقاله: مقاله مروری (Review Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.artmed.2019.101762
دانشگاه: Massachusetts Institute of Technology, Massachusetts
صفحات مقاله انگلیسی: 22
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 4.472 در سال 2019
شاخص H_index: 74 در سال 2020
شاخص SJR: 1.025 در سال 2019
شناسه ISSN: 0933-3657
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E14568
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Methods

3- Results

4- Discussion

5- Conclusions

Acknowledgements

Annexes

References

بخشی از مقاله (انگلیسی)

Abstract

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 patient’s 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.

Introduction

The growing demand for emergency services, combined with the priority sorting due to patient’s acuity, results in long waiting times for patients. Waiting times have a significant impact on patient mortality, morbidity with readmission in less than 30 days, number of preIntensive Care Units (ICU) resuscitation, length of stay (LOS), patient satisfaction and costs [1–۷]. The outcome of patients’ medical treatment is time-sensitive, therefore the sooner the treatment is rendered, the better the outcome [3–۷].