AMLETA: Automated Multi-Language Emergency Triage Assister

AMLETA：自动多语言紧急分类助手

• 批准号: 7800527
• 负责人: Chung-Suk Charles Lee
• 金额: $ 5.14万
• 依托单位: POLYGLOT SYSTEMS, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2010-11-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7800527
• 关键词: Accident and Emergency department; Architecture; Area; Caring; Computer Systems; Computer software; Conscious; Country; Cultural Backgrounds; Decision Trees; Development; Disasters; Elements; Emergency Situation; Emergency medical service; Evaluation; Face; Goals; Health Professional; Healthcare Systems; Hospitals; Human Resources; Institute of Medicine (U.S.); Internet; Language; Life; Medical; Narration; Nurses; Patient Care; Patients; Perception; Phase; Play; Population; Population Heterogeneity; Reading; Resources; Role; Services; Severities; Side; Simulate; Small Business Innovation Research Grant; Structure; Symptoms; System; Terminology; Text; Time; Training; Translations; Triage; Variant; base; care delivery; design; flexibility; foreign language; health disparity; health literacy; improved; indexing; prototype; public health relevance; response; software development; tool

DESCRIPTION (provided by applicant): During Phase I, we propose to develop an Automated Multi-Language Emergency Triage Assister (AMLETA), an internet-based, software prototype to assist triage nurses rapidly identify underlying symptoms of patients presenting to an emergency department to better prioritize those patients when they are unable to speak English. AMLETA will be designed for use by patients who are conscious, which typically represents over 95% of patients arriving to the emergency department (Levels 2-5 of the Emergency Severity Index - see below), and are physically able to interact with a computer system. The system will quickly elicit the most critical information about a patient's condition and then summarize those findings, in real time, to the triage nurse who can then prioritize patients based on his/her training and the resources available within the department at the time. Although many emergency departments have live interpreters to help communicate with patients who cannot speak English, those interpreters may not be available immediately to assist as soon as a patient arrives in the emergency department. In areas of the country where multiple languages are spoken, the hospital may not have interpreter in all needed languages. The ability to quickly understand the patient's chief complaint and prioritize care is the objective of triaging in the emergency department. Having a tool to assist in identifying the patient's presenting symptoms in any language can have a major beneficial impact on the delivery of care, particularly in overcrowded hospitals serving diverse populations, and during times of disasters. During Phase I, we will explore the challenges of creating a decision tree to quickly identify critical information necessary for prioritizing patients. Because our system is intended to be used by patients in a foreign language, designing an intuitive interface with graphical elements and audio narratives to accommodate potential low reading and health literacy will be a challenge. If successful, our system could dramatically improve timely access to emergency service for all our limited English proficient population. PUBLIC HEALTH RELEVANCE: Diminishing number of emergency departments, greater utilization of emergency departments for non-emergent care, and growing diversity of language and culture have created a critical breaking point for many hospitals in our health care system. Overcrowded emergency department are struggling to provide safe, effective care for patients and they are finding it even harder to provide service to those who are unable to speak effectively in English. To reach toward the Institute of Medicine's goal to eliminate health disparities, we propose to develop a software based tool to help triage nurses provide timely medical care by better understanding each patient's presenting complaints and symptoms when those patients are unable to speak English.

描述（由申请人提供）：在第一阶段，我们建议开发自动化的多语言紧急分类助理（AMLETA），这是一个基于Internet的软件原型，以帮助分类护士迅速识别出急诊室的患者的潜在症状，以在无法说英语时更好地优先考虑这些患者的优先级。 AMLETA将设计供有意识的患者使用，该患者通常代表到达急诊科的患者的95％以上（紧急严重性指数的2-5级 - 见下文），并且可以与计算机系统进行物理上的互动。该系统将迅速获取有关患者病情的最关键信息，然后将这些发现实时总结给分类护士，然后他们可以根据患者的培训和当时的部门可用的资源来优先考虑患者。尽管许多急诊部门都有现场口译员来帮助与不会说英语的患者进行沟通，但这些口译员可能不会立即提供帮助，一旦患者到达急诊科。在说多种语言的国家，医院可能没有所有必要语言的口译员。快速理解患者的主要投诉和确定护理优先级的能力是在急诊科进行分类的目标。拥有一种工具来协助确定患者在任何语言中表现出症状，可能会对服务的提供产生重大的有益影响，尤其是在为多样化人群的人满为患的医院和灾难时期。在第一阶段，我们将探讨创建决策树的挑战，以快速确定优先级排序的关键信息。因为我们的系统旨在用外语使用患者使用，因此设计具有图形元素和音频叙事的直观界面以适应潜在的低阅读和健康素养，这将是一个挑战。如果成功的话，我们的系统可能会大大改善我们所有有限的英语熟练人口的及时访问紧急服务。 公共卫生相关性：急诊部门的数量减少，急诊部门用于非热心护理的更多利用以及语言和文化的越来越多的多样性为我们的医疗保健系统中的许多医院带来了关键的破坏点。拥挤的急诊室正在努力为患者提供安全，有效的护理，他们发现为无法在英语中有效说话的人提供服务更加困难。为了实现医学研究所消除健康差异的目标，我们建议开发一种基于软件的工具，以帮助分类护士及时提供医疗服务，以更好地理解每个患者无法说英语时表现出投诉和症状。