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，我们建议开发一种自动多语言紧急分诊助手 (AMLETA)，这是一种基于互联网的软件原型，可帮助分诊护士快速识别到急诊室就诊的患者的潜在症状，当患者不会说英语时，最好优先考虑他们。 AMLETA 将设计供意识清醒的患者使用，通常占到达急诊室的患者的 95% 以上（紧急情况严重程度指数 2-5 级 - 见下文），并且身体能够与计算机系统交互。该系统将快速获取有关患者病情的最关键信息，然后将这些发现实时汇总给分诊护士，后者可以根据他/她的培训和当时科室可用的资源对患者进行优先排序。尽管许多急诊科都有现场口译员来帮助与不会说英语的患者进行沟通，但患者到达急诊科后，这些口译员可能无法立即提供帮助。在使用多种语言的国家/地区，医院可能没有所有所需语言的口译员。能够快速了解​​患者的主诉并优先处理护理是急诊科分诊的目标。拥有一个工具来帮助识别患者以任何语言出现的症状，可以对护理的提供产生重大的有益影响，特别是在为不同人群提供服务的过度拥挤的医院以及在灾难期间。在第一阶段，我们将探索创建决策树以快速识别优先考虑患者所需的关键信息的挑战。由于我们的系统旨在供患者使用外语，因此设计具有图形元素和音频叙述的直观界面以适应潜在的低阅读和健康素养将是一个挑战。如果成功，我们的系统可以极大地改善所有英语能力有限的人群及时获得紧急服务的情况。 公共卫生相关性：急诊科数量的减少、更多地利用急诊科进行非急诊护理以及语言和文化的日益多样化，为我们医疗保健系统中的许多医院造成了关键的突破点。人满为患的急诊室正在努力为患者提供安全、有效的护理，他们发现为那些无法有效地说英语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，帮助分诊护士在患者不会说英语时更好地了解每个患者提出的主诉和症状，从而提供及时的医疗护理。