Cognitive Engineering for Complex Decision Making & Problem Solving in Acute Care

复杂决策的认知工程

• 批准号: 9560708
• 负责人: Aaron Zachary Hettinger
• 金额: $ 49.21万
• 依托单位: MEDSTAR HEALTH RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-30 至 2020-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9560708
• 关键词: Cognitive; Engineering; Complex; Decision; Making

DESCRIPTION (provided by applicant): The proposed research applies cognitive engineering methods to understand and support complex, cognition and work activities within the acute care environment, using emergency medicine as the research setting. The research will provide a fundamental and comprehensive picture of work domain complexities and challenging sensemaking, decision-making, and planning/replanning tasks; along with the individual and team expertise required to meet those challenges. The research uses emergency medicine as a "field laboratory" because it both explicates some of the most challenging conditions for cognitive work - high risk, time-pressure, and uncertainty - and therefore provides a strong opportunity to generalize findings to other complex health care environments; while at the same time providing an environment which can clearly benefit from the decision aids, visualizations, and other supportive technologies that can result from cognitive engineering analyses. This project utilizes the extensive prior experience of research team in human factors and cognitive engineering analyses of health care work (including emergency medicine) and other high risk work environments; and evaluating the usability and effectiveness of information displays and decision support technologies designed to support complex, cognitive work. The application addresses the fundamental, research nature of the call for applications by including a comprehensive cognitive engineering analysis of emergency medicine along with targeted development and evaluation of visualizations, decision- support, and other aiding solutions. Contributions from this research will include a fundamental understanding of the nature of cognition and clinical work in a high intensity medical environment - including both work related complexities, and practitioner expertise, knowledge, and skills - which can provided a basis for the design of health IT solutions which support clinical work and improve the effectiveness of medical care. Additionally, the research will develop and evaluate exemplar solutions for a targeted set of needs identified through the cognitive engineering analysis, thus providing a methodological example and "proof-of-concept" for translating cognitive engineering analyses into designs.

描述(申请人提供)：建议的研究应用认知工程学方法，以急诊医学为研究背景，理解和支持急性护理环境中的复杂、认知和工作活动。这项研究将提供工作领域的复杂性和具有挑战性的感觉、决策和计划/重新计划任务的基本和全面的图景，以及应对这些挑战所需的个人和团队专业知识。这项研究将急诊医学用作“现场实验室”，因为它既阐明了认知工作的一些最具挑战性的条件--高风险、时间压力和不确定性--因此提供了一个将发现推广到其他复杂医疗环境的强大机会；同时提供了一个环境，可以明显受益于认知工程分析所产生的决策辅助、可视化和其他支持性技术。该项目利用研究团队在人的因素和认知工程方面的丰富经验，对医疗保健工作(包括急救医学)和其他高风险工作环境进行分析，并评估旨在支持复杂认知工作的信息显示和决策支持技术的可用性和有效性。该应用程序通过包括对紧急医学的全面认知工程分析以及可视化、决策支持和其他辅助解决方案的有针对性的开发和评估，解决了应用程序呼吁的基本和研究性质。这项研究的贡献将包括对高强度医疗环境中认知和临床工作的性质-包括与工作相关的复杂性以及从业人员的专业知识和技能-的基本理解，这可以为设计支持临床工作和提高医疗保健效率的医疗IT解决方案提供基础。此外，研究将为通过认知工程分析确定的一组目标需求开发和评估范例解决方案，从而为将认知工程分析转化为设计提供方法论范例和“概念证明”。

项目成果

Modeling patient-related workload in the emergency department using electronic health record data.

使用电子健康记录数据对急诊科患者相关的工作量进行建模。

• DOI: 10.1016/j.ijmedinf.2021.104451
• 发表时间: 2021
• 期刊: International journal of medical informatics
• 影响因子: 4.9
• 作者: Wang,Xiaomei;Blumenthal,HJoseph;Hoffman,Daniel;Benda,Natalie;Kim,Tracy;Perry,Shawna;Franklin,EllaS;Roth,EmilieM;Hettinger,AZachary;Bisantz,AnnM
• 通讯作者: Bisantz,AnnM