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心血管手术高风险护理阶段的认知支持

Cognitive Support During High-Consequence Episodes of Care in Cardiovascular Surgery.

作者信息

Conboy Heather M, Avrunin George S, Clarke Lori A, Osterweil Leon J, Christov Stefan C, Goldman Julian M, Yule Steven J, Zenati Marco A

机构信息

College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA.

Department of Engineering, Quinnipiac University, Hamden, CT, USA.

出版信息

IEEE Int Interdiscip Conf Cogn Methods Situat Aware Decis Support. 2017 Mar;2017. doi: 10.1109/COGSIMA.2017.7929610. Epub 2017 May 18.

Abstract

Despite significant efforts to reduce preventable adverse events in medical processes, such events continue to occur at unacceptable rates. This paper describes a computer science approach that uses formal process modeling to provide situationally aware monitoring and management support to medical professionals performing complex processes. These process models represent both normative and non-normative situations, and are validated by rigorous automated techniques such as model checking and fault tree analysis, in addition to careful review by experts. Context-aware Smart Checklists are then generated from the models, providing cognitive support during high-consequence surgical episodes. The approach is illustrated with a case study in cardiovascular surgery.

摘要

尽管人们为减少医疗过程中可预防的不良事件付出了巨大努力,但此类事件仍以不可接受的比率持续发生。本文描述了一种计算机科学方法,该方法使用形式化流程建模为执行复杂流程的医疗专业人员提供情境感知监测和管理支持。这些流程模型既代表规范情况,也代表非规范情况,并且除了经过专家仔细审查外,还通过模型检查和故障树分析等严格的自动化技术进行验证。然后从这些模型中生成情境感知智能检查表,在高风险手术过程中提供认知支持。本文通过心血管手术的案例研究对该方法进行了说明。

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