From the Department of Anesthesiology and Critical Care, Division of General Anesthesia, Perelman School of Medicine at the University of Pennsylvania and The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Department of Anesthesiology, Johns Hopkins All Children's Hospital and Johns Hopkins University School of Medicine, Baltimore, Maryland.
Anesth Analg. 2018 Jul;127(1):90-94. doi: 10.1213/ANE.0000000000002545.
Anesthesia information management systems (AIMS) have evolved from simple, automated intraoperative record keepers in a select few institutions to widely adopted, sophisticated hardware and software solutions that are integrated into a hospital's electronic health record system and used to manage and document a patient's entire perioperative experience. AIMS implementations have resulted in numerous billing, research, and clinical benefits, yet there remain challenges and areas of potential improvement to AIMS utilization. This article provides an overview of the history of AIMS, the components and features of AIMS, and the benefits and challenges associated with implementing and using AIMS. As AIMS continue to proliferate and data are increasingly shared across multi-institutional collaborations, visual analytics and advanced analytics techniques such as machine learning may be applied to AIMS data to reap even more benefits.
麻醉信息管理系统（AIMS）已经从少数几家机构中简单的自动化术中记录器发展成为广泛采用的复杂硬件和软件解决方案，这些解决方案已经集成到医院的电子健康记录系统中，用于管理和记录患者的整个围手术期体验。AIMS 的实施带来了许多计费、研究和临床方面的好处，但在 AIMS 的使用方面仍然存在挑战和潜在的改进领域。本文概述了 AIMS 的历史、AIMS 的组成部分和特点，以及实施和使用 AIMS 相关的好处和挑战。随着 AIMS 的不断普及以及数据在多机构合作中越来越多地共享，可视化分析和机器学习等高级分析技术可能会应用于 AIMS 数据，以带来更多的好处。
