Hasso Plattner Institute, Rudolf-Breitscheid-Straße 187, Potsdam, 14482, Brandenburg, Germany.
Institute of Medical Informatics at Charité - Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Berlin, Germany.
Int J Med Inform. 2024 Apr;184:105349. doi: 10.1016/j.ijmedinf.2024.105349. Epub 2024 Jan 29.
Alarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many - mostly unnecessary - alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety.
We present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems.
Policy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models - for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads.
ARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric.
ARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike - with organisational, economic, and clinical benefits for the implementing hospital.
报警疲劳是重症监护病房患者和医护人员面临的一个主要由技术引发的风险。太多（大多数是不必要的）警报会导致医护人员的脱敏和反应不足。不合适的报警策略是导致报警疲劳的一个原因。但是，改变报警策略是一个棘手的问题，因为这涉及到患者的安全。
我们提出了 ARTEMIS，这是一种针对政策制定者的新型计算机辅助临床决策支持系统，可以使用医院信息系统中的数据帮助大大改进报警策略。
政策制定者可以使用 ARTEMIS 内部库中的不同策略组件，为他们的重症监护病房组装定制的报警策略。或者，政策制定者可以使用来自医院信息系统的数据提供更具定制化的策略组件，例如作为 Python 函数。这甚至可以包括机器学习模型 - 例如用于设置报警阈值。最后，政策制定者可以评估他们的政策系统并比较产生的报警负载。
ARTEMIS 报告并比较了不同报警策略对易于适应的目标人群产生的报警数量。ARTEMIS 可以并排比较策略，并提供网格比较和热点图进行参数优化。例如，我们发现报警延迟的效用因目标人群而异。此外，政策制定者可以通过提供计算公式来引入不在原始数据中的虚拟参数。虚拟参数有助于根据正确的指标进行测量和报警，即使患者监护仪没有直接测量该指标。
ARTEMIS 并没有免除政策制定者从医学角度评估政策。但是，作为知识发现和临床决策支持系统，它为医疗决策提供了强有力的定量基础。ARTEMIS 的实施成本相对较低，可以对患者和医护人员都产生重大影响 - 为实施医院带来组织、经济和临床效益。
