Rappelsberger Andrea, Adlassnig Klaus-Peter, de Bruin Jeroen S, Plössnig Manuela, Schuler Jochen, Hofer-Dückelmann Christina
Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.
Salzburg Research Forschungsgesellschaft mbH, Jakob Haringer Strasse 5/3, A-5020 Salzburg, Austria.
Stud Health Technol Inform. 2017;245:1123-1127.
In times of steadily increasing numbers of administered drugs, the detection of adverse drug events (ADEs) is an important aspect of improving patient safety. At present only about 1-13% of detected ADEs are reported. Raising the number of reported ADEs will result in greater and more efficient support of pharmacovigilance. Potential ADE's must be identified early. In the iMedication system, which is a rule-based application, triggers are used for computerized detection of possible ADEs. Creating a pilot system, we defined the relevant use cases hyperkalemia, hyponatremia, renal failure, and over-anticoagulation; knowledge bases were implemented in Arden Syntax for each use case. The objective of these knowledge bases is to interpret patient-specific clinical data and generate notifications based on a calculated ADE risk score, which may indicate possible ADEs. This will permit appropriate monitoring of potential ADE situations over time in the interest of patient care, quality assurance, and pharmacovigilance.
在用药数量持续增加的时代,药物不良事件(ADEs)的检测是提高患者安全的一个重要方面。目前,检测到的ADEs中只有约1% - 13%被报告。增加报告的ADEs数量将为药物警戒提供更有力、更有效的支持。必须尽早识别潜在的ADEs。在基于规则的iMedication系统中,触发器用于计算机化检测可能的ADEs。通过创建一个试点系统,我们定义了相关的用例,即高钾血症、低钠血症、肾衰竭和抗凝过度;针对每个用例在Arden语法中实现了知识库。这些知识库的目的是解释患者特定的临床数据,并根据计算出的ADE风险评分生成通知,该评分可能表明存在可能的ADEs。这将有助于随着时间的推移对潜在的ADE情况进行适当监测,以利于患者护理、质量保证和药物警戒。