School of Informatics, University of Edinburgh, Edinburgh, UK.
J Biomed Inform. 2010 Apr;43(2):287-99. doi: 10.1016/j.jbi.2009.11.006. Epub 2009 Nov 26.
Computer-interpretable guidelines (CIGs) aim to improve patient care and reduce medical errors. Although CIGs implement evidence-based recommendations they cannot prevent exceptional behavior from happening. To address this problem we developed a framework that can monitor, detect, and handle exceptions that occur during normal CIG execution and can potentially prevent them from developing into medical errors. Our framework enables specifying the goals of a guideline and linking them with recommended tasks that could satisfy the goals. Exceptions are linked with goals that manage them, which can be realized by tasks or plans. To achieve a link between the tasks, plans, goals, monitored effects, and exceptions, our definition of goals and exceptions is state-based. We demonstrate our approach using a generic plan for management of a chronic disease and a particular instantiation for hypertension management.
计算机可解释指南（CIG）旨在改善患者护理并减少医疗错误。虽然 CIG 实施了基于证据的建议，但它们无法防止异常行为的发生。为了解决这个问题，我们开发了一个框架，可以监控、检测和处理正常 CIG 执行过程中发生的异常情况，并有可能防止它们发展成医疗错误。我们的框架允许指定指南的目标，并将其与可以满足这些目标的推荐任务相关联。异常情况与管理它们的目标相关联，这些目标可以通过任务或计划来实现。为了在任务、计划、目标、监测效果和异常情况之间建立联系，我们对目标和异常情况的定义是基于状态的。我们使用管理慢性病的通用计划和高血压管理的特定实例来演示我们的方法。
