背景:临床实践指南是旨在优化患者护理的系统开发声明。然而,指南建议的无间隙实施要求卫生保健人员不仅要了解建议并支持其内容,还要认识到适用的每种情况。不要错过应该应用建议的情况,计算机化的临床决策支持可以通过一个系统来提供,该系统允许自动监测个体患者对临床指南建议的遵守情况.
目的:本研究旨在收集和分析一个系统的要求,该系统允许监测个体患者对循证临床指南建议的遵守情况,基于这些要求,设计和实施一个软件原型,该原型将指南建议与单个患者数据集成在一起,并演示原型在治疗建议中的实用性。
方法:我们与有经验的重症监护临床医生进行了工作过程分析,以开发一个概念模型,说明如何在临床常规中支持指南依从性监测,并确定模型中的哪些步骤可以通过电子方式支持。然后,我们确定了软件系统的核心需求,以支持关键利益相关者(临床医生,指南开发人员,健康数据工程师,和软件开发人员)。根据这些要求,我们设计并实现了一个模块化的系统架构。为了证明它的效用,我们利用欧洲一家大型大学医院的临床数据,应用原型监测COVID-19治疗建议的依从性.
结果:我们设计了一个系统,该系统将指南建议与实时临床数据集成在一起,以评估单个指南建议的依从性,并开发了功能原型。与临床工作人员进行的需求分析得出了一个流程图,描述了应如何监控对建议的遵守情况的工作过程。确定了四个核心要求:能够决定建议是否适用于特定患者,整合来自不同数据格式和数据结构的临床数据的能力,显示原始患者数据的能力,并使用基于资源的快速医疗保健互操作性格式来表示临床实践指南,以提供可互操作的,基于标准的指南推荐交换格式。
结论:我们的系统在个体患者治疗和医院质量管理方面具有优势。然而,需要进一步的研究来衡量其对患者结局的影响,并评估其在不同临床环境中的资源有效性.我们指定了模块化软件体系结构,该体系结构允许来自不同领域的专家独立工作并专注于他们的专业领域。我们已经在开源许可下发布了我们系统的源代码,并邀请合作进一步开发该系统。
Clinical practice
guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients.
This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype\'s utility in treatment recommendations.
We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a
consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital.
We designed a
system that integrates
guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources-based format for the representation of clinical practice
guidelines to provide an interoperable, standards-based guideline recommendation exchange format.
Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the
system.