Bucur Anca, van Leeuwen Jasper, Christodoulou Nikolaos, Sigdel Kamana, Argyri Katerina, Koumakis Lefteris, Graf Norbert, Stamatakos Georgios
Precision and Decentralized Diagnostics, Philips Research, Eindhoven, The Netherlands.
National Technical University of Athens, ICCS, Athens, Greece.
BMC Med Inform Decis Mak. 2016 Jul 21;16 Suppl 2(Suppl 2):87. doi: 10.1186/s12911-016-0314-3.
The adoption in oncology of Clinical Decision Support (CDS) may help clinical users to efficiently deal with the high complexity of the domain, lead to improved patient outcomes, and reduce the current knowledge gap between clinical research and practice. While significant effort has been invested in the implementation of CDS, the uptake in the clinic has been limited. The barriers to adoption have been extensively discussed in the literature. In oncology, current CDS solutions are not able to support the complex decisions required for stratification and personalized treatment of patients and to keep up with the high rate of change in therapeutic options and knowledge.
To address these challenges, we propose a framework enabling efficient implementation of meaningful CDS that incorporates a large variety of clinical knowledge models to bring to the clinic comprehensive solutions leveraging the latest domain knowledge. We use both literature-based models and models built within the p-medicine project using the rich datasets from clinical trials and care provided by the clinical partners. The framework is open to the biomedical community, enabling reuse of deployed models by third-party CDS implementations and supporting collaboration among modelers, CDS implementers, biomedical researchers and clinicians. To increase adoption and cope with the complexity of patient management in oncology, we also support and leverage the clinical processes adhered to by healthcare organizations. We design an architecture that extends the CDS framework with workflow functionality. The clinical models are embedded in the workflow models and executed at the right time, when and where the recommendations are needed in the clinical process.
In this paper we present our CDS framework developed in p-medicine and the CDS implementation leveraging the framework. To support complex decisions, the framework relies on clinical models that encapsulate relevant clinical knowledge. Next to assisting the decisions, this solution supports by default (through modeling and implementation of workflows) the decision processes as well and exploits the knowledge embedded in those processes.
临床决策支持（CDS）在肿瘤学中的应用可能有助于临床使用者有效应对该领域的高度复杂性，改善患者预后，并缩小临床研究与实践之间目前存在的知识差距。尽管在CDS的实施方面已投入大量精力，但在临床中的应用仍有限。文献中已广泛讨论了应用的障碍。在肿瘤学领域，当前的CDS解决方案无法支持患者分层和个性化治疗所需的复杂决策，也无法跟上治疗选择和知识的高更新率。
为应对这些挑战，我们提出了一个框架，以实现有意义的CDS的高效实施，该框架纳入了多种临床知识模型，以利用最新领域知识为临床带来全面解决方案。我们既使用基于文献的模型，也使用在p-医学项目中利用来自临床试验的丰富数据集以及临床合作伙伴提供的护理所构建的模型。该框架对生物医学社区开放，允许第三方CDS实施重用已部署的模型，并支持建模人员、CDS实施人员、生物医学研究人员和临床医生之间的协作。为了提高应用率并应对肿瘤学中患者管理的复杂性，我们还支持并利用医疗保健组织所遵循的临床流程。我们设计了一种架构，通过工作流功能扩展CDS框架。临床模型嵌入到工作流模型中，并在临床过程中需要建议的时间和地点适时执行。
在本文中，我们展示了在p-医学中开发的CDS框架以及利用该框架的CDS实施。为支持复杂决策，该框架依赖于封装相关临床知识的临床模型。除了协助决策外，此解决方案默认情况下（通过工作流的建模和实施）还支持决策过程，并利用这些过程中嵌入的知识。
