基于指南的决策支持在移动设备上的分布式应用：实现与评价
Distributed Application of Guideline-Based Decision Support through Mobile Devices: Implementation and Evaluation
摘要：
传统上，基于指南（GL）的决策支持系统（DSS）使用集中式基础设施来向护理提供者生成建议。然而，在家管理患者更可取，可以降低成本并赋予患者权力。我们的目的是设计，实施，并证明一个分布式DSS的新架构的可行性，提供个性化的，上下文敏感的，基于证据的指导，通过他们的移动终端的患者，并增加了GL的分布式应用程序的鲁棒性，同时保持访问患者的纵向记录和最新的证据为基础的GL存储库。我们已经设计并实现了一种新的投影和回调（PCB）模型，其中小部分的证据为基础的GL程序的知识，适应患者的喜好和他们的当前上下文，从中央DSS服务器，投影到本地DSS的患者移动终端，应用该知识。在适当的时候，如由计划内的时间模式所定义的，本地DSS回叫中央DSS，请求进一步的帮助，可能是另一个计划。因此，GL规范包括两个级别：一个用于中央DSS，一个用于本地DSS。我们通过管理西班牙的糖尿病患者和意大利的房颤患者，成功评估了ECOGuide EU项目中的PCB模型。两种GL表示之间存在显着差异，这表明了描述GL的其他方法。两种GL的中枢和局部相互作用之间的平均时间差异很大：妊娠期糖尿病为3.95天，房颤为23.80天。大多数互动，83%，是由于预测的mDSS。其他是数据通知，主要是为了改变上下文。通过从多个本地DSS崩溃中成功恢复，证明了鲁棒性。
英文摘要：
Traditionally Guideline(GL)based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers. However, managing patients at home is preferable, reducing costs and empowering patients. We aimed to design, implement, and demonstrate the feasibility of a new architecture for a distributed DSS that provides patients with personalized, context-sensitive, evidence based guidance through their mobile device, and increases the robustness of the distributed application of the GL, while maintaining access to the patient longitudinal record and to an up to date evidence based GL repository. We have designed and implemented a novel projection and callback (PCB) model, in which small portions of the evidence based GL procedural knowledge, adapted to the patient preferences and to their current context, are projected from a central DSS server, to a local DSS on the patient mobile device that applies that knowledge. When appropriate, as defined by a temporal pattern within the projected plan, the local DSS calls back the central DSS, requesting further assistance, possibly another projection. Thus, the GL specification includes two levels: one for the central DSS, one for the local DSS. We successfully evaluated the PCB model within the MobiGuide EU project by managing Gestational Diabetes Mellitus patients in Spain, and Atrial Fibrillation patients in Italy. Significant differences exist between the two GL representations, suggesting additional ways to characterize GLs. Mean time between the central and local interactions was quite different for the two GLs: 3.95 days for gestational diabetes, 23.80 days for atrial fibrillation. Most interactions, 83%, were due to projections to the mDSS. Others were data notifications, mostly to change context. Robustness was demonstrated through successful recovery from multiple local DSS crashes.
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