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Envisioning a learning health care system: the electronic primary care research network, a case study.

机译：构想学习型医疗系统：电子基层医疗研究网络，一个案例研究。

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PURPOSE: The learning health care system refers to the cycle of turning health care data into knowledge, translating that knowledge into practice, and creating new data by means of advanced information technology. The electronic Primary Care Research Network (ePCRN) was a project, funded by the U.S. National Institutes of Health, with the aim to facilitate clinical research using primary care electronic health records (EHRs). METHODS: We identified the requirements necessary to deliver clinical studies via a distributed electronic network linked to EHRs. After we explored a variety of informatics solutions, we constructed a functional prototype of the software. We then explored the barriers to adoption of the prototype software within U.S. practice-based research networks. RESULTS: We developed a system to assist in the identification of eligible cohorts from EHR data. To preserve privacy, counts and flagging were performed remotely, and no data were transferred out of the EHR. A lack of batch export facilities from EHR systems and ambiguities in the coding of clinical data, such as blood pressure, have so far prevented a full-scale deployment. We created an international consortium and a model for sharing further ePCRN development across a variety of ongoing projects in the United States and Europe. CONCLUSIONS: A means of accessing health care data for research is not sufficient in itself to deliver a learning health care system. EHR systems need to use sophisticated tools to capture and preserve rich clinical context in coded data, and business models need to be developed that incentivize all stakeholders from clinicians to vendors to participate in the system.
机译：目的：学习型医疗保健系统是指将医疗保健数据转化为知识，将该知识转化为实践并通过先进的信息技术创建新数据的周期。电子初级保健研究网络（ePCRN）是一个由美国国立卫生研究院资助的项目，旨在促进使用初级保健电子健康记录（EHR）进行临床研究。方法：我们确定了通过链接到EHR的分布式电子网络进行临床研究的必要要求。在探索了各种信息学解决方案之后，我们构建了该软件的功能原型。然后，我们探讨了在美国基于实践的研究网络中采用原型软件的障碍。结果：我们开发了一个系统，可帮助从EHR数据中识别出符合条件的人群。为了保护隐私，计数和标记是远程执行的，没有数据从EHR传输出去。迄今为止，EHR系统缺少批量输出设备，并且在诸如血压之类的临床数据编码方面存在歧义，至今仍无法全面部署。我们创建了一个国际财团和一个模型，用于在美国和欧洲的各种正在进行的项目中共享ePCRN的进一步开发。结论：访问用于研究的医疗保健数据的方法本身不足以提供学习型医疗保健系统。 EHR系统需要使用复杂的工具来捕获和保存编码数据中的丰富临床环境，并且需要开发商业模型，以激励从临床医生到销售商的所有利益相关者参与该系统。

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