A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.
A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.
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DOI：
10.13063/2327-9214.1168
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Hutton JJ
中科院分区：
文献类型：
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作者：
Marsolo K;Margolis PA;Forrest CB;Colletti RB;Hutton JJ
我们与improecarenow网络合作，为基于网络的学习健康系统创建了一个概念验证架构。这种协作涉及将现有注册中心转换为与电子健康记录（EHR）相关联的注册中心，从而实现“一次数据”战略。我们试图自动化一系列支持护理改进的报告，同时也展示了观察性注册数据在比较有效性研究中的使用。我们与三家领先的电子病历供应商合作，创建了基于电子病历的数据收集表单。我们自动化了许多ImproveCareNow的分析报告，并开发了一个用于存储受保护的健康信息和跟踪患者同意的应用程序。最后，我们部署了一个队列识别工具来支持可行性研究和假设生成。该系统正在被不断采用。到目前为止，31个中心已经采用了基于电子病历的表格，21个中心正在向登记处上传数据。自动报告的使用率仍然很高，研究人员已经使用队列识别工具来响应几个临床试验请求。当前创建基于ehr的数据收集表单的流程要求团队单独与每个供应商合作。一个与供应商无关的模型将允许更快的吸收。我们认为，将基于网络的注册中心与电子病历连接起来，将使它们成为决策支持的来源。然而，为了实现这一愿景，还需要额外的标准。我们已经成功地实施了一个概念验证的学习卫生系统，同时为其他人提供了一个可以建立的基础。我们还强调了赞助商可以帮助加快进展的机会。
We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a “data in once” strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research. We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow’s analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests. The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however. We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.