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面向医疗保健的人口健康管理

数据工厂
Data Lake Analytics
事件中心
流分析
Power BI

解决方案构想 Solution Idea

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填充运行状况管理是一种重要的工具,医疗保健提供商越来越使用此工具来管理和控制增加的成本。Population Health Management is an important tool that is increasingly being used by health care providers to manage and control the escalating costs. 总体运行状况管理的关键是使用数据来改善运行状况结果。The crux of Population Health Management is to use data to improve health outcomes. 跟踪、监视和基准标记是填充运行状况管理的三个堡垒,旨在改进临床和运行状况,同时管理和降低成本。Tracking, monitoring, and bench marking are the three bastions of Population Health Management, aimed at improving clinical and health outcomes while managing and reducing cost.

在此解决方案中,我们将利用医院生成的临床和社会经济患者数据进行人口运行状况报告。In this solution, we will be leveraging the clinical and socioeconomic in-patient data generated by hospitals for population health reporting. 作为使用填充运行状况管理的机器学习应用程序的示例,使用模型来预测医院的长度。As an example of a machine learning application with population health management, a model is utilized to predict length of hospital stay. 它适用于医院和医疗保健提供商,可通过疾病预防和管理来管理和控制卫生保健支出。It is geared towards hospitals and health care providers to manage and control the health care expenditure through disease prevention and management. 可以在解决方案的手动部署指南中了解所使用的数据和医院保持模型的长度。You can learn about the data used and the length of hospital stay model in the manual deployment guide for the solution. 医院可利用这些结果来优化护理管理系统,并将其临床资源集中在患者上,并提供更多紧急需求。Hospitals can use these results to optimize care management systems and focus their clinical resources on patients with more urgent need. 了解他们通过填充运行状况报告提供的社区,可以帮助医院从收费服务支付过渡到基于价值的医疗,同时降低成本并提供更好的护理。Understanding the communities they serve through population health reporting can help hospitals transition from fee-for-service payments to value-based care while reducing costs and providing better care.

体系结构Architecture

体系结构关系图 下载此体系结构的SVGArchitecture diagram Download an SVG of this architecture.

说明Description

估计每日成本: $156Estimated Daily Cost: $156

使用 Cortana Intelligence Suite 您可以按照此处的说明,将总体运行状况管理解决方案组合在一起并进行部署。Using Cortana Intelligence Suite you can put together and deploy from the ground up a Population Health Management solution by following the instructions here. 若要查看使用 Cortana Intelligence Suite 操作时的整个总体运行状况管理解决方案,而不必手动启动和连接所有组件,可以使用此处提供的 "自动部署" 选项。To see the entire Population Health Management solution for Health care using Cortana Intelligence Suite in action without having to spin up and connect all the components manually, you can use the automated deployment option available here.

"部署" 按钮将启动一个工作流,该工作流将在指定的 Azure 订阅中的资源组内部署解决方案的实例。The 'Deploy' button will launch a workflow that will deploy an instance of the solution within a Resource Group in the Azure subscription you specify. 下图显示了用于医疗保健的人口健康管理解决方案的数据流和端到端管道。The architecture diagram below shows the data flow and the end-to-end pipeline for Population Health Management Solution for Healthcare. 此解决方案包含多个 Azure 服务,需要执行一些必要的手动步骤,以使用来自医院的模拟患者数据进行工作。The solution includes multiple Azure services and requires a few necessary manual steps to have a working end-to-end solution with simulated in-patient data from hospitals.

上述体系结构示意图显示了用于医疗保健的人口健康管理解决方案的解决方案设计。The architecture diagram above shows the solution design for Population Health Management Solution for Healthcare. 该解决方案由多个 Azure 组件组成,这些组件执行各种任务即引入、数据存储、数据移动、高级分析和可视化。The solution is composed of several Azure components that perform various tasks, viz. data ingestion, data storage, data movement, advanced analytics and visualization. Azure 事件中心 是将在此解决方案中处理的原始记录的引入点。Azure Event Hub is the ingestion point of raw records that will be processed in this solution. 然后,通过 Azure 流分析将这些方法推送到 Data Lake Store 进行存储和进一步处理。These are then pushed to Data Lake Store for storage and further processing by Azure Stream Analytics. 第二个流分析作业将所选数据发送到 PowerBI ,以获得近乎实时的可视化效果。A second Stream Analytics job sends selected data to PowerBI for near real time visualizations. Azure 数据工厂按计划对 Azure 流分析作业中原始事件的评分,方法是使用script.usqlR进行处理Azure Data Lake Analytics 。然后,分数的结果存储在Azure Data Lake Store ,并使用 Power BI 进行可视化。Azure Data Factory orchestrates, on a schedule, the scoring of the raw events from the Azure Stream Analytics job by utilizing Azure Data Lake Analytics for processing with both USQL and R. Results of the scoring are then stored in Azure Data Lake Store and visualized using Power BI.

部署后步骤Post Deployment Steps

将解决方案部署到订阅后,可通过单击最终部署屏幕上的资源组名称来查看部署的各种服务。Once the solution is deployed to the subscription, you can see the various services deployed by clicking the resource group name on the final deployment screen. 或者,可以使用 Azure 管理门户 查看订阅的资源组中预配的资源。Alternatively you can use Azure management portal to see the resources provisioned in your resource group in your subscription. 可在 此处找到解决方案的源代码和手动部署说明。The source code of the solution as well as manual deployment instructions can be found here. 部署后步骤构成了监视部署的运行状况,并实时可视化了总体运行状况报告,以及来自 "维持" 模型长度的预测结果。The post deployment steps constitute monitoring the health of your deployment and visualizing the Population Health Report in real time as well as the results of the predictions from length of stay model. 可在 此处找到部署后说明。The post deployment instructions can be found here.

组件Components

后续步骤Next steps