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Population Health Management for Healthcare

Population Health Management is an important tool that is increasingly being used by healthcare providers to manage and control the escalating costs. The crux of Population Health Management is to use data to improve health outcomes. Tracking, monitoring and benchmarking are the three bastions of Population Health Management, aimed at improving clinical and health outcomes while managing and reducing cost.

In this solution, we will be leveraging the clinical and socio-economic in-patient data generated by hospitals for population health reporting. As an example of a machine learning application for population health management, a model is utilised to predict length of hospital stay. It is geared towards hospitals and healthcare providers to manage and control healthcare expenditure through disease prevention and management. You can learn about the data used and the length of hospital stay model in the manual deployment guide for the solution. Hospitals can use these results to optimise care management systems and focus their clinical resources on patients with more urgent needs. 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.

Description

Note: If you have already deployed this solution, click here to view your deployment.

Estimated daily cost: $156

Estimated provisioning time: 35 minutes

Using Cortana Intelligence Suite you can put together and deploy from the ground up a Population Health Management solution by following the instructions here. To see the entire Population Health Management solution for Healthcare 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.

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. The architecture diagram below shows the data flow and the end-to-end pipeline for Population Health Management Solution for Healthcare. The solution includes multiple Azure services and requires a few manual steps to obtain a working end-to-end solution with simulated in-patient data from hospitals.

The architecture diagram below shows the solution design for Population Health Management Solution for Healthcare. The solution is composed of several Azure components that perform various tasks in relation to data ingestion, data storage, data movement, advanced analytics and visualisation. Azure Event Hub is the ingestion point of raw records that will be processed in this solution. These are then pushed to Data Lake Store for storage and further processing by Azure Stream Analytics. A second Stream Analytics job sends selected data to PowerBI for near real-time visualisations. Azure Data Factory orchestrates, on a schedule, the scoring of the raw events from the Azure Stream Analytics job by utilising Azure Data Lake Analytics for processing with both USQL and R. Results of the scoring are then stored in Azure Data Lake Store and visualised using Power BI.

Post-Deployment Steps

Once the solution has been deployed to the subscription, you can see the various services deployed by clicking the resource group name on the final deployment screen. Alternatively, you can use Azure management portal to see the resources provisioned in your resource group in your subscription. The source code of the solution as well as manual deployment instructions can be found here. The post-deployment steps consist of monitoring the health of your deployment and visualising the Population Health Report in real time, as well as the results of predictions from the length-of-stay model. The post-deployment instructions can be found here.

Disclaimer

©2017 Microsoft Corporation. All rights reserved. This information is provided “as is” and may change without notice. Microsoft makes no warranties, express or implied, with respect to the information provided here. Third-party data was used to generate the solution. You are responsible for respecting the rights of others, including procuring and complying with relevant licences in order to create similar datasets.