Machine Learning in Healthcare: What C-Suite Executives Must Know to Use it Effectively in Their Organizations (Executive Report)

Machine learning (ML) is gaining in popularity throughout healthcare. ML’s far-reaching benefits, from automating routine clinical tasks to providing visibility into which appointments are likely to no-show, make it a must-have in an industry that’s hyper focused on improving patient and operational outcomes.

This executive report—co-written by Microsoft Worldwide Health and Health Catalyst—is a basic guide to training machine learning algorithms and applying machine learning models to clinical and operational use case. This report shares practical, proven techniques healthcare organizations can use to improve their performance on a range of issues.

Why Predictive Modeling in Healthcare Requires a Data Warehouse (White Paper)

Interest in predictive modeling is part of a larger trend to employ business and clinical intelligence applications in healthcare. Until recently, organizations that had the ability to mine and analyze data were mostly conducting retrospective analyses. Using tools available today, organizations with the right technical infrastructure, including a data warehouse, can link predictions to specific clinical priorities, set up new workflows, apply analytics to emergency departments and to slowly changing clinical situations and more.