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Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growing Health Data Demands (Executive Report)

Smartphone applications, home monitoring equipment, genomic sequencing, and social determinants of health are adding significantly to the scope of healthcare data, creating new challenges for health systems in data management and storage. Traditional on-premises data warehouses, however, don’t have the capacity or capabilities to support this new era of bigger healthcare data.

Organizations must add more secure, scalable, elastic, and analytically agile cloud-based, open-platform data solutions that leverage analytics as a service (AaaS). Moving toward cloud hosting will help health systems avoid the five common challenges of on-premises data warehouses:

  1. Predicting future demand is difficult.
  2. Infrastructure scaling is lumpy and inelastic.
  3. Security risk mitigation is a major investment.
  4. Data architectures limit flexibility and are resource intensive.
  5. Analytics expertise is misallocated.

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.

Hadoop in Healthcare: Getting More from Analytics (White Paper)

Healthcare data is positioned for momentous growth as it approaches the parameters of big data. While more data can translate into more informed medical decisions, our ability to leverage this mounting knowledge is only as strong as our data strategy. Hadoop offers the capacity and versatility to meet growing data demands and turn information into actionable insight.

Specific use cases where Hadoop adds value data strategy include:

  1. Archiving
  2. Streaming
  3. Machine learning

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.