Why Healthcare Needs a New Breed of Analytics

Around the world, healthcare is coming under increasing pressure as expectations on health systems continue to rise and the cost of care continues to approach unsustainable levels.

In the US, there are mounting efforts to move from the traditional fee-for-service model to value-based reimbursement, where providers assume risk for the overall cost of care. Globally, health systems are striving to improve the delivery of care but are inevitably hamstrung by their inability to measure and track outcomes, ever-changing quality measures, and the processes they’re responsible for. At the heart of these issues and initiatives is the inability for healthcare organizations to effectively enable system-wide quality improvements and cost-reduction efforts.

Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it (H James Harrington, CIO, September 1999, p19).

Tackling the Integration Challenge

The foundation of any meaningful healthcare analytics implementation is a repository of high-quality data. Despite significant efforts from standards organizations like HL7®, initiatives like FHIR®, and regulatory incentives for vendors and healthcare organizations to improve system interoperability and data sharing, inconsistencies and quality issues plague the information exchange interfaces of healthcare systems. As a result of these challenges, a majority of analytics initiatives fail to move past the task of implementing and populating the underlying data repository.


As organizations look beyond core clinical and claims data integration to data sourced from devices, large genome data sets, and information from supporting systems that provide insights into behavioral, social, and environmental factors, healthcare analytics platforms will be presented with ever-increasing scalability challenges.

Flexible and Extensible

As healthcare organizations continue to broaden the data available for measuring processes and quality — and predicting outcomes across populations — the possibilities for refining existing reports and algorithms and creating entirely new ones are significant. To respond effectively to these rapidly evolving demands, healthcare analytics platforms will need to be much more flexible and extensible than ever before.

To help provide this flexibility, Orion Health has chosen Elasticsearch as the database to support its patient registries. Its high-performance, full-text indexing capability provides the ability for general-purpose registry designs to effectively support a much broader range of analysis without needing to design and deploy use-case-specific registries.

Care Delivery Integration

Another key challenge that hinders the effectiveness of today’s healthcare analytics solutions is the limited ability to use the insights generated by these platforms in the delivery of care. Identifying high-risk patients and patients with significant care gaps is meaningless unless physicians and care coordinators are informed and empowered to help those patients. Printed lists and spreadsheets are all too common in today’s healthcare environment and result in highly inefficient processes that hinder rather than help.

Amadeus integrates effortlessly with Coordinate, Orion Health’s care-coordination solution. Patient cohorts defined through Amadeus’ analytics application, Population Health Explorer, can be pushed to the provider’s workflow through shared worklists, triggering of tasks, and enrollment in care pathways, from which proactive care management interventions can occur.

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