Incorporating Machine Learning into Clinical Episode Grouping

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December 16, 2019

CORE® Clinical Episode Grouping was Built for Sophisticated Predictive Analytics

As healthcare organizations increasingly incorporate predictive analytics into their operational workflows, it is becoming more important for clinical episode groupers to be designed with machine learning in mind.

But many existing commercial episode groupers continue to leverage decades-old technology with basic logic assumptions unchanged—making their output ill-suited for sophisticated predictive analytics.

That’s why Certilytics built CORE® Clinical Episode Grouping, which optimizes data for machine learning and deep learning, empowering our customers to:

  • Identify, report, and benchmark cost trends, provider treatment patterns, condition severity, and health outcomes across billions of member records.
  • Standardize and aggregate raw claims data to perform analysis at the member level, enabling advanced predictive analytics of complete episodes of care, utilization patterns, emerging disease onset, gaps in care, and a member’s likelihood of engagement.
  • Develop high-performance provider networks supporting fee-for-service and value-based arrangements.
  • Evaluate care pathways and costs for a given condition.
  • Review performance, perform risk assessments, and analyze spend, helping business and clinical leaders make more informed decisions.

A Full Patient Journey

Unlike many legacy clinical episode groupers, CORE® (which stands for Collection of Related Events) is ideal for machine learning because of the way easy-to-understand features are strategically engineered through a complex series of clinically based algorithms.

For example, CORE® prepares analytic features such as a patient’s history of emergency room visits, inpatient admissions, unsupervised Rx refills, chronic conditions, condition severity, and many others. All of this information can be incorporated into predictive models designed to identify at-risk patients before they are diagnosed with chronic conditions or experience high-cost events.

The CORE® Engine analyzes all available data but organizes the output into specific target input periods with associated severity and control calculations, allowing users to tailor specific input populations and time periods for analysis and modeling.

In other words, CORE® assembles a patient’s entire healthcare journey, allowing for time period cross sections to be analyzed independently or as a whole.

This means that, in contrast to some legacy groupers, a patient’s entire history of diagnosis, intervention, treatment, and recovery is available for analysis, and no information about the patient is lost or ignored. This is extremely important given the individualized nature of healthcare, and CORE® allows for these deeper insights.

The result is more accurate predictive models that our customers have used to achieve millions of dollars in annual savings.

What Makes CORE® Different

At Certilytics, we were dissatisfied with existing episode groupers, many of which continue to leverage decades-old technology developed before critical advances in big data processing and machine learning. CORE® started as a research project to develop a better episode and condition grouping methodology to feed our machine learning models. What we discovered along the way was a radically different way of handling episode and condition grouping that we couldn’t find with commercially-available alternatives.

CORE Pathways Comparison Graphic

CORE® has grown from a prototype to a product to the backbone of our predictive analytics platform—providing a foundation for retrospective and prospective analysis across our products and solutions. The claim line-level data from CORE® —what we call CORE Report—has been used by health plans, PBMs, and other healthcare service providers to empower detailed retrospective analysis, provider assessments, intervention targeting, and clinical program management.

CORE® can group billions of health records into 450+ unique conditions on a regular basis—providing episode grouping accuracy at a speed and volume unseen in the market to-date.

Here are a few reasons to choose CORE:

  • Built on cutting-edge technology incorporating the latest advances in machine learning and big data analytics. The output of CORE Pathways is optimized for machine learning using our BrainstormAI® platform or your own internal tools.
  • Single integrated solution for clinical and financial analysis means users don’t have to join across multiple tools to get the answers they need.
  • Scaled for enterprise health care data intelligence with billions of claim records processed every week “as a Service” and the ability to deploy on-premises or in your private cloud environment.
  • Agile development practices ensure the product will continue to evolve over the foreseeable future—no 15+ year-old-technology bootstrapped into other applications.
  • Breaking-up grouping and reporting into separate processes ensures the best grouping based on all available historical data instead of limiting to a specific period of analysis.
  • Advanced pharmacy claim grouping logic ensures that commonly prescribed drugs or medications with multiple on—and off—label uses don’t end up ungrouped or ignored.

At Certilytics, next-generation technology has never been an afterthought. Focusing on the future of healthcare analytics is at the heart of everything we do, and our entire technology stack is built to empower and learn from the advanced machine learning technologies we’ve developed over years of research.

To learn more about CORE Pathways, contact us!