Incorporating Machine Learning into Clinical Episode Grouping

ML, Machine Learning, Predictive Analytics, Certilytics, CORE Pathways, Operational workflows, Algorithms, Cutting-edge technology
Incorporating Machine Learning into Clinical Episode Grouping

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.

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That’s why Certilytics built CORE Pathways, 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.

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 Pathways 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.

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In other words, CORE Pathways 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.

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