Incorporating Machine Learning into Clinical Episode Groupin
LOUISVILLE, Ky., Dec. 19, 2019 /PRNewswire/ -- 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 Pathways, which optimizes data for machine learning and deep learning, empowering our customers to:
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 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.
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.
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 Pathways 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 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 Pathways—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 Pathways 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:
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!
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