Value-Based Healthcare Platform Identifies Individuals at Risk for Diabetes With Over 80% Accuracy

Cedar Gate’s advanced artificial intelligence (AI) analytics platform enables early intervention and potential cost savings

Cedar Gate Technologies (Cedar Gate) identified individuals at risk for diabetes with over 80% accuracy across 1.2 million members within a 12-month timeframe. Leveraging its proprietary AI model, Cedar Gate’s findings represent the first commercially available and deployed value-based care platform with this level of accuracy, enabling preventive interventions at scale.

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“While there have been many university-based research studies showing the capability of AI-related diabetes prediction models, we’re applying our technology to commercial use cases, putting advanced analytic tools in the hands of business users to solve real-world problems”

“Diabetes is among the costliest diseases if left unmanaged. By predicting diagnosis at scale, payers and self-insured employers can partner with providers to intervene early to dramatically reduce the risk and cost of unmanaged diabetes,” said David B. Snow, Jr., Chairman and CEO of Cedar Gate. “Automatically and reliably predicting diabetes within a population gives our clients a powerful financial advantage by controlling spend through targeted prevention.”

The CDC reports 37.3 million people have diabetes, with an average expenditure of $9,601 per person, per year, according to the American Diabetes Association. Preventing individuals from developing the disease has the potential to eliminate millions of dollars of provider reimbursement by health plan sponsors.

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Through advanced technology, including machine learning neural networks, Cedar Gate is able to create predictive models that feed intuitive dashboards and enable active interventions related to chronic conditions, including diabetes.

The proprietary model and technology leveraged to predict diabetes utilized a medium threshold of patient criteria and was applied to 10% of Cedar Gate’s 12-million-member database. Data scientists at the company applied an iterative approach to building the model, which uses multiple years of claims data.

“While there have been many university-based research studies showing the capability of AI-related diabetes prediction models, we’re applying our technology to commercial use cases, putting advanced analytic tools in the hands of business users to solve real-world problems,” said Rajiv Mahale, Chief Product and Business Development officer at Cedar Gate. “Today, we have the technology to curb the trajectory of diabetes deeply and meaningfully, controlling cost and improving health outcomes at scale.”

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