At its heart, the Geneia Data Intelligence Lab (GDI Lab) uses leading-edge data science to drive lower healthcare costs and improve health outcomes.
Our lab prioritizes projects that address major cost drivers and enable our clients – health plans, hospitals, physicians and their value-based partners – to identify, stratify and predict high-cost patients and conditions such as heart failure and diabetes.
Our models help clients to intervene earlier with patients whose risk is expected to rise and/or who are at risk for major, expensive conditions, allowing healthcare organizations to mitigate future costs while improving the health of those they serve.
Featured Resource
Using Low Dimensional Representation of Medical Concepts to Improve Population Health
Chief Analytics and Technology Officer Fred Rahmanian discusses how Geneia’s use of low dimensional representation of medical concepts allows health plans, hospitals and physician organizations to better identify, stratify and engage patient cohorts.
This presentation was originally part of the Predictive Analytics World for Healthcare 2020 virtual conference on June 3rd, 2020.
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How Geneia data scientists collaborate with clinicians
Geneia clinicians help improve the usability of our analytic models.

Predicting high-cost claimants to control spend and improve outcomes
Next-generation predictive model uses less data and delivers more accurate, personalized and flexible results.

Geneia Conversations: AI Model Interpretability
GDI Lab data scientist Andrew Fairless explains importance of interpretability.
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Webinar: How Geneia Uses Machine Learning and Analytics to Drive Innovation
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Data Science
Next-Generation Identification, Stratification and Engagement Improves Triple Aim

Data Science
Geneia Data Intelligence Lab: Predicting Opioid Abuse & Overdose

Data Science
Geneia Data Intelligence Lab: Using AI to Drive Lower Healthcare Cost
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Data Science
AI Fireside Chat Series: Do you think AI has lagged in healthcare?

Data Science
AI Fireside Chat Series: What's the low-hanging fruit of AI in healthcare?
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