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Jorie AI Introduces Clinically Driven Predictive Analytics to Support Proactive Care and Revenue Performance
Hospital-specific predictive analytics designed to identify risk earlier, improve outcomes, and significantly reduce costs
SAN FRANCISCO, CA / ACCESS Newswire / January 12, 2026 / Jorie AI is proud to unveil its latest SmartCore Engine AI module at the J.P. Morgan Annual Conference. The new predictive analytics automation helps healthcare organizations better understand patient risk, support clinically informed engagement, and strengthen financial performance across diverse disease states and care pathways.
The offering, known as Know Your Patient (KYP), is a clinically driven predictive analytics capability built using each hospital's own historical data. Rather than applying generic risk models, KYP creates hospital-specific intelligence that segments patient populations based on clinical, behavioral, utilization, and demographic factors - enabling care teams to focus resources where they can have the greatest impact.
KYP enables more proactive, personalized patient engagement while aligning clinical decisions with financial sustainability. With clearer visibility into risk and utilization, organizations can intervene earlier, improve care coordination, and reduce preventable downstream costs.
An initial use case for the platform focuses on chronic kidney disease (CKD), a condition often associated with complex care needs and high variability in outcomes. Within this population, KYP analyzes historical EHR data to surface risk patterns, peer comorbidities, and progression indicators - helping care teams differentiate between patients who require intensive outreach and those best served through routine monitoring and preventive care. The same analytics framework is designed to apply across multiple disease states, with CKD serving as an initial example.
Unlike traditional analytics tools, KYP is customized for each organization. Models are developed and validated using a hospital's own data and care protocols, with implementation typically completed within 8-12 weeks. As patient outcomes and utilization data flow back into the system, the models continue to adapt-creating a living intelligence layer that evolves alongside clinical practice.
By identifying patients at higher risk earlier, care teams can act before conditions escalate into life-threatening events, while also reducing avoidable costs. At the same time, proactive intervention helps health systems avoid preventable admissions, complications, and high-cost utilization - delivering significant financial impact alongside better care.
"Generic risk models don't reflect how hospitals actually care for patients," said Chris Mashburn, COO at Jorie AI. "Know Your Patient is built using each organization's own data, allowing predictive analytics to align with real clinical workflows and real-world outcomes across all disease states."
As healthcare organizations face increasing pressure to manage complex patient populations with limited resources, clinically driven predictive analytics offer a path forward - enabling smarter decisions that benefit both patients and the bottom line.
Know Your Patient is now available for pilot deployment, allowing hospitals to evaluate its impact using their own data, care models, and operational goals.
To learn more about Jorie AI's predictive analytics capabilities or explore a pilot in your organization, visit www.jorie.ai or connect with the Jorie AI team
About Jorie AI
Jorie AI delivers advanced intelligent automation and analytics built to solve healthcare's most complex operational and clinical challenges. Designed to integrate seamlessly with existing systems, Jorie AI transforms fragmented data into actionable insight-helping healthcare organizations improve decision-making, strengthen performance, and deliver better outcomes across the care continuum.
Contact Information
Ashley Hibbetts
Director of Marketing
[email protected]
224-378-0092
SOURCE: Jorie AI
View the original press release on ACCESS Newswire
O.Karlsson--AMWN