Lumiata launches AI-powered predictive tool to identify at-risk patients

The technology leverages the company’s 175 million patient-record years and a patient’s medical history to predict the patient’s future health.
By Jessica Davis
12:14 PM

Lumiata, a provider of AI-powered predictive health analytics, today launched Risk Matrix, an advanced predictive tool able to forecast the future health of a patient using clinical conditions and diagnoses.

 

Leveraging the company’s holistic models based on its database of more than 175 million patient-record years, Risk Matrix makes custom predictions across multiple timeframes and explains the amount of risk, Lumiata executives said.

 

The platform is touted as the only predictive tool able to provide evidentiary clinical rationale with each prediction to better enable providers. According to Lumiata, Risk Matrix also provides real-time predictions for 20 chronic conditions, such as diabetes, chronic kidney disease and congestive heart failure.

 

“To deliver on the promise of value-based care, the industry desperately needs next-generation tools that can leverage the volume of health data that exists today, make sense of its complexity and provide personalized, actionable, real-time insights,” Ash Damle, Lumiata’s founder and CEO said in a statement.

 

Risk Matrix collects customer datasets such as claims information, EHR data, labs and other data types organized using the FHIR standard. This data is run through its deep learning models, which creates personalized predictions for each patient.

 

Once a patient is mapped into FHIR, the output can be generated for more than a million records in less than three hours. Users can access the platform’s analyses with a FHIR-compliant API, which allows for the data to be incorporated into the output of existing workflows.

“One of the biggest challenges for any risk bearer, but certainly for large health plans, is the struggle to fully utilize the different data types that are available today, Ed Cymerys, former chief actuary for Blue Shield of California said in a statement.

“More precise predictions are necessary, but sufficient to fundamentally improve risk and care management in a value-based model,” he added. “But strong predictions coupled with clinical evidence helps overcome one of the industry’s biggest challenges - giving providers the confidence to act.”

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