Nayya Health, Inc.

Akash Magoon

Aman Magoon

Sina Chehrazi

John Joseph Glorioso, Jr

David Feldman

Josh Allen Brown

Ishan Babbar

Satvik Gadamsetty

Lisa Renee Carpenter

Bradley Verdino

Mark Farnum

Daniel Young

Motaz Abdullah Balghonaim

Edward David Rothschild

A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associated with the insured user.

Machine-learning driven real-time data analysis

A data processing system for recommending insurance plans implements obtaining an electronic copy of demographic information associated with a user; analyzing the demographic information with a first machine learning model to recommend a bundle of insurance policies based on the demographic information, wherein the first machine learning model is configured to group insured people having similar demographics into clusters and to generate the bundle of insurance policies based on predicted medical insurance consumption associated with a respective group into which the model predicts that the first user falls; customizing the recommended bundle of insurance policies based on the demographic information associated with the user to generate a customized bundle of insurance policies; generating an insurance recommendation report that presents the customized bundle of insurance policies to the user; and causing a user interface of a display of a computing device associated with the user to present the insurance recommendation report.

Machine-learning driven data analysis based on demographics, risk, and need

A data processing system for machine-learning driven data analysis and reminders implements obtaining an electronic copy of demographic information and an electronic copy of insurance and benefits information associated with a user; providing the demographic information and the insurance and benefits information to a first machine learning model; analyzing the demographic information and the insurance and benefits information with the first machine learning model to output a first benefits utilization prediction that the one or more benefits are available to the user; providing the first benefits utilization prediction as an input to a recommendation engine; generating, using the recommendation engine, a benefits usage summary recommendation report that presents the information regarding the one or more benefits available to the user based on the first benefits utilization prediction; and causing a user interface of a display of a computing device to present the benefits usage summary recommendation report.

Machine-learning driven data analysis and reminders

A data processing system for machine-learning driven price guidance implements obtaining location information indicative of a location associated with a user; obtaining prescription information for a first prescription and first cost prescription; obtaining, from one or more pharmacy benefits managers, prescription cost information for a prescription from a plurality of pharmacies; analyzing the prescription cost information, the location associated with the user, and the prescription information using a machine learning model to obtain a prescription cost information prediction indicating that a first subset of the plurality of pharmacies provide the prescription at a second prescription cost lower than the first prescription cost; providing the prescription cost information prediction to a pharmacy recommendation unit as an input; generating a prescription savings opportunity report that presents the prescription cost information; and causing a user interface of a display of a computing device to present the prescription savings opportunity report.

Machine-learning driven pricing guidance

A data processing system for machine-learning driven data analysis implements obtaining digital healthcare service provider information associated with one or more digital healthcare service providers and query parameters information from a user searching for digital healthcare service providers that may provide digital healthcare services to employees of the user. The system analyzes query parameter information using a first machine learning model to obtain category information. The system analyzes the category information and the digital health service provider information to predict digital healthcare service providers that provide services that the user may be interested in provided to the employees. These predictions are provided to the user as digital healthcare service recommendations.

Machine-learning driven data analysis and healthcare recommendations

A data processing system for machine-learning driven data analysis and reminders implements obtaining insurance claim information associated with a plurality of insurance claims associated with a user, user demographic information for the user or both and obtaining digital healthcare service provider information associated with one or more digital healthcare service providers. The system further implements analyzing the insurance claim information and the user demographic information using a first machine learning model to obtain claim categorization information identifying a types of insurance claims that the user has filed or is likely to file, and analyzing the digital healthcare service provider and the claim categorization information to predict digital health services that the user may benefit from based on the claim categorization information and categories of digital health services included in the digital healthcare service provider information, and providing the digital healthcare service recommendations to a computing device associated with the user.

A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with an insured user, analyzing the insurance claim information using a first machine learning model to identify an occurrence of a first type of event and a first group of insurance claims associated with the first type of event; analyzing the first group of insurance claims, the policy information, and an indication of the first type of event using a second machine learning model to obtain a prediction whether a respective insurance policy will cover the first insurance claim; and responsive to determining that the respective insurance policy will cover the first insurance claim, causing a user interface to be presented on a display of a computing device associated with the insured user to guide the insured user through submitting the first insurance claim to an insurance provider associated with the respective insurance policy.

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Fallston, MD, United States of America

Akash Magoon