Continuum Health Technologies Corp.

Intry, LLC

Aquiles Mata

Michael Craig Tulkoff

Albert Scarasso

Aaron Salley

Jennifer Sethre

A platform for providing employment assistance services to enterprises and candidates is disclosed. For example, the platform trains, based on personal attributes of employees of multiple enterprise and work culture attributes associated with each employer of the multiple enterprises, a machine learning model that defines associations between the personal attributes and the work culture attributes. Further, the platform extracts information associated with a person from one or more sources on the Web where the person is represented, generates, based on the information associated with the person, a user profile associating personal attributes with the person, applies the user profile to the machine learning model; and receives an indication, from the machine learning model, of an employer profile that is compatible with the user profile, the employer profile including work culture attributes associated with an employer.

Artificial intelligence assisted hybrid enterprise/candidate employment assistance platform

A Statistical financial system to value patient visits to healthcare providers allows healthcare providers to optimize their financial outcomes when billing insurance payers for services rendered in the course of patient visits. Commercial healthcare information systems today lack the ability to value these services in advance. Given the complexity of valuing services based on many variables and resource limitations, a predictive valuation provides the user with the information for accurate and timely decision making Using the historical data, the system creates a predictive model of future claims by creating attribute patterns from the data. As new information comes into the system, these attribute patterns are compared to the real-time data in order to create a value for a service and an overall visit. Using the valuation, the system will now be able to prioritize all follow-up tasks. The overall result is a better financial performance for the healthcare provider.

Statistical financial system and method to value patient visits to healthcare provider organizations for follow up prioritization

A Statistical financial system to value patient visits to healthcare providers allows healthcare providers to optimize their financial outcomes when billing insurance payers for services rendered in the course of patient visits. Commercial healthcare information systems today lack the ability to value these services in advance. Given the complexity of valuing services based on many variables and resource limitations, a predictive valuation provides the user with the information for accurate and timely decision making. Using the historical data, the system creates a predictive model of future claims by creating attribute patterns from the data. As new information comes into the system, these attribute patterns are compared to the real-time data in order to create a value for a service and an overall visit. Using the valuation, the system will now be able to prioritize all follow-up tasks. The overall result is a better financial performance for the healthcare provider.

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Austin, TX, United States of America

Aquiles Mata