Clarify Health Solutions, Inc.

Adam F Rogow

Justin E Warner

Jeffrey D Larson

Jean P Drouin

Samuel H Bauknight

Yale Wang

Todd Gottula

Erik Talvola

Emmet Sun

Computer network architectures for machine learning, and more specifically, computer network architectures for the automated completion of healthcare claims. Embodiments of the present invention provide computer network architectures for the automated completion of estimated final cost data for claims for healthcare clinical episodes using incomplete data for healthcare insurance claims and costs, known to date. Embodiments may use an automatic claims completion web application, with other computer network architecture components. Embodiments may include a combination of third-party databases to generate estimated final claims for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, social-economic data, medical data, search engine data, e-commerce site data, and other databases.

Computer network architecture with automated claims completion, machine learning and artificial intelligence

Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and active updates of outcomes. Embodiments of computer network architecture automatically update forecasts of outcomes of patient episodes and annual costs for each patient of interest after hospital discharge. Embodiments may generate such updated forecasts either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Computer network architecture with machine learning and artificial intelligence and active updates of outcomes

Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and risk adjusted performance ranking of healthcare providers. Embodiments of computer network architecture automatically make risk adjusted performance rankings of healthcare service providers and generate and transmit reports of the rankings. Embodiments may generate such rankings either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may include a combination of third-party databases to generate the updated forecasts for pending patient clinical episodes, and to drive the forecasting models for the same, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Computer network architecture with machine learning and artificial intelligence and risk adjusted performance ranking of healthcare providers

Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and the automatic development of patient care groupings of patient data. Embodiments of computer network architecture automatically generate care grouping, and organize and analyse patient care data accordingly, and generate and transmit reports of the care grouping definitions, data, and analysis. Embodiments may generate care groupings either occasionally on demand, or periodically, or as triggered by events such as an update of available data. Embodiments may include a combination system databases with data provided by system users, and third-party databases to generate the patient care groupings, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Computer network architecture with machine learning and artificial intelligence and care groupings

Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and automated improvement and regularization of forecasting models, providing rapid improvement of the models. Embodiments may generate such rapid improvement of the models either occasionally on demand, or periodically, or as triggered by events such as an update of available data for such forecasts. Embodiments may indicate, after the improvement of the models, that various web applications using the models may be rerun to seek improved results for the web applications. Embodiments may include a combination of third-party databases to drive the forecasting models, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Computer network architecture with machine learning and artificial intelligence and automated scalable regularization

Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and dynamic patient guidance. Embodiments automatically update patient guidance in the patient care plan, based on the effectiveness of the guidance to date, attributes of the patient, other updated information, ongoing experience of the network, and updated predictions of possible patient outcomes and metrics.

Computer network architecture with machine learning and artificial intelligence and dynamic patient guidance

The present disclosure is related generally to computer network architectures for machine learning, and more specifically, to computer network architectures for the automated production and distribution of custom healthcare performance benchmarks for specific patient cohorts. Embodiments allow specification and automated production of benchmarks using any of many dozens of patient, disease process, facility, and physical location attributes. Embodiments may use an analytic module web application and a benchmark service module web application, with other architecture components. Embodiments may include a combination of third-party databases to generate benchmarks and to drive the forecasting models, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

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San Francisco, CA, United States of America

Adam F Rogow