Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, and more specifically, to computer network architectures in the context of program rules, using combinations of defined patient clinical episode metrics and other clinical metrics, thus enabling superior performance of computer hardware. Aspects of embodiments herein are specific to patient clinical episode definitions, and are applied to the specific outcomes of highest concern to each episode type. Furthermore, aspects of embodiments herein produce more accurate and reliable predictions of possible patient outcomes and metrics.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments relate generally to computer network architectures for machine learning, and more specifically, to computer network architectures in the context of program rules, using combinations of defined patient clinical episode metrics and other clinical metrics, thus enabling superior performance of computer hardware. Aspects of embodiments herein are specific to patient clinical episode definitions, and are applied to the specific outcomes of highest concern to each episode type. Furthermore, aspects of embodiments herein produce more accurate and reliable predictions of possible patient outcomes and metrics.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and automated insight generation. Embodiments of computer network architecture automatically identify, measure, and generate insight reports of underperformance and over performance in healthcare practices. Embodiments may generate the insight reports of performance either occasionally on demand, or periodically, or as triggered by events such as an update of available data. Embodiments may include a combination of system databases with data provided by system users, and third-party databases to generate the insight reports, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and automated insight generation. Embodiments of computer network architecture automatically identify, measure, and generate insight reports of underperformance and over performance in healthcare practices. Embodiments may generate the insight reports of performance either occasionally on demand, or periodically, or as triggered by events such as an update of available data. Embodiments may include a combination of system databases with data provided by system users, and third-party databases to generate the insight reports, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

Abstract: 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.