Abstract: Embodiments in the present disclosure relate to computer network architectures and methods for predictive analysis using lookup tables as prediction models. The predictive analysis, including the generation of the lookup tables, performed by a predictive system of the present disclosure is driven entirely by a query language, such as Structured Query Language, in various embodiments. The predictive analysis, including the generation of the lookup tables, is performed without machine learning or generative artificial intelligence, in various embodiments.

Abstract: Embodiments in the present disclosure relate to computer network architectures and methods for predictive analysis using lookup tables as prediction models. The predictive analysis, including the generation of the lookup tables, performed by a predictive system of the present disclosure is driven entirely by a query language, such as Structured Query Language, in various embodiments. The predictive analysis, including the generation of the lookup tables, is performed without machine learning or generative artificial intelligence, in various embodiments.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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 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.