Abstract: Implementations described herein relate to methods, systems, and computer-readable media to generate an alert or recommendation based on an activity log. In some implementations, a computer-implemented method includes receiving, at a processor, an activity log associated with one or more enterprise sub-processes for a first time period, determining, at the processor, change context data based on a comparison of the entity data and the associated interaction data with corresponding entity data and associated interaction data of a second time period, analyzing the change context data to determine atomic event data, providing the atomic event data associated with the enterprise process as input to a trained machine learning model, determining, using the trained machine learned model and the atomic event data, a predicted future state of the enterprise process, and generating and transmitting an alert or recommendation to one or more data sinks associated with the enterprise process.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to generate an alert or recommendation based on an activity log. In some implementations, a computer-implemented method includes receiving, at a processor, an activity log associated with one or more enterprise sub-processes for a first time period, determining, at the processor, change context data based on a comparison of the entity data and the associated interaction data with corresponding entity data and associated interaction data of a second time period, analyzing the change context data to determine atomic event data, providing the atomic event data associated with the enterprise process as input to a trained machine learning model, determining, using the trained machine learned model and the atomic event data, a predicted future state of the enterprise process, and generating and transmitting an alert or recommendation to one or more data sinks associated with the enterprise process.

Abstract: The disclosed technology includes a system for modeling progression of lifetime healthcare expenses, including healthcare events resulting in an out-of-pocket expenditure per a given healthcare plan, wherein each healthcare event is associated with a group of healthcare events. Each group of healthcare events is associated with different sets of certainty with different groups of individuals. A central computing device communicates with healthcare event data generation sources to obtain the healthcare event data. A static database module stores the healthcare event data in hierarchical layered graphs. A dynamic database module dynamically generates data that depicts different future expenditures over a life span based on the healthcare event data in the static database module. A computer modeling module generates the most likely set of future expenditures using the different future expenditures data in the dynamic database module.

Abstract: The disclosed technology provides a method for generating quantitative recommendations for healthcare benefits plans using hierarchical layered graphs. One or more nodes are identified in the hierarchical layered graphs. The hierarchical layered graphs store historical healthcare claims data. The one or more nodes are identified based on contextual data associated with an employee and on similarities between the contextual data associated with the employee and node contextual data associated with the node. One or more paths are identified among the one or more identified nodes. A plurality of healthcare plans are scored using the hierarchical layered graphs by applying contextual data associated with the plurality of healthcare plans to the identified paths. A subset of the plurality of scored healthcare plans are identified and recommended to the employer or the employee.

Abstract: The disclosed technology includes a system for modeling progression of lifetime healthcare expenses, including healthcare events resulting in an out-of-pocket expenditure per a given healthcare plan, wherein each healthcare event is associated with a group of healthcare events. Each group of healthcare events is associated with different sets of certainty with different groups of individuals. A central computing device communicates with healthcare event data generation sources to obtain the healthcare event data. A static database module stores the healthcare event data in hierarchical layered graphs. A dynamic database module dynamically generates data that depicts different future expenditures over a life span based on the healthcare event data in the static database module. A computer modeling module generates the most likely set of future expenditures using the different future expenditures data in the dynamic database module.