Abstract: A system and method to authorize requests for procedures or medical claim requests is described. In one aspect, datasets from multiple data sources, including historical authorizations associated with various medical claims, are processed. The machine learning engines and the machine learning models are trained based on the received data. Simultaneously, real-time authorization requests are received and analyzed from secondary data sources. Each request is split into service requests based on second attributes, which include identifiers linked to services. The services are encoded using fixed-length encoding, and unique numeric identifiers are assigned to categorical variables. For each encoded service request, contexts, actions, and decisions are determined by comparing the first attributes of historical authorization requests with the second attributes of real-time requests.

Abstract: Configurations of a system and a method for a dynamic trust-based authorization system (DTBAS), are described. In one aspect, the DTBAS may implement an execution of operations of multiple engines, models, framework, circuits, code, etc., for dynamically authorizing a request for a service or an operation. The DTBAS, when deployed in a managing entity, may receive a request from a managed entity to execute an operation or task. Based on parameters or attributes associated with the managed entity, the DTBAS may execute operations to evaluate a trust score. Based on the evaluated trust score, the DTBAS may compute a weight decision matrix that may include multiple weights associated with the managed entity. Based on the computed weight decision matrix and the evaluated trust score, the DTBAS may authorize or deny the execution of the requested operation.

Abstract: A system and method to authorize requests for procedures or medical claim requests is described. In one aspect, datasets from multiple data sources, including historical authorizations associated with various medical claims, are processed. The machine learning engines and the machine learning models are trained based on the received data. Simultaneously, real-time authorization requests are received and analyzed from secondary data sources. Each request is split into service requests based on second attributes, which include identifiers linked to services. The services are encoded using fixed-length encoding, and unique numeric identifiers are assigned to categorical variables. For each encoded service request, contexts, actions, and decisions are determined by comparing the first attributes of historical authorization requests with the second attributes of real-time requests.

Abstract: Configurations of a system and a method for a dynamic trust-based authorization system (DTBAS), are described. In one aspect, the DTBAS may implement an execution of operations of multiple engines, models, framework, circuits, code, etc., for dynamically authorizing a request for a service or an operation. The DTBAS, when deployed in a managing entity, may receive a request from a managed entity to execute an operation or task. Based on parameters or attributes associated with the managed entity, the DTBAS may execute operations to evaluate a trust score. Based on the evaluated trust score, the DTBAS may compute a weight decision matrix that may include multiple weights associated with the managed entity. Based on the computed weight decision matrix and the evaluated trust score, the DTBAS may authorize or deny the execution of the requested operation.

Abstract: System and method for optimizing outcomes for healthcare entities, are described. In one aspect, the system includes an implementation of an optimization framework of machine learning engines, models, multiple sources of information or data. The machine learning engines may cooperatively collaborate with the models and execute operations on the data from the diverse data sources. In response to execution of the operations, the machine learning engines of the system may identify and/or determine multiple outcomes that may be optimized. The results of the execution of the operations by the machine learning engines of the system may provision analyzing different scenarios, analyzing recommendations, and take actionable steps based on the results of the analysis. Upon implementing the actionable steps, the outcomes for the healthcare entities may be optimized.

Abstract: System and method for scaling up an access to a professional expert, in real time or on demand, is described. In one aspect, the system may include a communicatively couple arrangement of multiple input data sources, an artificial intelligence (AI) mediated multimodal communication system (MMCS), a professional experts system, and external data sources. The AI MMCS may receive data or information in multiple formats from the input data sources, process the received data, and based on multiple attributes, such as a domain area, skills, areas of expertise, availability, etc., the AI MMCS may facilitate connecting with a professional expert. When the professional expert of is unavailable or unable to provide a further improvised resolution, the AI MMCS may, execute operations to make determinations and provide access to a next level of professional expert who may provide resolution/remediation to the end user.

Abstract: A system and a method to optimize values delivered by an enterprise, are described. An integrated system may receive data from multiple data sources that may be processed by engines and/models. The engines and/or models may execute operations or functions, such as data processing, analysis based on rules, automated learning, machine learning and transforming the data to create value graphs associated with processes, tasks, and/or services in the enterprise. The value graphs may be generated that may provision continuous monitoring and insights to measure of KPIs and other influencing factors that may be associated with the processes, and services in the enterprise. The integrated system may generate visualizations that may be rendered via user interfaces. Further based on the value graphs, stakeholders may be able to identify opportunities associated with processes, tasks, and/or services that may be actioned to drive employee engagement, and value of the enterprise.

Abstract: A system and a method to optimize values delivered by an enterprise, are described. An integrated system may receive data from multiple data sources that may be processed by engines and/models. The engines and/or models may execute operations or functions, such as data processing, analysis based on rules, automated learning, machine learning and transforming the data to create value graphs associated with processes, tasks, and/or services in the enterprise. The value graphs may be generated that may provision continuous monitoring and insights to measure of KPIs and other influencing factors that may be associated with the processes, and services in the enterprise. The integrated system may generate visualizations that may be rendered via user interfaces. Further based on the value graphs, stakeholders may be able to identify opportunities associated with processes, tasks, and/or services that may be actioned to drive employee engagement, and value of the enterprise.

Abstract: Staffing is allocated by expressing a risk of violating a service level agreement for a given service line as a function of a number of full-time equivalents allocated to the given service line and a number of service tickets received at the given service line per unit of time per ticket severity level. The number of service tickets are processed by the number of full-time equivalents allocated to the given service line. Risks are summed across a plurality of distinct service lines to generate a total risk. Total risk is minimized by adjusting the number of full time equivalents allocated to each given service line across all services lines subject to a pre-determined reduction in total cost to process all service tickets by the number of full-time equivalents across all service lines and a pre-determined range of a permissible number of full-time equivalents for each service line.