Abstract: A system includes an orchestration engine. The orchestration engine is configured to receive communications from a client device of a client and a provider device of a provider. The orchestration engine is configured to execute workflows across a plurality of artificial intelligence engines. The plurality of artificial intelligence engines includes a first set of artificial intelligence engines, a second set of artificial intelligence engines, and a third set of artificial intelligence engines.

Abstract: A system includes first, second and third input data sets. The first input data set includes demographic information characterizing a patient. The second and third input data sets characterize a healthcare treatment history of the patient. A neural network includes first, second and third neural subnetworks. The first neural subnetwork is configured to process the first input data set to produce a first output data set. The second neural subnetwork is configured to process the second input data set to produce a second output data set. The third neural subnetwork is configured to process the third input data set to produce a third output data set. An autoencoder layer has an input layer comprising the first, second and third output data sets and is configured to process the first, second and third output data sets to produce a secondary output data set.

Abstract: A unified framework for healthcare workflows to introduce multiple integrated approaches to error analysis. A first approach uses machine learning to extend probabilistic record linkage and apply it to the task of reconciliation, classifying changes between datasets as intentional or unintentional. A second approach uses process mining to extract maximum information about process diagrams and process bottlenecks.

Abstract: A system includes first, second and third input data sets. The first input data set includes demographic information characterizing a patient. The second and third input data sets characterize a healthcare treatment history of the patient. A neural network includes first, second and third neural subnetworks. The first neural subnetwork is configured to process the first input data set to produce a first output data set. The second neural subnetwork is configured to process the second input data set to produce a second output data set. The third neural subnetwork is configured to process the third input data set to produce a third output data set. An autoencoder layer has an input layer comprising the first, second and third output data sets and is configured to process the first, second and third output data sets to produce a secondary output data set.

Abstract: A unified framework for healthcare workflows to introduce multiple integrated approaches to error analysis. A first approach uses machine learning to extend probabilistic record linkage and apply it to the task of reconciliation, classifying changes between datasets as intentional or unintentional. A second approach uses process mining to extract maximum information about process diagrams and process bottlenecks.

Abstract: A computer-implemented method includes defining a set of multiple stakeholder entities. For each stakeholder entity of the set of stakeholder entities, a corresponding set of multiple health outcomes (Qi) is defined. A set of multiple costs (Cj) is defined. Each cost of the set of costs corresponds to a respective health outcome (Qi) of the set of health outcomes. For each stakeholder entity of the set of stakeholder entities, a value (V) is determined according to the equation V=(?inwiQi)/(?jmejCj), wherein wi represents a set of numerical weights and ej represents a set of episodes associated with a particular cost to create a set of values (V). A numerical value (V?) optimal to the set of stakeholder entities is determined from the set of values (V).

Abstract: This invention relates generally to methods of measuring interfacial areas, and more specifically to flow-based methods for measuring the area of the interfaces between two immiscible fluids. The immiscible fluids of interest are various oils and gases that do not have significant solubility, and thus form a distinct fluid phase and exist as agglomerations of varying sizes of blobs or ganglia when added to aqueous solutions. Our invention permits the measurement of such fluid-fluid interface areas for two immiscible fluids present in flow systems. The flow-based method involves the introduction of an interfacial tracer chemical into the flow system in one of two modes: (1) step input, and (2) pulse input. The tracer is soluble to appreciable extent in only one of the two fluids, designated as the carrier fluid phase.