Abstract: A system for providing compartmented access to secure data assets includes a mobile device, a secure access platform, and a secure data storage platform. The mobile device may be configured to generate a user interface configured to allow a user to input credentials and a request for a secure data asset on the secure data storage platform. The mobile device may transmit the credentials and the request to a secure access platform. The secure access platform may transmit the credentials to the secure data storage platform. The secure data storage platform may transfer a copy of the secure data assets to a data access module on the secure data access platform. The secure access platform may be configured to create an appling instance, receive the copy of the secure data assets, and transmit a response package based on the copy of the secure data access to the mobile device.

Abstract: A method for publishing a data message to shared storage includes receiving an action targeting a user associated with a distributed network having a plurality of service providers. Each service provider includes a private storage system and has access to a shared storage system. The method also includes parsing data elements of the action into a first set of data elements and a second set of data elements. The method further includes classifying the first set of data elements into categories of a data taxonomy. The method additionally includes generating a message for the action where the message includes the first set of data elements. The method also includes publishing the message to the shared storage system using a first identifier indicating the user and a second identifier that indicates that a respective service provider associated with the action has authority to publish the message to the shared storage system.

Abstract: A computer system includes memory hardware and processor hardware configured to execute instructions, including training a machine learning model to generate a claim prediction output, obtaining structured claim data and demographic data specific to a patient entity, processing the structured data to generate the claim prediction output, storing the claim prediction output as a predicted future claim value specific to the patient entity, and assigning the patient entity to a first entity subset or to a second entity subset according to the structured demographic data specific to the patient entity.

Abstract: Methods and systems for performing operations comprising: receiving, by a server from a client device, a request to access a data object comprising one or more medical records, the request comprising authentication information; determining, by the server, that the authentication information is valid; in response to determining that the authentication information is valid, transferring, by the server, the data object to a temporary storage location; transmitting a first portion of the data object to the client device from the temporary storage location; and deleting the first portion of the data object from the temporary storage location after the first portion of the data object has been transmitted to the client device.

Abstract: A computer system includes processor hardware configured to execute instructions that include joining at least a portion of multiple call transcription data entries with at least a portion of multiple agent call log data entries according to timestamps associated with the entries to generate a set of joined call data entries, and validating the joined call data entry by determining whether a transcribed entity name matches with entity identifier information associated with the agent call log data entry. The instructions include preprocessing the joined call data entry according to word confidence score data entries associated with the call transcription data entry to generate preprocessed text, performing natural language processing vectorization on the preprocessed text to generate an input vector, and supplying the input vector to an unsupervised machine learning model to assign an output topic classification of the model to the joined call data entry associated with the input vector.

Abstract: A method includes, in response to a determination that one or more authenticated delivery locations includes a first delivery location, identifying, using an unmanned aerial vehicle registry, one or more unmanned aerial vehicles based on at least one unmanned aerial vehicle characteristic associated with a medication delivery request. The method also includes determining, for each unmanned aerial vehicle of the one or more unmanned aerial vehicles, an availability status, selecting an unmanned aerial vehicle based on at least a corresponding availability status and at least one selection criteria, and instructing the unmanned aerial vehicle to transport the medication package from a starting location to the first delivery location.

Abstract: A computerized method of transforming an interactive graphical user interface according to machine learning includes generating a selectable user interface element corresponding to a persona. In response to a user selecting the user interface element, data structures related to the persona are extracted from a first data store. The data structures are transformed into a set of input variables at a data processing module and loaded at a model execution module. The model execution module generates a first output variable based on the set of input variables. An analysis module generates a second output variable based on the first input variable. The graphical user interface displays a first message, a second message, or a third message in response to conditions being met for the second output variable.

Abstract: A system and method of generating a reuse metric starts with a processor receiving from a client device, a program comprising modules. Processor extracts the modules from the program and generates frequency tables associated with the modules. Frequency tables includes numbers of imports associated with the modules, and import ratios associated with the modules. Processor generates, based on the frequency tables, a histogram of the modules and the import ratios. Processor generates key statistical measures based on the import ratios and the histogram. Generating the key statistical measures comprises determining histogram weights, determining a Pareto fit of the histogram, and generating a reuse metric based on the Pareto fit and a Kurtosis of the histogram weights. Processor causes the client device to display the reuse metric and the histogram. Other embodiments are disclosed herein.

Abstract: A computerized method for transforming an interactive graphical user interface according to machine learning includes selecting a persona, loading a data structure associated with the selected persona, and generating the interactive graphical user interface. The method includes, in response to a user selecting a first selectable element, inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric, and transforming the user interface according to the selected persona and the first metric. The method includes, in response to the user selecting a second selectable element, inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric, and transforming the user interface according to the selected persona and the second metric. In various implementations, first metric is a first probability of the persona being approved for a first prior authorization prescription.

Abstract: A query processing server for providing scalable architectures in repricing is provided. The query processing server includes a gateway platform for processing communications and a repricing engine. The query processing server processor is configured to receive a repricing request from a frontend server. The query processing server processor is also configured to define a set of repricing periods. The query processing server processor is further configured to transmit a creation instruction to the repricing database server to create a repricing status database and queue. The query processing server processor is also configured to determine a set of claims data queries for execution based on the set of scenario data and to query the repricing database server with the set of claims data queries. The query processing server processor is configured to perform a repricing analysis using the set of claims data responses and the set of repricing periods.