Abstract: A method for analyzing prior authorization documentation includes receiving a set of pages, determining a set of relevant pages, and determining a set of document types. The method includes orchestrating a first OCR process to produce a first set of digitized pages and a second OCR process to produce a second set of digitized pages. The method includes extracting a set of request data from the second set of digitized pages and determining a set of authorization criteria. The method includes determining a set of authorization data from the second set of digitized pages and determining whether the authorization data meets the authorization criteria. The method includes, in response to a determination that the authorization data meets the set of authorization criteria, automatically approving an authorization request and, in response to a determination that the set of authorization data does not meet the authorization criteria, automatically rejecting the authorization request.

Abstract: A computerized method for analyzing prior authorization documentation includes receiving a first set of information including a set of pages and determining a set of relevant pages. The method includes determining a set of document types associated with the set of relevant pages and performing optical character recognition. The method includes prompting a machine learning model to extract a set of request data. The method includes prompting the machine learning model with a set of prompts associated with a set of authorization criteria to determine a set of authorization data. The method includes, in response to a determination that the set of authorization data has met the set of authorization criteria, approving an authorization request. The method includes, in response to a determination that set of authorization data has not met the set of authorization criteria, rejecting the authorization request.

Abstract: A computer system includes memory hardware configured to store a transcription database, a call database, and instructions. The transcription database includes multiple call transcription data entries. The call database includes multiple agent call log data entries. The computer system executes instructions including joining the call transcription data entries with the agent call log data entries according to associated timestamps. The instructions include, for at least one of the set of joined call data entries, generating an input vector for an unsupervised machine learning model. The instructions include supplying the input vector to the model to assign an output topic classification of the model to the joined call data entry associated with the input vector, and supplying the input vector to at least one sub-topic model associated with the output topic classification to assign one or more sub-topic output classifications to the joined call data entry associated with the input vector.

Abstract: A method for adapting a user interface includes accessing a data package. The method includes determining whether execution of an action based on the data package requires secondary authorization. The method includes, in response to a determination that the execution requires secondary authorization, identifying a set of inquiries and determining whether user data includes information associated with the set of inquiries. The method includes, in response to a determination that the user data includes information associated with the set of inquiries, determining a set of responses. The method includes transmitting a request for secondary authorization including the set of responses and a first subset of the user data. The set of responses is based on the first subset of the user data. The method includes transforming the user interface to display the set of responses, the set of inquiries, and the first subset of the user data.

Abstract: A system includes memory hardware storing processor-executable instructions, a persona, and a data structure associated with the persona. Processor hardware executes the processor-executable instructions. The instructions include generating a graphical user interface and, in response to a first condition, inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric and transforming the graphical user interface according to the persona and the first metric. The instructions include, in response to a second condition, inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric and transforming the graphical user interface according to the persona and the second metric. The first trained machine learning model is different from the second trained machine learning model.

Abstract: A computer-implemented method includes defining model attributes including a training iteration value that defines a set of training iterations to be used in machine learning to associate portions of feedback data with a set of topic groups based on similarities in concepts conveyed in the feedback data. The method includes removing at least some of the confidential information from the feedback data. The method includes receiving a topic model number selection that indicates a subset of the set of topic groups. The method includes using machine learning to train a machine model based on the model attributes and the topic model number selection. The method includes generating a display showing at least one of a topic cluster graph or a word cloud based on the machine model.

Abstract: A computer-implemented method for generating one or more summarizations of a large volume of feedback data includes obtaining the feedback data. The feedback data is provided from disparate sources. The method includes separating the feedback data into a set of sentences, generating feedback embeddings of the set of sentences by providing the set of sentences to a set of machine models, providing topic input data to the set of machine models, computing sentence similarity of the feedback embeddings, calculating an importance score for each sentence of the set of sentences, ranking the set of sentences according to their respective importance scores, selecting one or more subsets of the set of sentences to generate the one or more summarizations, generating a visual representation of the one or more summarizations, and displaying the visual representation in an interactive user interface.

Abstract: A method includes storing a parameter related to a user, storing descriptive data for multiple identifiers, and indexing multiple events. Each event corresponds to a physical object supplied to a user on behalf of an entity. The method includes identifying a first set of identifiers based on commonality among the descriptive data. The method includes training a machine learning model for the first set of identifiers based on event data from within a predetermined epoch. The method includes receiving an indication of a selected identifier and determining a first intake metric of the selected identifiers using the machine learning model. The method includes determining a second intake metric of the selected identifier and the parameter and transforming the user interface according to the first and second intake metrics. The first intake metric represents an amount of resources expected to be received during a second epoch subsequent to the predetermined epoch.

Abstract: A content analysis system includes a processor executing instructions from memory. The instructions include, in response to receiving a request signal from a user device, obtaining feedback items, each having a source indicator; identifying unique source indicators; and, for each source indicator, aggregating corresponding ones of the feedback items. A set of filtered feedback items is generated according to either first or second access levels associated with a user of the user device. A subset of filtered feedback items is selected according to a date range specified by the request signal, a set of automated rules is applied, and natural language processing is performed based on frequency of presence of salient terms to identify themes. A control signal is transmitted to a user interface of the user device instructing display of a representation that indicates a change in the frequency of the identified themes over the specified date range.

Abstract: A computer system includes memory hardware configured to store a transcription database, a call database, and instructions. The transcription database includes multiple call transcription data entries. The call database includes multiple agent call log data entries. The computer system executes instructions including joining the call transcription data entries with the agent call log data entries according to associated timestamps. The instructions include, for at least one of the set of joined call data entries, generating an input vector for an unsupervised machine learning model. The instructions include supplying the input vector to the model to assign an output topic classification of the model to the joined call data entry associated with the input vector, and supplying the input vector to at least one sub-topic model associated with the output topic classification to assign one or more sub-topic output classifications to the joined call data entry associated with the input vector.

Abstract: A method includes receiving a first set of identifiers selected based on commonality among descriptive data corresponding to the identifiers of the first set. Each identifier corresponds to a user who has been supplied a physical object. The method includes identifying event data for the first set within a specified epoch. The method includes training a machine learning model for the first set using the identified event data. The machine learning model is trained using parallel processing of records from a storage structure storing the event data. The parallel processing includes assigning analysis of the event data of each of a subset of the first set to respective processor threads for parallel execution on processing hardware. The trained machine learning model is configured to receive a selected identifier and generate an output representing an amount of resources expected to be used by the selected identifier for a subsequent epoch.

Abstract: A computer-implemented method includes defining model attributes including a training iteration value that defines a set of training iterations to be used in machine learning to associate portions of feedback data with a set of topic groups based on similarities in concepts conveyed in the feedback data. The method includes removing at least some of the confidential information from the feedback data. The method includes receiving a topic model number selection that indicates a subset of the set of topic groups. The method includes using machine learning to train a machine model based on the model attributes and the topic model number selection. The method includes generating a display showing at least one of a topic cluster graph or a word cloud based on the machine model.

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 defining model attributes of a machine model that organizes feedback data into topic groups based on similarities in concepts in the feedback data. The model attributes include a topic model number that defines how many topic groups are to be created, a hyperparameter optimization alpha value, and/or a hyperparameter optimization beta value. The method also includes generating the machine model using the model attributes that are defined and the feedback data, and applying the machine model to the feedback data to divide different portions of the feedback data into the different topic groups based on contents of the feedback data, the topic model number, the hyperparameter optimization alpha value, and/or the hyperparameter optimization beta value.

Abstract: A system includes memory hardware storing processor-executable instructions, a persona, and a data structure associated with the persona. Processor hardware executes the processor-executable instructions. The instructions include generating a graphical user interface and, in response to a first condition, inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric and transforming the graphical user interface according to the persona and the first metric. The instructions include, in response to a second condition, inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric and transforming the graphical user interface according to the persona and the second metric. The first trained machine learning model is different from the second trained machine learning model.

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 method includes storing a parameter related to a user, storing descriptive data for multiple identifiers, and indexing multiple events. Each event corresponds to a physical object supplied to a user on behalf of an entity. The method includes identifying a first set of identifiers based on commonality among the descriptive data. The method includes training a machine learning model for the first set of identifiers based on event data from within a predetermined epoch. The method includes receiving an indication of a selected identifier and determining a first intake metric of the selected identifiers using the machine learning model. The method includes determining a second intake metric of the selected identifier and the parameter and transforming the user interface according to the first and second intake metrics. The first intake metric represents an amount of resources expected to be received during a second epoch subsequent to the predetermined epoch.

Abstract: A computer system for transforming a user interface according to data store mining includes a data store configured to store a parameter related to a user and index event data of a set of events. A data processing circuit is configured to identify a first set of identifiers and train a machine learning model based on event data by the data store. An interface circuit is configured to receive an indication of a selected identifier of the plurality of identifiers, determine a first intake metric of the selected identifier using the machine learning model, and a second intake metric of the selected identifier and the parameter using the machine learning model. The interface circuit is configured to transform the user interface according to the first intake metric and the second intake metric.

Abstract: A method includes receiving a first set of identifiers selected based on commonality among descriptive data corresponding to the identifiers of the first set. Each identifier corresponds to a user who has been supplied a physical object. The method includes identifying event data for the first set within a specified epoch. The method includes training a machine learning model for the first set using the identified event data. The machine learning model is trained using parallel processing of records from a storage structure storing the event data. The parallel processing includes assigning analysis of the event data of each of a subset of the first set to respective processor threads for parallel execution on processing hardware. The trained machine learning model is configured to receive a selected identifier and generate an output representing an amount of resources expected to be used by the selected identifier for a subsequent epoch.

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.