Abstract: Methods and systems for displaying information in augmented reality are described. In one embodiment, a system comprises a camera, a memory, a display, and a processor configured to (a) detect a first prescription drug in a field of view of a camera by detecting a glyph printed on a label on a container of the first prescription drug, (b) determine a first identifier associated with the first prescription drug using optical character recognition, (c) determine whether a user has access to additional data about the first prescription drug, (d) request the additional data about the first prescription drug from a database, and (e) display the additional data in augmented reality adjacent to the first prescription drug on the display such that the additional data moves when the field of view of the camera changes such that the additional data remains displayed adjacent to the first prescription drug.

Abstract: A method for providing mainframe codebase maintenance and security is performed by a mainframe deployment device. The method includes importing the mainframe codebase from a mainframe device. The mainframe codebase includes at least one code region including at least one code element. The method also includes identifying the code regions and initializing a branch repository corresponding to at least one identified code region. The method includes querying the imported mainframe codebase to identify, for the code elements, a user identifier and a source region. The method includes populating the branch repositories with the code elements based on the respective source region. The method includes applying a code quality scan to the populated branch repositories to identify a code quality issue in the respective code elements. The method also includes submitting at least one code element having an identified code quality issue to a user device correct the code quality issues.

Abstract: A method includes receiving a data processing request at a computing system. The data processing request identifies data to be compared to sets of criteria according to a predefined sequence of the sets that is defined by a non-variant logic process. The method also includes determining whether the request is to be processed according to a variant logic process that defines a modified sequence of the criteria sets than the non-variant logic process. The method also includes dynamically altering the predefined sequence of the criteria sets to the modified sequence responsive to determining that the request is to be processed using the variant logic process, comparing the data identified by the request with the criteria sets according to the modified sequence, and processing the data according to the criteria sets of criteria in the modified sequence.

Abstract: A system includes a processor and memory providing instructions for execution by the processor. The instructions include, responsive to a request from a user device indicating a group type and a time frame, selecting a set of users based on the group type. The instructions include retrieving the user data and historical data from a database and identifying a set of new users during the time frame. The instructions include adding the set of new users to the set of users and generating synthetic historical data for the set of users. The instructions include, based on the synthetic historical data for each user, computing a likelihood of discontinuation of a drugs and computing a likelihood of initiation of a drug. The instructions include estimating, for the group type, an aggregated output based on the likelihoods of initiation and discontinuation. The instructions include displaying the aggregated output on the user device.

Abstract: A pharmaceutical dispenser for dispensing a quantity of pharmaceuticals into a container includes a pharmaceutical counter to count and release the quantity of pharmaceuticals. A pharmaceutical outlet delivers the quantity of pharmaceuticals to the container. A pharmaceutical gate receives the quantity of pharmaceuticals from the pharmaceutical counter. The pharmaceutical gate includes a receiver sized and shaped to define a pharmaceutical receiving space to hold the quantity of pharmaceuticals. The receiver moves between a receiving position and a dispensing position. In the receiving position, the receiver receives the quantity of pharmaceuticals in the pharmaceutical receiving space from the pharmaceutical counter. In the dispensing position, the receiver dispenses the quantity of pharmaceuticals toward the pharmaceutical outlet.

Abstract: A method for generating predictions for year-over-year change in drug spending and per member per month spending and for providing the predictions via a web portal includes receiving data collected from a health plan. The data includes per member per month costs of the health plan and demographic information of members of the health plan. The method includes selecting therapeutic classes based on the per member per month costs and demographic information, segmenting the data by the therapeutic classes, and detecting patterns by analyzing the segmented data. The method includes generating models for the therapeutic classes based on the patterns, and generating predictions for year-over-year change in drug spending and per member per month spending for the therapeutic classes by utilizing the models. The method includes providing the predictions via a web portal in at least one of a displayable graphical format and a downloadable data structure.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified task based on transcribed caller utterance. Processor samples audio caller utterance to generate samples of audio caller utterance. Processor generates loudness result based on loudness values of samples using loudness neural network associated with identified task. Processor generates pitch result based on pitch values of samples using pitch neural network associated with identified task. Processor generates tone result for each word in transcribed caller utterance using tone neural network associated with identified task. Using task completion probability neural network associated with identified task, processor generates task completion probability result that is based on at least one of: loudness result, pitch result, or tone result. Other embodiments are disclosed herein.

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 pharmaceutical package for carrying a pharmaceutical container includes a first housing and a second housing in the first housing. The second housing defines a compartment sized and shaped to receive and carry the pharmaceutical container. A gas chamber is disposed between the first and second housings. The gas chamber is configured to hold a gas in an inflated state of the gas chamber. A passage extends from the first housing to the second housing and defines a passageway extending there-between to allow the pharmaceutical container to be positioned in the compartment of the second housing by moving the pharmaceutical container through the passageway. One or more supports are connected to and extending between the first housing and the second housing. The one or more supports secure and hold the second housing in the first housing.

Abstract: A medication dispensing system includes an automated dispensing device that includes cells with electronically activated locks. The device is configured to detect when medication counts in the cells fall below predetermined thresholds. The system further includes a plurality of first electronic devices that are associated with some of the cells and that have imagers. In response to any of the medication counts in the cells being below the predetermined threshold, the automated dispensing device is configured to automatically send a replenishment needed notification to the first electronic device associated with the cell. That first electronic device is configured to transmit a picture of a medication to a second electronic device. In response to a positive verification by a user of the second electronic device that the medication in the picture is the correct medication, the second electronic device is configured to transmit an unlock signal to the automated dispensing device.

Abstract: Methods and systems for an accessibility system are provided. The methods and systems include operations comprising: receiving a request for a markup language document; obtaining the markup language document; processing a first portion of the markup language document with a machine learning technique to generate a first dictation corresponding to the first portion of the markup language document, the machine learning technique being trained to establish a relationship between a plurality of training markup language documents and training dictations corresponding to the training markup language documents; and replacing the first portion of the markup language document with the first dictation.

Abstract: A computer system for improving a user interface (UI) is disclosed. The system includes a reinforcement module that obtains (i) a shortest path to a goal in the UI and (ii) user interaction experiments; a distance module that determines a reinforcement distance for each page of the UI based on the user interaction experiments; and a step analysis module that determines a count of times that user interaction experiments indicate a deviation from each step of the shortest path. The determination is based on the determined reinforcement distance for the page of the UI associated with the step. The reinforcement module is configured to, in response to determining that a step requires reinforcement, generate a reinforcement for the step. Generating the reinforcement includes at least one of generating a prompt for an action associated with the step and altering a UI element associated with the step.

Abstract: A method includes receiving a scheduling request that identifies a medical consumable, accessing first document types that indicate how often medical consumables are to be ingested, identifying a frequency at which the first designated medical consumable is to be consumed based on the first document types, accessing second document types that indicate a pattern of personal activity behavior, identifying one or more temporal opportunity windows based on the second document types, determining one or more matches between the frequency at which the first designated medical consumable is to be consumed and the one or more temporal opportunity windows, creating an adherence schedule based on the one or more matches that indicates when reminders to ingest the first designated medical consumable are to be generated, and generating one or more of the reminders to ingest the first designated medical consumable on a mobile electronic device according to the adherence schedule.

Abstract: A method includes generating an intervention model for a population of users based on contact data, demographic data, and engagement data indicating successfulness of prior interventions for each of the population of users. The method includes, obtaining first data related to a first user, including engagement data indicating successfulness of prior interventions with the first user. The method includes supplying the obtained data as input to the intervention model to determine an intervention expectation, which indicates a likelihood that the first user will take action in response to an intervention. The method includes determining a likelihood of a gap in care. The method includes, in response to the care gap likelihood exceeding a minimum threshold, selecting and scheduling execution of a first intervention. The first intervention is one of a real-time communication with the first user by a specialist and an automated transmission of a message to the first user.

Abstract: An order processing system, such as may be used in a high-volume pharmacy, may include a material handling system, an accumulator, and a packing device. The material handling system may move containers through a plurality of container paths. The container sorting device may receive the containers, scan the plurality of containers to detect container identifiers, and separate the containers based on the container identifiers to travel through a first container exit path or a second container exit path. The accumulator may accumulate containers that traveled through the first container exit path and the second container exit path. The accumulator may include an accumulator scanning device for scanning the containers to detect the container identifiers. The accumulator may group the containers together based on the detected container identifiers and orders, and provide the grouped containers. The packing device may package the grouped containers along with literature associated with the orders.

Abstract: A computer-implemented method includes generating an intervention model for a population of users based on engagement data indicating successfulness of prior interventions for each of the population of users. Each prior intervention corresponds to one of multiple engagement channels, and the intervention model includes multiple channel-specific models. The method includes supplying data related to a first user as input to the intervention model to determine multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the first user will take action in response to an intervention being executed using the corresponding engagement channel.