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 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: 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.

Abstract: A computerized method includes determining a clinical opportunity to improve care for a user according to automated triggering of a gap identification rule, generating a persona of the user based on one or more personalization scores that are specific to the user, and generating a care plan for reducing the gap in care based on the persona. The care plan includes a plurality of methods of increasing compliance of the user with the care plan, selected based on the one or more personalization scores, and include different modes of communicating with the user either directly or through at least one of a physician and a pharmacist depending on the one or more personalization scores. The method includes deploying the care plan to provide automated selection of one or more of the different modes of communicating with the user to increase compliance of the user with the care plan.

Abstract: A pharmaceutical order filling system uses a physical parameter that occurs during attaching cap to a pharmaceutical container to determine if the cap is properly engaged. The physical parameter may be torque on the cap when placed on the container. An order processing device receives a pharmaceutical order and sends the order to a dispensing device that fills the container with a pharmaceutical in the pharmaceutical order. A cap device is configured to apply the cap the container containing the pharmaceutical from the dispensing device, wherein the cap device is configured to sense the physical parameter, e.g., torque, to the cap when applying the cap to the container.

Abstract: A machine learning process for use with a pharmacy benefits management system. The machine learning process identifies a first predicted set of drug benefit claims impacted by a pricing error, reprices a sample of the first predicted set of drug benefit claims to adjust for the error, and trains a predictive model as a function of the repriced sample. Based on the trained model, the machine learning process predicts a second predicted set of drug benefit claims impacted by the error and initiates automatic repricing.

Abstract: A method for providing secure single sign on includes receiving a first data object from an application hosting server, the first data object indicating at least a service provider name and identifying a configuration file corresponding to the service provider name, wherein the configuration file includes at least trusted identity information. The method also includes determining, using the configuration file corresponding to the service provider name, whether the first data object is valid and, in response to a determination that the first data object is valid, generating a response message.

Abstract: A computer system for dynamic adaptation of a user interface according to data store mining includes a data store configured to index event data of a plurality of events. A data analyst device is configured to render the user interface to a data analyst and transmit a message that identifies a selected identifier of the plurality of identifiers. A data processing circuit is configured to train a machine learning model based on event data stored by the data store for a first set of identifiers from within a predetermined epoch. An interface circuit determines an interface metric for the selected identifier based on the determined output of the selected identifier and transmits the interface metric to the data analyst device. The data analyst device is configured to, in response to the interface metric from the interface circuit, selectively perform a modification or removal of a second user interface element.

Abstract: A method for controlling an autonomous unmanned aerial vehicle for delivery of a medication package includes determining a thermal control period for the medication package. The method also includes identifying a delivery location corresponding to the medication package. The method also includes identifying at least one environmental characteristic of an environment that includes a delivery three-dimensional flight path between a starting location and the delivery location, wherein the at least one environmental characteristic indicates an actual weather value at the delivery location. The method also includes determining whether to deliver the medication package based on the thermal control period and the at least one environmental characteristic, using the unmanned aerial vehicle.

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 delivery scheduling method includes receiving a bundle request from a user and identifying a first prescription scheduled for delivery within a date range. The method includes setting a scheduled delivery date of the first prescription as a target delivery date, identifying a set of prescriptions scheduled for delivery within a predetermined time of the target delivery date, and selecting one of the set of prescriptions as a candidate prescription. The method includes obtaining prescription parameters of the candidate prescription including a prior fill date of the candidate prescription and determining a required fill date of the candidate prescription. The method includes determining a blackout window for the candidate prescription based on a predetermined window following a prior fill date of the candidate prescription and, in response to the required fill date being after the blackout window, adjusting a delivery date of the candidate prescription to the target delivery date.

Abstract: Method starts with processing, by a processor, audio signal to generate audio caller utterance. Processor generates an agent action ranking score associated with the audio caller utterance and determines whether the agent action ranking score is below a minimum threshold. In response to determining that the agent action ranking score is below the minimum threshold, processor generates a transcribed caller utterance using a speech-to-text processor and generates an identified task based on the transcribed caller utterance. Using the transcribed caller utterance and a task-specific agent coaching neural network associated with the identified task, processor generates an ideal response. Processor generates a feedback result and causes the feedback result to be displayed on a display device of the agent client device. Other embodiments are disclosed herein.

Abstract: Methods and systems for selecting a machine learning algorithm are described. In one embodiment, one or more factors to be used by a plurality of machine learning algorithms in predicting a value of a required pharmacy element of a prescription are identified, each of the plurality of machine learning algorithms are trained to predict the value of the required pharmacy element using a first subset of previously received prescriptions, respective success rates for each of the plurality of machine learning algorithms at predicting respective known values of respective known required pharmacy elements for each of a second subset of the previously received prescriptions are determined, and a first of the plurality of machine learning algorithms having a highest success rate is selected to predict the value of the required pharmacy element of the prescription for a first predetermined period.