Abstract: Methods and systems for prescription drug shipping selection are provided. In one embodiment, a shipping temperature for a prescription drug may be identified. A forecast temperature may be determined for a shipping origin and a forecast temperature may be determined for a shipping destination. One or more potential packing configurations capable of achieving the shipping temperature between the shipping origin and the shipping destination may be determined. A shipping cost for each of the one or more potential packing configurations may be calculated. Packaging materials and labor cost may additionally be considered. Other methods and systems are described.

Abstract: A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.

Abstract: A computerized search method includes receiving first input designating a first location of a first analytic. The method includes, in response to a scheduling event, obtaining a first document from the first location, identifying a first predefined label within the first document, obtaining first and second data associated with the first predefined label, storing the first datum into a value index as a current value of the first analytic, and storing the second datum into a text index as a textual description of the first analytic. The method includes presenting a search interface and, in response to receiving a search query from a user: identifying a set of result analytics relevant to the search query based on the text index and presenting, for each of the result analytics, a textual description of the analytic from the text index and a most recent value of the analytic from the value index.

Abstract: A container disassembly workstation for a pharmacy order processing system includes a base, a desktop connected to the base and defining a container disassembly workspace, and a pharmaceutical receptacle assembly extending through the desktop. The pharmaceutical receptacle assembly includes a pharmaceutical dust separation screen configured to retain pharmaceuticals and to permit pharmaceutical dust to pass through the pharmaceutical dust separation screen. The pharmaceutical receptacle assembly also includes a pharmaceutical dust receptacle configured to receive the pharmaceutical dust separation screen and pharmaceutical dust from the pharmaceuticals.

Abstract: A method starts with processor retrieving member's initial context data. Processor receives a string that is a transcribed utterance or an electronic message from the communication session established between member client device and agent client device. Processor determines potential relationships between the member and a patient that is the subject of the string by processing the string using Long Short-Term Memory (LSTM) neural networks to generate a plurality of relationship values. Relationship values are associated with relationship types. Processor generates weight values based on member's initial context data for each of the plurality of relationship types, and generates probability values for the relationship types based on the relationship values and the weight values. Processor narrows the potential relationships, generates relationship data that includes the narrowed potential relationships, and causes the relationship data to be displayed by the agent client device.

Abstract: Methods and systems for processing data inquiries are described. In one embodiment, data from a data source can be received, a data processing request indicating a request to perform a first function can be received, and a first identifier associated with a user can be determined based on the received data. A table storing a plurality of adjudication results including a plurality of field parameters may be accessed. The plurality of adjudication results in the table can be filtered by determining which of the plurality of adjudication results includes one of the plurality of field parameters having a value that matches or corresponds to the selected field value to create a first subset of filtered adjudication results from the plurality of adjudication results. The first subset of adjudication results can be transmitted when the processor determines that at least one adjudication result of the plurality of adjudication results is within the first subset. Additional methods and systems are disclosed.

Abstract: A method may include filling an order of a plurality of orders with a dosing filler system. The method may include receiving pharmaceutical orders including order for drugs used in multi-drug regimens using an order processing device. Each of the multi-drug regimen may have a plurality of scheduled dosing events. The method may include transporting containers to a dosing device, using the dosing device to dispense drugs for scheduled dosing events into the containers based on the received pharmaceutical orders, transporting the containers with the dispensed drugs to the container sealing device, using the container sealing device to seal the plurality of the containers with the dispensed drugs, transporting the dosage unit containers to the container identifier assembly, and using the container identifier assembly to identify the dosage unit containers based on the received pharmaceutical order.

Abstract: A computer system for testing a user interface (UI) includes a test creation module, a test execution module, a path entry module, and an analysis module. The test creation module obtains testing parameters including a UI location and goal criteria. The test execution module obtains a state of the UI based on the location, sets a current position to a predetermined location, executes tests according to the obtained state and the current position, stores test distance or test data results from completed UI tests, and determines a shortest path toward the goal in the UI based on the completed UI tests. The path entry module receives preferred path information and accordingly generates a preferred path toward the goal. The analysis module compares the determined shortest path to the generated preferred path and outputs an analysis result according to the comparison of the determined shortest path to the generated preferred path.

Abstract: A monitoring system for mapping and monitoring inter-application communications in a computing ecosystem is described. The monitoring system provides consolidated visibility to computing ecosystems by providing end-to-end mapping and monitoring of inter-application communications and events, changes, incidents, and status information of applications, services, and systems. As described, the monitoring system is configured to (a) identify communication paths linking the host devices, (b) generate an ecosystem map based on the communication paths, (c) transmit a monitoring signal to the network, (d) receive a monitoring response from the host devices in response to the monitoring signal including at least a first status, (e) process the monitoring response with the ecosystem map to generate an active ecosystem map, and (f) display the active ecosystem map including the host devices and at least one status associated with the host devices.

Abstract: A system includes a processor and memory. The memory stores a model database including models and a classification database including classification scores corresponding to an input. The memory stores instructions for execution by the processor. The instructions include, in response to receiving a first input from a user device of a user, determining, for the first input, classification scores for classifications by applying the models to the first input. Each model determines one of the classification scores. The instructions include storing the classification scores as associated with the first input in the classification database and identifying the first input as within a first classification in response to a first classification score corresponding to the first classification exceeding a first threshold. The instructions include transmitting, for display on an analyst device, the first input based on the first classification to a first analyst queue associated with the first classification.

Abstract: Method starts with processing, by processor, receiving audio signal of communication session between member-related client device and agent client device. Processor processes audio signal to generate caller utterances, each including audio caller utterance and transcribed caller utterance. Caller utterances includes first and second caller utterances. For each of the plurality of caller utterances, processor generates relationship data based on transcribed caller utterance, generates identified task based on transcribed caller utterance, and generates task completion probability result based on audio caller utterance, relationship data, and identified task, and stores the task completion probability result in a database. Processor computes agent action ranking score that is based on difference between task completion probability result of first caller utterance and task completion probability result of second caller utterance that precedes first caller utterance.

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 capturing, by an image-capturing device, a one or more images of at least a portion of a pharmacy workstation. The method also includes identifying, by a processor in communication with the image capturing device, objects of interest in a first image of the one or more images and classifying, by the processor, the detected objects of interest using a convolutional neural network associated with the processor. The method also includes identifying, by the processor, a boundary defining an opening of a container in a second image of the one or more images. The method also includes updating, by the processor, an objects in container list based on a determination that at least one of the classified objects passed the boundary.

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 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: Systems and methods for manually filling a prescription order. A manual fill device may be incorporated into a manual fill center of a pharmacy operated by one or more pharmacists and/or pharmacist technicians to manually fill certain prescription containers. The manual fill device may include a control unit which may operate at the direction of the order processing device. The manual fill device may also include a distribution section automating distribution of containers for manual fulfillment, and a manual section in which a pharmacist may utilize available pharmaceuticals to manually fill orders.

Abstract: A messaging module is configured to, in response to a first indicator indicating that a patient has opted into receiving text messages, selectively send and receives text messages to and from a mobile phone number of the patient. A refill by text (RBT) module is configured to, when a second indicator indicates that the patient is not included in an RBT program, trigger the messaging module to send a predetermined text message to the mobile phone number in response to a determination that: a name of a prescription drug prescribed to the patient is included in a stored list of prescription drugs associated with the RBT program; the first indicator indicates that the patient has opted into receiving text messages; and a number of refills of the prescription drug already provided to the patient without a change in the therapy of the prescription drug is greater than a predetermined number.

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.