Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with one of the principal components, selecting features of the training set most closely correlated with the principal components, performing a regression analysis on the selected features to determine a subset of the selected features that are most closely correlated with a model target, training a machine learning model with the subset, verifying the trained machine learning model with a verification set, and saving the verified trained machine learning model as the intervention model. The method includes determining an intervention expectation indicating a likelihood that the user will take action in response to an intervention being execute, determining a likelihood of a gap in care for the user, selecting and scheduling an intervention for execution based on the care gap likelihood and the intervention expectation.

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with a principal component, selecting features of the training set most highly correlated with principal components, training a machine learning model with at least some of the selected features, and saving the verified trained machine learning model as the intervention model. The method includes determining multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the user will take action in response to an intervention being executed using the engagement channel corresponding to the channel-specific intervention expectation. The method includes selecting an intervention and scheduling the selected intervention for execution.

Abstract: A method includes receiving a prescription benefit request and identifying one or more covered alternative drugs. The method also includes identifying one or more additional alternative drugs based on at least one of the requested drug and determining whether the one or more additional alternative drugs includes at least one covered additional alternative drug. The method also includes determining, for each of the at least one covered additional alternative drug and the one or more covered alternative drugs, an alternative drug value. The method also includes identifying the at least one covered additional alternative drug and the one or more covered alternative drugs having a lowest alternative drug value and generating a request response indicating at least the alternative drug of the at least one covered additional alternative drug and the one or more covered alternative drugs having the lowest alternative drug value.

Abstract: A method includes generating an intervention model by determining principal components for features of a training set, associating each feature of the training set with a principal component, selecting features of the training set most highly correlated with principal components, training a machine learning model with at least some of the selected features, and saving the verified trained machine learning model as the intervention model. The method includes determining multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the user will take action in response to an intervention being executed using the engagement channel corresponding to the channel-specific intervention expectation. The method includes selecting an intervention and scheduling the selected intervention for execution.

Abstract: A computerized method includes receiving, from a predictive model, a personalization score representing a likelihood that a user is receptive to multiple communication protocols. The method includes selecting a set of communication protocols based on the personalization score. The method includes generating a compliance plan, for addressing a non-compliance failure, including a hierarchy of communication protocols and a set of rule-based conditions. The method includes automatically deploying the compliance plan by generating a first compliance message with a first communication protocol that corresponds to a first level of the hierarchy associated with the compliance plan. The method includes, in response to determining that the non-compliance failure persists for a threshold period of time, generating a second compliance message with a second communication protocol that corresponds to a second level of the hierarchy. The second level demands greater communication resources than the first level.

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: 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: Systems and methods in this document describe a mental health management system. The mental health management system accesses patient data associated with a patient from a database, determines that the patient is associated with a trigger event, generates a prediction, using a predictive modeling system trained to analyze the patient data, the prediction corresponding to a probability that the patient's current medication data will be modified, stores the prediction in association with the patient data, and transmits the prediction to a computing device.

Abstract: Systems and methods herein describe a mental health management system. The mental health management system accesses patient data associated with a patient from a database, determines that the patient is associated with a trigger event, identifies a category associated with the trigger event, generates a notification based on the trigger event and the identified category, validates the notification based on a notification history and transmits the notification to a computing device.

Abstract: A method includes obtaining source data containing feedback information, identifying different phrases of interest in the feedback information, generating count data that indicates how often the different phrases of interest appear in the feedback information, determining counts of how often the phrases of interest appear in the feedback information, and modifying a count that is representative of how often at least one phrase of interest appears in the feedback data. The count is modified by reducing the count by a count of how often another, shorter phrase of interest also appears in the feedback data. The method also includes generating at least one interface respectively reflecting the count data for the different phrases of interest and the count that has been modified.

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.