Evernorth Strategic Development, Inc.

A computer-implemented method includes generating a candidate machine learning model. The candidate machine learning model is configured to generate probability scores for members. The method includes training the candidate machine learning model using an initial data set, testing the candidate machine learning model to generate performance metrics indicative of a bias value correlated to the candidate machine learning model, and determining whether the performance metrics are below a threshold. In response to determining that the performance metrics are below the threshold the method includes generating a proxy feature based on the bias value, adding the proxy feature to the candidate machine learning model, calculating weights based on the bias value, updating the initial data set using the weights to generate an updated data set, and retraining the candidate machine learning model using the updated data set.

Apparatuses, systems, and methods relate to technology to receive a user request from a user device, identify that a plurality of questions is available for presentation on the user device based on the user request and select a first question from the plurality of questions. The technology transmits the first question to the user device, receives a first user reply from the user device, where the first user reply is an answer to the first question and selects a second question from the plurality of questions based on the first user reply. The technology transmits the second question to the user device, receives a second user reply from the user device, where the second user reply is an answer to the second question, determines a user suggestion based on the first and second user replies, and transmits the user suggestion to the user device.

A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training a machine learning model to generate an entity expiration likelihood output, obtaining a set of multiple database entities, and processing, by the machine learning model, feature vector inputs associated with each database entry to generate an entity expiration likelihood output. The instructions include determining a subset of the database entities having the highest entity expiration likelihood outputs, and, for each database entity in the subset, determining output impact scores for parameters of the feature vector input associated with the database entity, generating a feature list based on the determined output impact scores, and automatically selecting an executable sequence according to the entity expiration likelihood output associated with the database entity. The feature list is specific to the database entity and includes one or more of the parameters having the highest output impact scores.

A method includes receiving at least one input data object corresponding to an individual and determining whether a field of the input data object includes data formatted according to an expected format. The method also includes, storing the data associated with the field of the input data object in the corresponding field of the plurality of fields of the intermediate data object and determining, for the corresponding field of the plurality of fields of the intermediate data object, a classification. The method also includes identifying an aggregated data object having a field having a classification corresponding to the classification of the corresponding of the plurality of fields of the intermediate data object. The method also includes updating a value associated with at least one field of the aggregated data object using the corresponding field of the plurality of fields of the intermediate data object.

A method performed by a system for processing healthcare transactions is disclosed herein. The method comprises: receiving, by the cloud computing platform and the mainframe platform, a request for coverage of a service received by a member under a health insurance plan; assessing, by the mainframe platform, coverage of the service under the health insurance plan; determining, by the cloud computing platform, a first and a second healthcare attribute associated with the service; receiving, by the mainframe platform, the first healthcare attribute and the second healthcare attribute; and generating, by the mainframe platform, a response to the request for coverage of the service under the health insurance plan indicating the value associated with the second healthcare attribute, wherein the response is generated in response to a value associated with the first healthcare attribute being greater than a value associated with the second healthcare attribute.

A system includes memory hardware configured to store instructions and processor hardware configured to execute the instructions. The instructions include generating an interactive graphical user interface including a first user interface element. The instructions include, in response to a user entering a text string into the first user interface element: generating a second user interface element, generating predictive elements based on the text string, and populating the second user interface element with the predictive elements. The instructions include, in response to the user selecting one of the predictive elements: adding the predictive element to a fourth user interface element, querying a database based on the predictive element, and populating a fifth user interface element with results of the query.

A computerized method includes receiving, from a user, a request to perform an operation on data stored in a shared data resource that stores physically integrated data from a plurality of tenants. The method includes determining whether the user is authorized to access the shared data resource based on a role attribute corresponding to the user. The method includes, in response to the user having access to the shared data resource storing the data, identifying user attributes associated with the user; determining, using the user attributes, a set of tenants whose data the user is authorized to access to perform the operation of the request; and generating, for the user, a view of the shared data resource. The view represents data stored in that shared data resource that corresponds to the set of tenants whose data the user is authorized to access to perform the operation of the request.

Technology identifies at least one object part of provided data from an object repository that corresponds to provided objects that were provided from the object repository, and extrapolates a first attribute associated with a condition based on the at least one object part. The technology receives a request for a first object associated with a first user and the condition. The technology determines whether to execute an automatic process to determine whether to permit the request based on the first attribute, when the automatic process is determined to be executed to determine whether to permit the request, automatically analyzes user data of the first user and an object attribute associated with the first object to determine whether to permit the request, and when the automatic process is determined not to be executed, assigning the request to a queue for a manual review to determine whether to permit the request.