Abstract: Systems and methods for targeted medical document review are disclosed. A list of medical documents is received. Each medical document is associated with a user enrolled in a plan subject to a medical document review process. A dataset for each medical document, including clinical data, membership data, and provider data associated with the user, is received. A first model is used to determine whether each medical document includes an undocumented condition based on the dataset. The list is reduced to a subset of medical documents determined to include an undocumented condition. A second model is used to determine a risk score associated with each medical document of the subset based on the dataset. The subset of medical documents are ordered in the reduced list based on the risk scores. The ordered, reduced list is provided as input to the medical document review process.

Abstract: Systems and methods are disclosed for training a machine-learning model to predict and manage a condition of an entity. The method includes receiving historical data associated with a target entity from a plurality of data sources; deriving feature(s) from the historical data; determining a condition of the target entity by applying the feature(s) to a machine-learning model trained by: receiving a plurality of datasets associated with each entity of a plurality of entities; determining a specific condition associated with each entity based on the plurality of datasets; generating an identifier for each entity based on the determined specific condition; deriving training feature(s) for each entity from historical training data associated with the entity; and inputting the identifier and the training feature(s) for each entity to the machine-learning model to learn associations between the identifiers and the training feature(s) associated with the plurality of entities.

Abstract: Systems and methods of predicting regimen compliance and selecting electronic intervention plans are disclosed. A compliance event notification related to a regimen data structure is received and a next regimen compliance state for the regimen data structure is iteratively predicted for a prediction time period. The next regimen compliance state is predicted by a randomized prediction model including an integrated machine learning framework and the prediction time period is incremented by a predetermined increment during each iteration. When the next regimen compliance state comprises a compliant state, predicting at least one parameter of the next regimen compliance state by implementing a trained parameter prediction model comprising a tree-based machine learning framework. An intervention communication data structure is selected based on the next regimen compliance state and the regimen data structure is modified to reference the intervention communication data structure.

Abstract: A computer-implemented method for identifying a problem list section from an electronic document includes receiving, by one or more processors, the electronic document, generating, by the one or more processors and based on applying an optical character recognition algorithm to the electronic document, unstructured text, and identifying, by the one or more processors, one or more problem list words in the unstructured text, the one or more problem list words belonging in a dataset for identifying a presence of a problem list section. The method also includes associating, by the one or more processors, a portion of the unstructured text that corresponds to the one or more problem list words in the unstructured text with the problem list section and outputting, by the one or more processors, at least a portion of the problem list section.

Abstract: Systems and methods for targeted medical document review are disclosed. A list of medical documents is received. Each medical document is associated with a user enrolled in a plan subject to a medical document review process. A dataset for each medical document, including clinical data, membership data, and provider data associated with the user, is received. A first model is used to determine whether each medical document includes an undocumented condition based on the dataset. The list is reduced to a subset of medical documents determined to include an undocumented condition. A second model is used to determine a risk score associated with each medical document of the subset based on the dataset. The subset of medical documents are ordered in the reduced list based on the risk scores. The ordered, reduced list is provided as input to the medical document review process.

Abstract: A shifter mechanism for a motor vehicle includes a shifter lever, a park switch indicating when the shifter lever is out of a park position, and an electric actuator movable between a locking position and an unlocking position to lock and unlock the lever. An interlock switch indicates when the vehicle operator desires to move the lever. A controller moves the actuator when an ignition switch indicates an ignition is on, a brake switch indicates a brake pedal is pressed, and the interlock switch is actuated. The controller also moves the actuator when the lever is not in the park position and the interlock switch is actuated. The controller actuates a vehicle key lock actuator when the ignition switch indicates the vehicle ignition is on or when the park switch indicates that the lever is not in the park position.

Abstract: A shifter mechanism for a motor vehicle includes a shifter lever, a park switch indicating when the shifter lever is out of a park position, and an electric actuator movable between a locking position and an unlocking position to lock and unlock the lever. An interlock switch indicates when the vehicle operator desires to move the lever. A controller moves the actuator when an ignition switch indicates an ignition is on, a brake switch indicates a brake pedal is pressed, and the interlock switch is actuated. The controller also moves the actuator when the lever is not in the park position and the interlock switch is actuated. The controller actuates a vehicle key lock actuator when the ignition switch indicates the vehicle ignition is on or when the park switch indicates that the lever is not in the park position.