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 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 are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: There is a need for more effective and efficient predictive anomaly detection. This need can be addressed by, for example, solutions for performing anomaly detection using an anomaly detection machine learning model. In one example, a method includes: identifying a plurality of event records; for each event record of the plurality of event records, determining a temporally-related event code data object based at least in part on a temporally-related subset of the one or more event codes that is associated with the event record; generating one or more event record profiles based on each temporally-related event code data object; processing the one or more event record profiles using an anomaly detection machine learning model to generate one or more anomaly detection predictions; and performing one or more prediction-based actions based at least in part on the one or more anomaly detections.