Abstract: Methods, systems and computer program products for implementing a mere-parser are disclosed. Text data is processed to generate one or more parse items. A boundary based attribute associated with one of the parse items is identified, and the identified mere attribute is associated with one or more of the remaining parse items that is not blocked from associated with the boundary based attribute.

Abstract: Computer program products, methods, systems, apparatus, and computing entities are described for identifying significant incidental findings from medical records. In one example embodiment, an example computing device receives a medical report and derives a textual component from the medical report. The computing device then identifies one or more medical findings from the textual component and determines a clinical context for each of the one or more medical findings. The computing device then identifies one or more clinical cues from the one or more medical findings and generates one or more condition signals from the one or more clinical cues. The computing device then generates a condition alert from the one or more condition signals. The condition alert is indicative of a significant incidental finding. Using various embodiments contemplated herein, significant incidental findings can be identified for follow-up by a user.

Abstract: Among other things, methods, systems and computer program products for providing visual indication of documentation and coding of medical procedures may include providing a choice of medical codes associated with a medical procedure. A user selection of one of the medical codes is detected. Based on the detection, a visual indication of the user selection is generated on one or more anatomical diagrams.

Abstract: Computer implemented systems and methods of processing clinical documentation for a multi-axial coding scheme include inputting clinical documentation from memory operatively coupled with a computer system, and executing a natural language processor configured to process narrative text in the clinical documentation. The processor segments the narrative text based on boundaries defined in the clinical documentation, sequences words in the narrative text based on the segmentation, and maps the sequenced words to semantic objects in an ontology database. The ontology defines classes of semantic objects and relationships between them, corresponding to the multi-axial coding scheme. The semantic objects are converted into characters and output into slots in a medical code, with the characters positioned in the slots based on the multi-axial coding scheme.

Abstract: Methods, systems and computer program products for resolving multiple magnitudes assigned to a target vector are disclosed. A target vector that includes one or more target vector dimensions is received. One of the target vector dimensions is processed to determine a total number of magnitudes assigned to the processed target vector dimension. Also, a source vector that includes one or more source vector dimensions is received. The received source vector is processed to determine a total number of features associated with the source vector. When it is detected that the total number of magnitudes assigned to the processed target vector dimension exceeds one, one of the assigned magnitudes is selected based on one of the determined features associated with the source vector.

Abstract: Computer implemented systems and methods of processing clinical documentation for a multi-axial coding scheme include inputting clinical documentation from memory operatively coupled with a computer system, and executing a natural language processor configured to process narrative text in the clinical documentation. The processor segments the narrative text based on boundaries defined in the clinical documentation, sequences words in the narrative text based on the segmentation, and maps the sequenced words to semantic objects in an ontology database. The ontology defines classes of semantic objects and relationships between them, corresponding to the multi-axial coding scheme. The semantic objects are converted into characters and output into slots in a medical code, with the characters positioned in the slots based on the multi-axial coding scheme.

Abstract: Computer program products, methods, systems, apparatus, and computing entities are described for identifying significant incidental findings from medical records. In one example embodiment, an example computing device receives a medical report and derives a textual component from the medical report. The computing device then identifies one or more medical findings from the textual component and determines a clinical context for each of the one or more medical findings. The computing device then identifies one or more clinical cues from the one or more medical findings and generates one or more condition signals from the one or more clinical cues. The computing device then generates a condition alert from the one or more condition signals. The condition alert is indicative of a significant incidental finding. Using various embodiments contemplated herein, significant incidental findings can be identified for follow-up by a user.

Abstract: Methods, systems and computer program products for resolving multiple magnitudes assigned to a target vector are disclosed. A target vector that includes one or more target vector dimensions is received. One of the target vector dimensions is processed to determine a total number of magnitudes assigned to the processed target vector dimension. Also, a source vector that includes one or more source vector dimensions is received. The received source vector is processed to determine a total number of features associated with the source vector. When it is detected that the total number of magnitudes assigned to the processed target vector dimension exceeds one, one of the assigned magnitudes is selected based on one of the determined features associated with the source vector.

Abstract: There is a need for more effectively and efficiently performing predictive data analysis to determine associations between input data objects and predictive categories This need can be addressed by, for example, solutions for performing predictive data analysis to determine associations between input data objects and predictive categories that utilize at least one of accuracy scores for predictive categories, evidentiary scores for predictive categories, and predicted certification statuses for claim data objects. In one example, a method includes: for each predictive category of one or more predictive categories associated with a claim data object, determining an accuracy score and an evidentiary score; determining a predicted certification status for the claim data object based on each accuracy score for a predictive category and each evidentiary score for a predictive category; and performing prediction-based actions based on each predicted certification status.

Abstract: Computer implemented systems and methods of processing clinical documentation for a multi-axial coding scheme include inputting clinical documentation from memory operatively coupled with a computer system, and executing a natural language processor configured to process narrative text in the clinical documentation. The processor segments the narrative text based on boundaries defined in the clinical documentation, sequences words in the narrative text based on the segmentation, and maps the sequenced words to semantic objects in an ontology database. The ontology defines classes of semantic objects and relationships between them, corresponding to the multi-axial coding scheme. The semantic objects are converted into characters and output into slots in a medical code, with the characters positioned in the slots based on the multi-axial coding scheme.

Abstract: Computer program products, methods, systems, apparatus, and computing entities are described for identifying significant incidental findings from medical records. In one example embodiment, an example computing device receives a medical report and derives a textual component from the medical report. The computing device then identifies one or more medical findings from the textual component and determines a clinical context for each of the one or more medical findings. The computing device then identifies one or more clinical cues from the one or more medical findings and generates one or more condition signals from the one or more clinical cues. The computing device then generates a condition alert from the one or more condition signals. The condition alert is indicative of a significant incidental finding. Using various embodiments contemplated herein, significant incidental findings can be identified for follow-up by a user.

Abstract: Techniques for implementing Quality Assurance of the process of coding medical documents are disclosed. An audit of a coding process for a medical document is initiated by selecting and setting audit parameters. Using the selected parameters, a sample batch of coded documents is obtained from a universe of coded documents. The sample batch of coded documents is presented to auditor(s), and the auditor(s) provide corrections, which are recorded, and a score for each correction is calculated. A sample score, based on the corrections, is calculated in a manner that tracks to subjective auditor assessments of the process quality as being acceptable, marginally acceptable, or unacceptable, and which sample score accounts for the individual auditor subjectivity and an error.

Abstract: There is a need for solutions for more effective and efficient predictive data analysis systems in conceptually hierarchical domains. This need can be addressed, for example, by a system configured to obtain one or more initial raw inputs; determine a partial prediction for the one or more initial raw inputs, wherein the partial prediction is associated with an initial encoding hierarchy and the initial encoding hierarchy is associated with a plurality of prediction nodes; determine, based on the partial prediction and the initial encoding hierarchy, one or more partial prediction information deficiencies for partial prediction; obtain one or more supplemental raw inputs based on the one or more partial prediction information deficiencies; and generate a conceptually hierarchical prediction based on the one or more supplemental raw inputs and the partial prediction.

Abstract: Methods, systems and computer program products for implementing a mere-parser are disclosed. Text data is processed to generate one or more parse items. A boundary based attribute associated with one of the parse items is identified, and the identified mere attribute is associated with one or more of the remaining parse items that is not blocked from associated with the boundary based attribute.

Abstract: Methods, systems and computer program products for implementing a mere-parser are disclosed. Text data is processed to generate one or more parse items. A boundary based attribute associated with one of the parse items is identified, and the identified mere attribute is associated with one or more of the remaining parse items that is not blocked from associated with the boundary based attribute.

Abstract: Techniques for implementing Quality Assurance of the process of coding medical documents are disclosed. An audit of a coding process for a medical document is initiated by selecting and setting audit parameters. Using the selected parameters, a sample batch of coded documents is obtained from a universe of coded documents. The sample batch of coded documents is presented to auditor(s), and the auditor(s) provide corrections, which are recorded, and a score for each correction is calculated. A sample score, based on the corrections, is calculated in a manner that tracks to subjective auditor assessments of the process quality as being acceptable, marginally acceptable, or unacceptable, and which sample score accounts for the individual auditor subjectivity and an error.

Abstract: Computer implemented systems and methods of processing clinical documentation for a multi-axial coding scheme include inputting clinical documentation from memory operatively coupled with a computer system, and executing a natural language processor configured to process narrative text in the clinical documentation. The processor segments the narrative text based on boundaries defined in the clinical documentation, sequences words in the narrative text based on the segmentation, and maps the sequenced words to semantic objects in an ontology database. The ontology defines classes of semantic objects and relationships between them, corresponding to the multi-axial coding scheme. The semantic objects are converted into characters and output into slots in a medical code, with the characters positioned in the slots based on the multi-axial coding scheme.

Abstract: Methods, systems and computer program products for implementing a mere-parser are disclosed. Text data is processed to generate one or more parse items. A boundary based attribute associated with one of the parse items is identified, and the identified mere attribute is associated with one or more of the remaining parse items that is not blocked from associated with the boundary based attribute.

Abstract: Methods, systems and computer program products for implementing a mere-parser are disclosed. Text data is processed to generate one or more parse items. A boundary based attribute associated with one of the parse items is identified, and the identified mere attribute is associated with one or more of the remaining parse items that is not blocked from associated with the boundary based attribute.

Abstract: Techniques for implementing Quality Assurance of the process of coding medical documents are disclosed. An audit of a coding process for a medical document is initiated by selecting and setting audit parameters. Using the selected parameters, a sample batch of coded documents is obtained from a universe of coded documents. The sample batch of coded documents is presented to auditor(s), and the auditor(s) provide corrections, which are recorded, and a score for each correction is calculated. A sample score, based on the corrections, is calculated in a manner that tracks to subjective auditor assessments of the process quality as being acceptable, marginally acceptable, or unacceptable, and which sample score accounts for the individual auditor subjectivity and an error.