Optum360, LLC

A-life Medical, LLC

Optum, Inc.

Optum360, Llcq

A-life Hospital Coding, LLC

Mark L Morsch

Daniel T Heinze

Brian Potter

George Karres

Christopher Miller

Destinee Tormey

Peter Feller

Ronald Sheffer

Michelle Wieczorek

Michael A Landis

Blair C Jennings

Emily V Ho

Michael J DeTolla

Kelly Hogan

Troy C Lacher

Paul C Van Fossan

Chad L Hilton

David J Falk

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.

Auditing the coding and abstracting of documents

Embodiments of the present disclosure provide for improved determination of complexities associated with event data objects. Such complexity determinations may be identified based at least in part on complexity score(s) generated from any number of augmented event codes. Embodiments identify augmented event codes from any of a myriad of sources, including various service encodings and participant history profiles for particular event entities. The augmented event codes may be processed in a myriad of ways, utilizing the entirety and/or portions thereof, to further enhance the accuracy of the event complexity determined therefrom. Based at least in part on the accurately generated complexity determination(s), one or more prediction-based actions may be initiated that correspond to the particular complexity determination(s), for example such that only the correct prediction-based action is initiated based at least in part on such a determined event complexity.

Apparatuses, computer-implemented methods, and computer program products for accurate data code processing utilizing augmented data codes

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.

Ontologically driven procedure coding

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.

Mere-parsing with boundary and semantic driven scoping

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.

Methods, apparatuses, and systems for gradient detection of significant incidental disease indicators

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.

Visualizing the documentation and coding of surgical procedures

Computer-based, natural language processing systems and methods are provided for review of clinical documentation and other medical records, and for clinical documentation improvement. The systems and methods are configured to analyze received diagnoses and/or procedures in view of documents in the record using a natural language processor and a tiered information model to identify clinical indicators, and optionally markers. The identified information is compared with the received data for use in generating queries requesting evidence in support of the received diagnosis or procedure, or for use in validating the received information.

Automated clinical indicator recognition with natural language processing

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.

Multi-magnitudinal vectors with resolution based on source vector features

Computer-based, natural language processing systems and methods are provided for review of clinical documentation and other medical records, and for clinical documentation improvement. The systems and methods are configured to review documents in the record using a natural language processor and to identify clinical indicators with associated contextual information. The clinical indicators are compared to scenarios to generate markers based on an information model. The markers used to generate physician queries and other informational requests with supporting evidence for each query based on indicators identified in the record. In additional examples, pragmatic guidelines including business-based rules can also be utilized, either in combination with, or as part of, the scenarios in the information model.

Growing community of inventors

San Diego, CA, United States of America

Mark L Morsch