Abstract: The present invention relates to a workflow for chart review and audit of natural language medical text. More specifically, the present invention uses Knowledge Graph to find the ICD-10-CM Codes and MEAT (Monitor, Evaluate, Assess and Treat) evidence for a given diagnosis. The present workflow also provided for feedback collection and consolidation. The present system, instead of processing OCR for all pages, pre-processes to identify the pages that need OCR (i.e., which are the pages containing non-machine readable text—either in full page or partial page—are determined) and passed to the OCR engine. This approach reduces the time, cost and errors associated with the OCR process.

Abstract: Systems and methods for a risk adjustment solutions workflow for projecting a risk adjustment factor (RAF) are disclosed. The workflow comprises retrospective and prospective workflows which are stackable and modular in nature functioning as a standalone workflow. The workflow is provided with structured or unstructured data in the form of patient medical charts and the same is processed through an NLP module. The NLP module receives inputs from a knowledge graph to add a clinical context and relations with other entities found. An Application recommendation module suggests the output with a plurality of ICD-10-CM codes, description, diagnosis evidence, MEAT evidence, Cross-walk to HCC codes, and RAF score of HCC codes. The ICD-10-CM codes are compared with the claimed codes to determine a condition of proper claiming, under-claiming or over-claiming.