Abstract: Some aspect include a system for automatically processing text comprising information regarding a patient encounter to prioritize medical billing codes derived from the text. The system comprises at least one storage medium storing processor-executable instructions, and at least one processor configured to execute the processor-executable instructions to analyze the text to extract a plurality of facts from the text, assign a plurality of medical billing codes to the text based at least in part on the plurality of facts, using a model trained at least in part on feedback from a user, order the plurality of medical billing codes in a sequence beginning with a primary medical billing code corresponding to a primary diagnosis associated with the text, and present the ordered sequence of medical billing codes to the user for review.

Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.

Abstract: Some aspect include a system for automatically processing text comprising information regarding a patient encounter to prioritize medical billing codes derived from the text. The system comprises at least one storage medium storing processor-executable instructions, and at least one processor configured to execute the processor-executable instructions to analyze the text to extract a plurality of facts from the text, assign a plurality of medical billing codes to the text based at least in part on the plurality of facts, using a model trained at least in part on feedback from a user, order the plurality of medical billing codes in a sequence beginning with a primary medical billing code corresponding to a primary diagnosis associated with the text, and present the ordered sequence of medical billing codes to the user for review.

Abstract: A system and method for use in a medical coding system is provided. The method may include receiving, at a computing device, one or more electronic medical documents and assigning the one or more medical documents with a medical coder. The method may further include monitoring the performance of the medical coder with respect to the one or more medical documents and assigning a score to the medical coder based upon, at least in part, the monitoring. The method may also include providing the score and a second medical coder's score to the medical coder.

Abstract: Cascaded models may be applied to extract facts from a medical text. A first model may be applied to at least a portion of the medical text. The first model extracts at least one first medical fact. The at least one first medical fact is linked to at least first text in the at least a portion of the medical text. A second model may be applied to the first text. The second model extracts at least one second fact that is an attribute of the at least one first medical fact.