Abstract: A method for assessing diagnostic certainty in diagnostic reporting natural language, the method comprising receiving a natural language impression portion of a diagnostic report submitted for certainty evaluation, the impression portion having one or more sentences of natural language, accessing a pre-trained and fine-tuned language model, applying the one or more sentences to the trained language model for evaluation of the one or more sentences as a whole, receiving an assessment of certainty for the respective one or more sentences, based on the evaluation, communicating the assessment of certainty to a user before accepting the impression portion, and accepting submission of the impression portion only after the impression portion satisfies certainty criteria, or if the certainty criteria is not required obtaining validation from the user.

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: 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.