Abstract: The current document is directed to methods and automated systems that assign individual medical codes selected from one or more medical codebooks to electronic medical records. In certain implementations, the currently disclosed automated systems generate multiple streams of medical terms or medical terms and phrases from an electronic medical record as well as multiple streams of medical terms or medical terms and phrases from individual medical codes contained within a medical codebook and then use stream-comparison functionality to select those individual medical codes of the medical codebook most likely to be relevant and related to the information encoded within the electronic medical record.

Abstract: The current document is directed to methods and systems for automated generation of evaluation and management medical codes (“E/M codes”). In one implementation, a series of processes are applied to a medical document in order to generate annotations and concepts, extract metadata, and, using the annotations and concepts, and, in certain cases, the extracted metadata, to generate a set of feature/feature-value pairs that parametrically represent the contents of the medical document. Models for E/M codes and E/M-code components are generated to contain sets of weights, each weight corresponding to a feature for which a feature-value is automatically generated from medical documents. These weights are used as multipliers, in certain implementations, of the feature values generated for a medical document. Multiplication of feature values by corresponding weights produces terms that are used to generate scores for each of various different E/M codes.

Abstract: This disclosure is directed to methods and automated documentation and medical-coding systems that combine predictions of clinical decision support or multiple medical-code assignments into a final medical-code assignment, such that the combination is different for different contexts. In certain implementations, each agent receives the same set of terms and phrases extracted from an electronic medical record (“EMR”). Based on the context of the EMR, each agent extracts medical codes from one or more medical codebooks, compares the terms and phrases to the medical codes, and assigns a code to the EMR based on a confidence score. The multiple code assignments are combined to generate a final medical-code assignment based on the confidence scores, context, and each agent's historical performance within the context. The automated system stores and outputs the final medical-code assignment.