| 摘要 |
A probabilistic medical billing system and method using contextual data and inferential logic for use in screening accuracy of medical bill coding and for presenting results as probabilities or predictions of correctness. The probabilistic medical billing system and method is accomplished using the contextual information contained in a care givers' patient encounter notes, a set of rules and keywords, and an inferential, logic, engine based on Bayesian mathematics or similar disciplines. The inventive device includes an input device to capture care giver's encounter notes or other information, a lexical engine that extracts information while preserving the contextual order of the information, a relational database that contains keywords, phrases and rules and a statistical/probabilistic engine that uses Bayesian mathematics or similar disciplines to create the output. The lexical engine parses a document into words and is capable of extracting keywords or phrases as listed or defined in a master list. Further, the lexical engine would preserve the relative position of discovered keywords or phrases as the keywords or phrases and relative positions were encountered. The Bayesian engine is a mathematical algorithm that uses inferential logic to analyze historical data and shows the results as a predictive level as to the accuracy of a medical bill produced from the source documents. The inherent nature of Bayes like algorithms allows them to learn and improve their predictive capability through the use of a feedback system which is also part of the invention. Variations in algorithms and data flow can be easily made to support other predictive output related to billing or for the purposes of data mining and statistical evaluation.
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