A context-sensitive methodology for automatic episode creation

Episode creation, the task of classifying medical events and related clinical data to a high-level concept, such as a disease, illness or care, has been primarily an interest of healthcare payers for purposes of cost outcomes analysis. Traditional challenges in episode creation have included: inconsistencies in defining episodes; lack of sufficient information to infer episodes; and differences in methods for diagnosing and resolving episodes. However, with the advent of the electronic medical record, which contains multiple sources of patient-related information, data is now accessible to construct more accurate and refined episodes. This work presents a context-sensitive episode creation methodology that utilizes features extracted from different medical repositories (e.g., claims records, structured medical reports) to associate the data with their respective motivating episodes. The combinatorial approach used to find the optimal clustering of patient-related data into episode groups and the measure used to evaluate candidate episode sets are described.