title: Evaluating measures of redundancy in clinical texts.
abstract: Although information redundancy has been reported as an important problem for clinicians when using electronic health records and clinical reports, measuring redundancy in clinical text has not been extensively investigated. We evaluated several automated techniques to quantify the redundancy in clinical documents using an expert-derived reference standard consisting of outpatient clinical documents. The technique that resulted in the best correlation (82%) with human ratings consisted a modified dynamic programming alignment algorithm over a sliding window augmented with a) lexical normalization and b) stopword removal. When this method was applied to the overall outpatient record, we found that overall information redundancy in clinical notes increased over time and that mean document redundancy scores for individual patient documents appear to have cyclical patterns corresponding to clinical events. These results show that outpatient documents have large amounts of redundant information and that development of effective redundancy measures warrants additional investigation.
authors.affiliation: Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
authors.forename: Serguei
authors.initials: S
authors.lastname: Pakhomov
authors.forename: Bridget T
authors.initials: BT
authors.lastname: McInnes
authors.forename: Genevieve B
authors.initials: GB
authors.lastname: Melton
mesh_terms: D000465:Algorithms; D001330:Electronic Data Processing; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval; D010363:Pattern Recognition, Automated