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This paper applies topological data analysis (TDA) techniques to investigate the nature of complex high-dimensional data by extracting global shape information (pat-terns) and gaining novel insights from them. The objective is to characterize miscoding behaviors, identify reasons for miscoding behaviors, and select specific groups of subjects for which the health records are worth giving an additional review. Our method combines a TDA technique and an optimization-based model to provide a geometric representation of inter-related hospital stays while permitting the censoring of miscoded subjects by preferentially selecting subgroups with more coding errors. Through the proposed method, we successfully identified and validated four distinct subtypes of miscoding behaviors that traditional methodologies fail to find. Furthermore, with only 20% of the subjects reviewed, the proposed approach reduces coding errors by 64% of the whole population. Experimental results indicate that the proposed method is promising and can reduce coding errors efficiently, thereby eliminating the negative impacts caused by hospital miscoding. |
