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Rule-based approach for detecting dirty data in discharge summaries

Hospital Information Systems (HIS) are responsible for the production, analysis and dissemination of different data in hospital facilities. As the availability of these data increases, it also heightens the problem of data quality. Indeed, despite the fact that several solutions are developed to deal with data quality, the dirty data persist. In this context, HIS would be provided by an accurate solution for dirty data detection and resolution. The present paper proposes a rule-based approach for both describing a subset's dirty data occurring in Discharge Data Summaries, called. Discharge Dirty Data Summaries (D3S) and assisting health practitioners in repairing it. Precisely, the proposed solution provides an automatic-way to deal with D3S. An empirical evaluation of the proposed approach with real clinical data provides preliminary evidence for the effectiveness of our proposal.