title: A Novel Schema to Enhance Data Quality of Patient Safety Event Reports.
abstract: The most important knowledge in the field of patient safety is regarding the prevention and reduction of patient safety events (PSEs). It is believed that PSE reporting systems could be a good resource to share and to learn from previous cases. However, the success of such systems in healthcare is yet to be seen. One reason is that the qualities of most PSE reports are unsatisfactory due to the lack of knowledge output from reporting systems which makes reporters report halfheartedly. In this study, we designed a PSE similarity searching model based on semantic similarity measures, and proposed a novel schema of PSE reporting system which can effectively learn from previous experiences and timely inform the subsequent actions. This system will not only help promote the report qualities but also serve as a knowledge base and education tool to guide healthcare providers in terms of preventing the recurrence of PSEs.
authors.affiliation: School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Hong
authors.initials: H
authors.lastname: Kang
authors.affiliation: School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Gong
mesh_terms: D000068598:Data Accuracy; D066264:Datasets as Topic; D006282:Health Personnel; D006801:Humans; D007256:Information Systems; D061214:Patient Safety; D017751:Safety Management; D012660:Semantics