Stage division and pattern discovery of complex patient care processes
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This paper studies the design of a clinical pathway that defines medical service activities within each stage of a patient care process. Much prior research has developed clinical process models that consider the trajectory of services occurring in a care process, by using data mining techniques on process execution logs. A novel approach that provides a more efficient way of clinical pathway design is introduced in this paper. Based on the strategy of TEI@I methodology, the proposed approach integrates statistical methods, optimization techniques and data mining. With the preprocessed data, a complex care process is subsequently divided into several medical stages, and then the patterns of each stage are identified, and thus a clinical pathway is developed. Finally, the proposed method is applied to the real world, using archival data derived from a hospital in Beijing, about three diseases that involve various departments, with an average of 300 samples for each disease. The results of realworld applications demonstrate that the proposed method can automatically and efficiently facilitate clinical pathways design. The main contributions to the field in this paper include (a) a new application of TEI@I methodology in healthcare domain, (b) a novel method for complex processes analysis, (c) tangible evidence of automatic clinical pathways design in the real world.
KeywordsClinical pathway frequent pattern mining stage division TEI@I virtual business
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