Data-Oriented Maintenance of Schedule Management of Nursing Care



This chapter proposes a temporal data mining method to maintain a clinical pathway used for schedule management of clinical care. The method consists of the following four steps: first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering and multidimensional scaling method. Third, by using the information on groups, feature selection is applied to the data and important features for classification are extracted. Finally, original temporal data are split into several groups and the first step will be repeated. After the grouping results are stable, a new pathway is constructed based on the induced results. The method was applied to a dataset whose patients had an operation of cataracts. The results show that the reuse of stored data will give a powerful tool for maintenance of clinical pathway, which can be viewed as data-oriented management of nursing schedule.


Clinical pathway Clustering Hospital information system Temporal data mining 



This research is supported by Grant-in-Aid for Scientific Research (B) 24300058 from Japan Society for the Promotion of Science(JSPS).


  1. 1.
    Bichindaritz I (2006) Memoire: a framework for semantic interoperability of case-based reasoning systems in biology and medicine. Artif Intell Med 36(2):177–192CrossRefGoogle Scholar
  2. 2.
    Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis, 5th edn. Wiley, ChichesterCrossRefMATHGoogle Scholar
  3. 3.
    Hanada E, Tsumoto S, Kobayashi S (2010) A ubiquitous environment, Ah through wireless voice/data communication and a fully computerized hospital information system in a university hospital. In: Takeda H (ed) E-health, vol 335, IFIP advances in information and communication technology. Springer, Boston, pp 160–168CrossRefGoogle Scholar
  4. 4.
    Hyde E, Murphy B (2012) Computerized clinical pathways (care plans): piloting a strategy to enhance quality patient care. Clin Nurse Spec 26(4):277–282CrossRefGoogle Scholar
  5. 5.
    Iakovidis D, Smailis C (2012) A semantic model for multimodal data mining in healthcare information systems. Stud Health Tech Inform 180:574–578Google Scholar
  6. 6.
    Quinlan J (1986) Induction of decision trees. Mach Learn 1:81–106Google Scholar
  7. 7.
    Shortliffe E, Cimino J (eds) (2006) Biomedical informatics: computer applications in health care and biomedicine, 3rd edn. Springer, New YorkGoogle Scholar
  8. 8.
    Tsumoto S, Hirano S (2010) Risk mining in medicine: application of data mining to medical risk management. Fundam Inform 98(1):107–121MathSciNetGoogle Scholar
  9. 9.
    Tsumoto S, Hirano S, Tsumoto Y (2010) Clustering-based analysis in hospital information systems. In: Proceedings of GrC2011. IEEE Computer Society, WashingtonGoogle Scholar
  10. 10.
    Tsumoto Y, Tsumoto S (2010) Exploratory univariate analysis on the characterization of a university hospital: a preliminary step to data-mining-based hospital management using an exploratory univariate analysis of a university hospital. Rev Socionetwork Strat 4(2):47–63CrossRefGoogle Scholar
  11. 11.
    Tsumoto Y, Tsumoto S (2011) Correlation and regression analysis for characterization of university hospital. Rev Socionetwork Strat 5(2):43–55CrossRefGoogle Scholar
  12. 12.
    Tsumoto S, Iwata H, Hirano S, Tsumoto Y (2014) Similarity-based behavior and process mining of medical practices. Future Generat Comput Syst 33:21–31Google Scholar
  13. 13.
    Ward M, Vartak S, Schwichtenberg T, Wakefield D (2011) Nurses’ perceptions of how clinical information system implementation affects workflow and patient care. Comput Inform Nurs 29(9):502–511CrossRefGoogle Scholar

Copyright information

© Springer Japan 2014

Authors and Affiliations

  1. 1.Department of Medical Informatics, Faculty of MedicineShimane UniversityIzumoJapan
  2. 2.Division of NursingShimane University HospitalIzumoJapan

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