Maintenance and Discovery of Domain Knowledge for Nursing Care using Data in Hospital Information System

• Autorzy: Iwata H. , Hirano S. , Tsumoto S.

Identyfikatory

DOI

10.3233/FI-2015-1177

• Języki publikacji: EN

Abstrakty

EN

[Introduction] Schedule management of hospitalization is important to maintain or improve the quality of medical care and application of a clinical pathway is one of the important solutions for the management. Although several kinds of deductive methods for construction for a clinical pathway have been proposed, the customization is one of the important problems. This research proposed an inductive approach to support the customization of existing clinical pathways by using data on nursing actions stored in a hospital information system. [Method] The number of each nursing action applied to a given disease during the hospitalization was counted for each day as a temporal sequence. Temporal sequences were compared by using clustering and multidimensional scaling method in order to visualize the similarities between temporal patterns of clinical actions. [Results] Clustering and multidimensional scaling analysis classified these orders to one group necessary for the treatment for this DPC and the other specific to the status of a patient. The method was evaluated on data sets of ten frequent diseases extracted from hospital information system in Shimane University Hospital. Cataracta and Glaucoma were selected. Removing routine and poorly documented nursing actions, 46 items were selected for analysis. [Discussion] Counting data on executed nursing orders were analyzed as temporal sequences by using similarity-based analysis methods. The analysis classified the nursing actions into the two major groups: one consisted of orders necessary for the treatment and the other consisted of orders dependent on the status of admitted patients, including complicated diseases, such as DM or heart diseases. The method enabled us to inductive construction of standardized schedule management and detection of the conditions of patients difficult to apply the existing or induced clinical pathway.

Słowa kluczowe

EN

clinical pathway; hospital information system; clustering; MDS; feature selection; multidimensional scaling

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2015
• Tom: Vol. 137, nr 2
• Strony: 237--252
• Opis fizyczny: Bibliogr. 13 poz., tab., wykr.

Twórcy

autor

Iwata H.

• Department of Medical Informatics Faculty of Medicine, Shimane University 89-1 Enya-cho Izumo 693-8501, Japan

autor

Hirano S.

• Department of Medical Informatics Faculty of Medicine, Shimane University 89-1 Enya-cho Izumo 693-8501, Japan

autor

Tsumoto S.

• Department of Medical Informatics Faculty of Medicine, Shimane University 89-1 Enya-cho Izumo 693-8501, Japan

Bibliografia

• [1] Bichindaritz, I.: Memoire: A framework for semantic interoperability of case-based reasoning systems in biology and medicine, Artif Intell Med, 36(2), 2006, 177–192.
• [2] Cox, T., Cox, M.: Multidimensional Scaling, 2nd edition, Chapman & Hall/CRC, Boca Raton, 2000.
• [3] Everitt, B. S., Landau, S., Leese, M., Stahl, D.: Cluster Analysis, 5th edition,Wiley, 2011.
• [4] Hanada, E., Tsumoto, S., Kobayashi, S.: A hUbiquitous Environmenth through Wireless Voice/Data Communication and a Fully Computerized Hospital Information System in a University Hospital, in: E-Health (H. Takeda, Ed.), vol. 335 of IFIP Advances in Information and Communication Technology, Springer Boston, 2010, 160–168.
• [5] Hyde, E., Murphy, B.: Computerized clinical pathways (care plans): piloting a strategy to enhance quality patient care., Clin Nurse Spec, 26(4), 2012, 277–282.
• [6] Iakovidis, D., Smailis, C.: A semantic model for multimodal data mining in healthcare information systems, Stud Health Technol Inform, 180, 2012, 574–578.
• [7] Quinlan, J.: Induction of decision trees, Machine Learning, 1, 1986.
• [8] Shortliffe, E., Cimino, J., Eds.: Biomedical Informatics: Computer Applications in Health Care and Biomedicine, 3rd edition, Springer, 2006.
• [9] Tsumoto, S., Hirano, S.: Risk Mining in Medicine: Application of Data Mining to Medical Risk Management, Fundam. Inform., 98(1), 2010, 107–121.
• [10] Tsumoto, S., Hirano, S., Tsumoto, Y.: Clustering-based Analysis in Hospital Information Systems, Proceedings of GrC2011, IEEE Computer Society, 2010.
• [11] Tsumoto, Y., Tsumoto, S.: 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, The Review of Socionetwork Strategies, 4(2), 2010, 47–63.
• [12] Tsumoto, Y., Tsumoto, S.: Correlation and Regression Analysis for Characterization of University Hospital (submitted), The Review of Socionetwork Strategies, 5(2), 2011, 43–55.
• [13] Ward, M., Vartak, S., Schwichtenberg, T., Wakefield, D.: Nurses’ perceptions of how clinical information system implementation affects workflow and patient care, Comput Inform Nurs, 29(9), 2011, 502–511