Japan Journal of Medical Informatics
Online ISSN : 2188-8469
Print ISSN : 0289-8055
ISSN-L : 0289-8055
Original Article
The Study of the Relationship Between Patient Status Indicators and Patient’s Care Time Using Multilevel Analysis
Atsue ISHIIYuko OHNOSatoko KASAHARAKatsumi HIRAKAWAYuko KITAMURAAkiko HAGIMOTOAki NAKAMURAKanako MURATAKiyonari INAMURAHajime HARAUCHIMorito MONDENMasato SAKONHarumi FUJIMOTOTeruyo MORITA
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2002 Volume 22 Issue 5 Pages 367-375

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Abstract

Background: Nursing activity should be evaluated from the viewpoint of the balance of “patient’s demand” and “nursing supply.” In this study, the relationship between nursing care time and the patient’s condition was investigated by Multilevel analysis.

Method: The patient’s condition is thought to reflect the “patient’s demand” and is classified into two groups, “human needs originated patient’s condition (HNOPC)” and “medical treatment originated patient’s condition (MTOPC).” Time-motion study data of nursing care were used as “nursing supply,” from an investigation conducted for 15 days in an acute-care and surgical ward of a university hospital in July 1999 and August 2000. At the same time, inpatient’s information on HNOPC and MTOPC were collected for all of the evaluated patients in the ward. Multilevel analysis was applied to examine which group of patient’s conditions would have more effect on patient’s care time. Furthermore, the number of “postoperative days” was employed as the predictor variable because it was an important factor that effected “patient’s demand” and “nursing supply” in the surgical ward. The model selection was done by Akaike’s Information Criteria (AIC).

Results and Discussions: The MTOPC group showed a stronger influence on patient’s care time than the HNOPC group. The most effective items were [impaired verbal communication] in HNOPC and [move-in] in MTOPC. The effect of “postoperative days” on the patient’s care time was not so large. The statistically significant coefficients of each model showed good agreement with clinical knowledge.

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© 2002 Japan Association for Medical Informatics
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