隨著經濟快速地進步,台灣的醫療體系為了維持醫療環境品質、醫療照護品質以及增進護理人員的相關專業知識,除了護理人員的臨床經驗外,也於2009年的醫療繼續教育推廣協會開辦護理人員繼續教育資訊平台系統等多項醫護人員數位學習課程,藉此提升醫療服務品質及護理人員的專業知識。本研究以陳盈芳(2011)針對「應用科技接受模式探討護理人員數位學習之使用意願」的調查研究作為延伸探討的基礎,應用自我組織映射網路(Self Organozing Feature Map Network, SOM)與群聚適切性指標(Davies Bouldin Index, DB)將同質性較高的問卷受測者分為一群,再針對各集群以類神經網路(Artificial Neural Network, ANN)建構預測模式,並提供相關建議,以利醫療體系找尋適合自己的教育訓練系統與改善護理人員學習情況。本研究主要的研究成果如下: 1.本研究提供各模式以預測不同背景及思維的護理人員所呈現的學習情況。 2.針對不同情況的護理人員提供相對應的管理方針。
Along with the rapid growth of economic, the health care system of Taiwan has to maintain the medical environment, the quality of caring, and the enhancement of the expertise of nursing staff consequently. In addition to the clinical experiences of nursing staff, Taiwan Medical Enduring Education Institute launched continuous education information system to offer an E-learning platform for nursing staff in 2009 to improve the quality of medical service and nursing expertise. In this research, we extend the model of “The Study of Technology Acceptance Model for the Nurse’s Intention of Using E-learning” (Ying-fang Chen, 2011) to explore the best number of clusters between SOM and Davies Bouldin Index, and adopt Neural Network to construct forecasting model. The results of this research can provide relevant suggestions for clinical organization to find out a suitable education and training system , and improve the learning condition of nursing staff. The main findings of this study are: 1.This study provides a model to predict the learning conditions presented by the nurses of different backgrounds and thinking. 2.Provides corresponding management approaches to different nurses.