隨著雲端運算的興起，讓醫療資訊可結合網際網路與雲端科技，發展出一套健康資訊服務。尤 其，防疫雲不僅讓感染管制人員簡化通報的程序，並且以更有效率及成本效益的方式來提昇病患安 全。儘管有些專家學者強調雲端運算提供醫院競爭優勢的機會。但整體而言，醫院對於防疫雲的使 用偏低，並且可能面臨醫院或員工的抗拒。有鑑於此，需要瞭解醫院如何評估對防疫雲的採用。因 此，本研究基於兩階段採用觀點，針對防疫雲發展兩個整合模型，分別來解釋組織層級的採用 (首要 採用)及個人的採用(次要採用)。因此，本研究採實證研究的方式，以衛生福利部所公告之醫院評鑑 合格名單為樣本來源，第一年以資訊部門主管及資訊副院長為研究對象，以組織採用研究模型進行 問卷調查；第二年以感染管制人員為研究對象，以個人採用研究模型進行問卷調查，並以結構方程 模式進行資?分析來驗證研究模式變?間之因果關係，藉以評估與驗證此整合模式以及影響因素之 關係。因此，期望本研究之成果能作為中央衛生主管機關、醫院與醫療資訊產業對於健保雲端藥歷 系統實施策?及績效評估之?考，並增益學術界對於組織及個人採用防疫雲之相關研究。 The latest technological trends such as cloud computing provide a strong infrastructure and offer a true enabler for health information services over the Internet. Especially, epidemic prevention cloud allows infection control staff to streamline many of their reporting procedures and improve patient safety in a more efficient and cost-effective manner. Despite some practitioners and academics emphasized the opportunities for competitive advantage that cloud computing offers hospitals, their overall adoption remains low and meets resistance from hospitals or employee. There are gaps in our understanding of how hospitals evaluate change related to the epidemic prevention cloud by and decide to adopt it. Based on the concept of two-stage adoption, two integrated models to explain organization-level decision to adopt (primary adoption) and individual adoption (secondary adoption) by users of the epidemic prevention cloud, respectively, will be developed. A sample source was achieved by using the roster of the Ministry of Health and Welfare. A series of surveys will be conducted to empirically test the organizational adoption research model from Chief Information Officer and Vice President of MIS department, in the first year. Next, a series of surveys will be conducted to empirically test the individual adoption research model from infection control staff in the secondary year. The structural equation model was used to examine the data. The research results reveal constructive suggestions to researchers, hospitals, and the government to increase the likelihood of adopting epidemic prevention cloud.
