雲端電子病歷使用意圖之探討

A Study on the Adoption Intention of Cloud Electronic Medical Record

環境污染、食品安全及生活壓力引起的健康問題造成今日醫療行為複雜度大幅 提升，針對病症之確認或治療，跨院醫療行為日益頻繁且有其必要性。透過安全機制 快速分享個人病歷以利於跨院醫療行為，乃成為提升醫療品質並控制經濟成本不可或 缺的活動。隨著雲端世代的來臨，人們對其熟悉度也逐日增加。在雲端儲存服務已被 廣泛使用的背景下，本研究針對一般民眾探討雲端電子病歷使用意圖的影響因子，使 用隱私計算理論分析利益與風險對使用意圖的影響，並以科技接受與使用整合模型的 構面代表隱私計算理論中的利益因子。研究架構包含三項信任構面（促成條件）、知覺有用性、知覺易用性、社會規範及隱私關注等自變數，共同影響雲端電子病歷的使 用意圖，而且隱私關注調節信任與意圖之間的關係。實證研究共收集 259 份網路問卷進行分析，結果顯示三項信任構面未對使用意 圖產生影響，知覺有用性、知覺易用性及社會規範對使用意圖有正向影響力；隱私關 注雖然對使用意圖有負向影響力，但其影響力比知覺有用性的影響力小，表示在面對 自己健康問題時，民眾願意把資訊的有用性（利益）放在資訊的隱私性（風險）之前。 隱私關注部份調節信任與使用意圖間的關係；此外，控制變數（性別、年齡、雲端儲 存服務使用經驗、就診頻率）也會影響研究模式的驗證結果。

One of the major challenges facing today’s healthcare industry is the increasingly complicated medical treatments caused by environmental pollution, food safety and life stress. To improve the cost-effectiveness and quality of healthcare services, one possible solution is to share electronic medical records (EMRs) among service providers by using information technologies. As cloud storage service has been adopted by more and more people today, this study investigates, from the perspective of the general public, factors affecting the adoption intention of cloud EMR. Based on the privacy calculus theory, a research model is proposed by simultaneously considering the benefit and risk factors of using cloud EMR. Constructs from the unified theory of acceptance and use of technology (UTAUT) take the role of benefit factors in the model. Three trust constructs (representingfacilitating conditions of UTAUT) together with perceived usefulness (PU), perceived easeof use (PEOU), social norms (SN) and privacy concern (PC) are assumed to affect behavioral intention (BI), while PC moderates the relationship between trust and BI.A total of 259 web-based questionnaires have been collected to validate the research model. Results show that the three trust constructs do not affect BI significantly; PU, PEOU and SN have a positive impact on BI, and PC has a negative impact on BI. The influence of PU is more pronounced than that of PC, indicating people’s willingness to accept risks after considering the benefits of cloud EMR. It is also found that PC partially moderates the relationship between trust and BI. Different control variables (gender, age, cloud storage service experience, and service visit frequency) yield different results of model validation.