Beglaryan Mher, Petrosyan Varduhi, Bunker Edward
Turpanjian School of Public Health, American University of Armenia, 40 Marshal Baghramian, Yerevan 0019, Armenia.
Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, 2024 East Monument St. S 1-200, Baltimore, MD 21205, USA.
Int J Med Inform. 2017 Jun;102:50-61. doi: 10.1016/j.ijmedinf.2017.02.013. Epub 2017 Mar 2.
In health care, information technologies (IT) hold a promise to harness an ever-increasing flow of health related information and bring significant benefits including improved quality of care, efficiency, and cost containment. One of the main tools for collecting and utilizing health data is the Electronic Health Record (EHR). EHRs implementation can face numerous barriers to acceptance including attitudes and perceptions of potential users, required effort attributed to their implementation and usage, and resistance to change. Various theories explicate different aspects of technology deployment, implementation, and acceptance. One of the common theories is the Technology Acceptance Model (TAM), which helps to study the implementation of different healthcare IT applications. The objectives of this study are: to understand the barriers of EHR implementation from the perspective of physicians; to identify major determinants of physicians' acceptance of technology; and develop a model that explains better how EHRs (and technologies in general) are accepted by physicians.
The proposed model derives from a cross-sectional survey of physicians selected through multi-stage cluster sampling from the hospitals of Yerevan, Armenia. The study team designed the survey instrument based on a literature review on barriers of EHR implementation. The analysis employed exploratory structural equation modeling (ESEM) with a robust weighted least squares (WLSMV) estimator for categorical indicators. The analysis progressed in two steps: appraisal of the measurement model and testing of the structural model.
The derived model identifies the following factors as direct determinants of behavioral intention to use a novel technology: projected collective usefulness; personal innovativeness; patient influence; and resistance to change. Other factors (e.g., organizational change, professional relationships, administrative monitoring, organizational support and computer anxiety) exert their effects through projected collective usefulness, perceived usefulness, and perceived ease of use. The model reconciles individual-oriented and environment-oriented theoretical approaches and proposes a Tripolar Model of Technology Acceptance (TMTA), bringing together three key pillars of the healthcare: patients, practitioners, and provider organizations. The proposed TMTA explains 85% of variance of behavioral intention to use technology.
The current study draws from the barriers of EHR implementation and identifies major determinants of technology acceptance among physicians. The study proposes TMTA as affording stronger explanative and predictive abilities for the health care system. TMTA paves a long overlooked gap in TAM and its descendants, which, in organizational settings, might distort construal of technology acceptance. It also explicates with greater depth the interdependence of different participants of the healthcare and complex interactions between healthcare and technologies.
在医疗保健领域,信息技术有望利用不断增长的健康相关信息流,并带来显著益处,包括提高医疗质量、效率和控制成本。收集和利用健康数据的主要工具之一是电子健康记录(EHR)。电子健康记录的实施可能面临诸多接受障碍,包括潜在用户的态度和认知、实施和使用所需的努力以及对变革的抵触。各种理论阐述了技术部署、实施和接受的不同方面。其中一个常见的理论是技术接受模型(TAM),它有助于研究不同医疗信息技术应用的实施情况。本研究的目的是:从医生的角度了解电子健康记录实施的障碍;确定医生接受技术的主要决定因素;并开发一个能更好地解释医生如何接受电子健康记录(以及一般技术)的模型。
所提出的模型源自对从亚美尼亚埃里温医院通过多阶段整群抽样选取的医生进行的横断面调查。研究团队基于对电子健康记录实施障碍的文献综述设计了调查问卷。分析采用探索性结构方程模型(ESEM),并使用稳健加权最小二乘法(WLSMV)估计器处理分类指标。分析分两步进行:测量模型评估和结构模型测试。
所推导的模型确定了以下因素为使用新技术的行为意图的直接决定因素:预期集体有用性;个人创新性;患者影响;以及对变革的抵触。其他因素(如组织变革、专业关系、行政监督、组织支持和计算机焦虑)通过预期集体有用性、感知有用性和感知易用性发挥作用。该模型协调了以个人为导向和以环境为导向的理论方法,并提出了技术接受三极模型(TMTA),将医疗保健的三个关键支柱:患者、从业者和提供者组织结合在一起。所提出的TMTA解释了使用技术的行为意图方差的85%。
本研究借鉴了电子健康记录实施的障碍,并确定了医生中技术接受的主要决定因素。该研究提出TMTA对医疗保健系统具有更强的解释和预测能力。TMTA填补了TAM及其衍生模型长期以来被忽视的空白,在组织环境中,这可能会扭曲对技术接受的理解。它还更深入地阐述了医疗保健不同参与者之间的相互依存关系以及医疗保健与技术之间的复杂相互作用。