UNASSIGNED: This study identified the Decision-making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) method was used to construct a multilayer recursive structural model and to analyze the interrelationships between the levels. A MICMAC quadrant diagram was used for a cluster analysis.
UNASSIGNED: The ISM recursive structural model was divided into a total of seven layers. The bottom layer contained the four factors of High risk of data leakage (T1), Lack of awareness of the value and benefits of AI healthcare technology (T5), Lack of management leadership support (O1), and Government policies (E1). Having a low dependency but high driving force, these factors are the root causes of adoption by healthcare organizations. The topmost layer contained the most direct factors, which had a high dependency but the low driving force, influencing adoption: Competitive pressures (E2), Lack of patient trust (E5), and Lack of excellent partnerships (E7). Healthcare organizations are more concerned with technology and their environments when deciding to adopt intelligent healthcare resources.
UNASSIGNED: The combination of the three methods of DEMATEL-ISM-MICMAC construction models provides new ideas for smart healthcare services for hospitals. The DEMATEL method favors the construction dimension of the micro-model, while the ISM method favors the construction dimension of the macro-model. Combining these two methods may reduce the loss of information within the system, simplify the matrix calculation workload, and improve the efficiency of operations while decomposing the complex problems into several sub-problems in a more comprehensive and detailed way. Conducting cluster analysis of the adoption determinants utilizing MICMAC quadrant diagrams may provide strong methodological guidance and decision-making recommendations for government departments, senior decision-makers in healthcare organizations, and policy-makers in associations in the senior care industry.
UNASSIGNED:这项研究确定了决策试验和评估实验室解释结构建模(DEMATEL-ISM)方法用于构建多层递归结构模型并分析之间的相互关系。水平。MICMAC象限图用于聚类分析。
UNASSIGNED:ISM递归结构模型共分为七层。底层包含数据泄露风险高(T1)四个因素,缺乏对AI医疗技术(T5)的价值和益处的认识,缺乏管理领导支持(O1),和政府政策(E1)。依赖性低,驱动力高,这些因素是医疗机构采用的根本原因。最顶层包含最直接的因素,依赖性很高,但驱动力很低,影响采用率:竞争压力(E2),缺乏患者信任(E5)缺乏优秀的合作伙伴关系(E7)。医疗机构在决定采用智能医疗资源时更关注技术及其环境。
UNASSIGNED:DEMATEL-ISM-MICMAC构建模型的三种方法的结合为医院的智能医疗服务提供了新思路。DEMATEL方法有利于微观模型的构造维度,而ISM方法有利于宏观模型的构造维度。将这两种方法结合起来,可以减少系统内信息的丢失,简化矩阵计算工作量,并提高操作效率,同时以更全面和详细的方式将复杂的问题分解为几个子问题。利用MICMAC象限图对采用决定因素进行聚类分析可以为政府部门提供强有力的方法指导和决策建议,医疗机构的高级决策者,和高级护理行业协会的政策制定者。