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Employability implications of artificial intelligence in healthcare ecosystem: responding with readiness

机译:人工智能在医疗保健生态系统中的就业性影响:准备回应

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Purpose - Intervention of artificial intelligence (AI) has brought up the issue of future job prospects in terms of the employability of the professionals and their readiness to harness the benefits of the Al. The purpose of this study is to recognize the implications of AI on employability by analyzing the issues in the health-care sector that if not addressed, can dampen the possibilities offered by AI intervention and its pervasiveness (Cornell University, INSEAD, and WIPO, 2019). Design/methodology/approach - To get an insight on these concerns, an approach of total interpretive structural modelling, cross impact matrix multiplication applied to classification and path analysis have been used to understand the role of the critical factors influencing employability in the health-care sector. Findings - This study primarily explores the driving-dependence power of the critical factors of the employability and displays hierarchical relationships. It also discusses measures which, if adopted, can enhance employability in the health-care sector with the intervention of AI. Research limitations/implications - Employability also has an impact on the productivity of the healthcare service delivery which may provide a holistic opportunity to the management in health-care organizations to forecast the allocation and training of human resources and technological resources. Orlginallty/value - The paper attempts to analyze AI intervention and other driving factors (operational changes, customized training intervention, openness to learning, attitude toward technology, job-related skills and AI knowledge) to analyze their impact on employability with the changing needs. It establishes the hierarchical relationship among the critical factors influencing employability in the health-care sector because of the intervention of AI.
机译:目的 - 人工智能的干预(AI)就专业人士的就业能力提出了未来的工作前景问题,以及他们准备利用AL的福利。本研究的目的是通过分析卫生保健部门的问题,认识到AI对就业能力的影响,如果没有解决,可以抑制AI干预和普遍存在所提供的可能性(康奈尔大学,INSEAD和WIPO,2019年)。设计/方法/方法 - 了解对这些问题的识别,一种完全解释性结构建模的方法,应用于分类和路径分析的交叉冲击矩阵乘法已被用于了解影响卫生保健中使用能力的关键因素的作用行业。调查结果 - 本研究主要探讨了就业性的关键因素的驾驶依赖权,并显示了等级关系。它还讨论了如果通过的措施,可以通过AI的干预来提高医疗部门的就业能力。研究限制/影响 - 就业性也对医疗保健服务交付的生产力产生了影响,这可能为医疗保健组织的管理提供全部机会,以预测人力资源和技术资源的配置和培训。 Orlginalty / Value - 纸张试图分析AI干预和其他驱动因素(操作变化,定制培训干预,学习,技术态度,与就业相关技能和AI知识的态度),以分析它们对不断变化的需求的影响。由于AI的干预,它建立了影响卫生保健部门的就业性的关键因素之间的层次关系。

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