Optimal Safe Staffing Standard for Right Workforce Planning

Claire Su-Yeon Park, Jee Young Park


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Abstract


The Artificial Intelligence (AI)-driven automated decision-making support system has been heralded as a considerable workforce replacement in the near future by automating mundane repetitive tasks and eliminating time-consuming support tasks in all disciplines (Park & Glenn, 2017). It is no exaggeration to say that such a prediction is already manifesting as reality. The typical example is an application of AI to radiology and pathology in medicine. The Google DeepMind has developed the ‘AI Ophthalmologist,’ which can diagnose complicated eye diseases in real time (within 30 seconds) (Fauw et al., 2018; see Figure 1) and is currently undergoing commercialization. In the arena of pathology, AI has already shown its potential for cancer detection in differentiating from the precancerous lesion through an improved grading of tumors based on machine learning technology in breast, lung, prostate, and stomach cancers (Niazi, Parwani, & Gurcan, 2019; Chang et al., 2019). Even though a number of practical hurdles in the field of the AI-integrated pathology still exist—which is mainly caused by a higher degree of complexity and specialty of the pathologic diagnosis process—such difficulties are expected to be soon overcome by rapid advances in AI technology.

Accordingly, there is a growing sense of debate that medical AI could cause human doctors to lose their jobs (Lee, 2019). Since the doctoral function that can be replaced by AI is mainly limited to diagnoses at this stage, the opinion that doctors who make good use of AI would have a better chance of surviving seems to be a likely outcome (Lee, 2019). However, a considerable adjustment to the healthcare workforce also seems to be inevitable because healthcare institutions will continue to secure a competitive advantage through an AI’s economic efficiency in the fast-paced healthcare industry, even though ethical debates related to commercial exploitation of such technological advances continues (Lee, 2019). It may be safe to say that a re-allocation of human resources is preordained in the AI-integrated healthcare system.

 


Keywords


Artificial Intelligence; Workforce, Optimal Safe Staffing; Evidence-based Informed Shared Decision-making Rationales; Pathology

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References


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Park, C. S. (2017). Optimizing staffing, quality and cost in home healthcare nursing: Theory synthesis. Journal of Advanced Nursing, 73(8), 1838-1847. doi: 10.1111/jan.13284

Park, C. S. (2018a). Thinking outside the box [Editorial]. Journal of Advanced Nursing, 74(2), 237-238. doi:10.1111/jan.13312

Park, C. S. (2018b). Challenging rules, creating values: Park’s sweet spot theory-driven central-‘optimum nurse staffing zone’ [Editorial]. Journal of Advanced Nursing, 74(6), 1231-1232. doi:10.1111/jan.13496

Park, Y. S., & Glenn, J. (2017). The millennium project: World future report 2055. Seoul, Republic of Korea: The Business Books Co. Ltd.


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Journal of Learning and Teaching in Digital Age. All rights reserved, 2016. ISSN:2458-8350