Optimal Safe Staffing Standard for Right Workforce Planning

Yıl 2019, Cilt: 4 Sayı: 2, 42 - 44, 01.06.2019

Claire Su-yeon Park Jee Young Park

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Chang, H. Y., Jung, C. K., Woo, J. I., Lee, S., Cho, J., Kim, S. W., & Kwak, T.-Y. (2019). Artificial Intelligence in Pathology. Journal of Pathology and Translational Medicine, 53(1), 1-12. doi:10.4132/jptm.2018.12.16
• Fauw, J. D., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., . . . Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350. doi:10.1038/s41591-018-0107-6
• Lee, J.-H. (2019). 2019 Medical Artificial Intelligence Forum: “Will AI with more accuracy in diagnosis work for a doctor?” Retrieved from https://youtu.be/SISyzAKudrA
• Niazi, M. K., Parwani, A. V., & Gurcan, M. N. (2019). Digital pathology and artificial intelligence. The Lancet Oncology, 20(5). doi:10.1016/s1470-2045(19)30154-8
• O’Neil, C. (2016). Weapons of math destruction: how big data increases inequality and threatens democracy. New York, NY, U.S.A.: William Morris Endeavor Entertainment, LLC.
• 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.