- Key points: Identify three significant/key points from the article.
- Summary: Write a section summarizing the article. Do not simply use information from the article.
- Analysis: Identify how the article aligns with and relates to concepts learned :
Discuss the staffing planning process as well as the workforce planning process and how it impacts future business activities.
Explain steps taken for an organization to forecast its workforce supply and demand.
Contrast internal and external forecasting decisions. - Personal Evaluation: What do you find to be valid or invalid in the article? Do you agree with the author’s assertion(s)? Explain why or why not.
Two pages in length.
Journal of Learning and Teaching in Digital Age, 2019, 4(2), 42-44
ISSN: 2458-8350 (online)
Perspectives
Correspondence to: Jee Young Park, PhD, MD, Clinical Assistant Professor, Department of Pathology, School of Medicine,
Kyungpook National University, Kyungpook National University Chilgok Hospital 807, Hoguk-ro, Buk-gu, Daegu, Republic of
Korea (Zip Code: 41404), Email: pathpjy@naver.com, Phone: (+82-53-200-3405)
Optimal Safe Staffing Standard for Right Workforce Planning
Claire Su-Yeon Park, MSN, RN, Nursing Decision Scientist
(ORCID ID: 0000-0002-2109-9885)
CEO, Center for Econometric Optimization in the Nursing Workforce, Seoul, Republic of Korea
clairesuyeonpark@gmail.com
Jee Young Park, PhD, MD
(ORCID ID: 0000-0002-1857-813X)
Clinical Assistant Professor, Department of Pathology, School of Medicine, Kyungpook National University,
Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
pathpjy@naver.com
Received 31 October 2018, Revised 15 February 2019, Accepted 15 February 2019
Keywords: Artificial Intelligence, Workforce, Optimal Safe Staffing, Evidence-based Informed Shared Decision-making Rationales, Pathology
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.
Figure 1. Google DeepMind’s AI Ophthalmologist.
*Note. Image captured from the DeepMind’s “A major milestone for the
treatment of eye disease,” https://deepmind.com/blog/moorfields-major-
milestone/; https://youtu.be/MCI0xEGvHx8
The challenge, then, will be to set up the Optimal Safe
Staffing Standard for Right Workforce (Park, 2017) to
ensure the best operational effectiveness while also
satisfying patient needs, a quotient which will be in high
demand as the controversy about the healthcare
professional substitution intensifies over time.
However, the scientific evidence of the Optimal Safe
Staffing Standard for Right Workforce is currently
lacking in literature (Park, 2018a, 2018b). To present a
real data-driven Optimal Safe Staffing Standard for
Right Workforce is thus urgent to maintain human
dignity and defend patient safety against possible AI-
mailto:clairesuyeonpark@gmail.com
mailto:pathpjy@naver.com
C. S. Park & J. Y. Park
43 © 2019, Journal of Learning and Teaching in Digital Age, 4(2), 42-44
driven pitfalls which would cause health inequity or
social injustice (O’Neil, 2016).
We plan to conduct a preliminary study about the
Optimal Safe Staffing Standard for Right Workforce in
the setting of pathology using Park’s Optimized Nurse
Staffing [Sweet Spot] Estimation Theory (Park, 2017;
Figure 2) within the year. Park’s Optimized Nurse
Staffing [Sweet Spot] Estimation Theory was developed
by a creative synthesis of Nursing Science (Nursing
Workforce in Home Healthcare Nursing),
Microeconomics (Integrated Production and Cost
Function Theory), Mathematical Economics (Duality
Theorem), and Advanced Applied Mathematics
(Mathematical Programming [Optimization]) (Park,
2017, 2018a). Park’s Optimized Nurse Staffing [Sweet
Spot] Estimation Theory pinpoints specific, practical,
and applicable optimal healthcare safe staffing levels —
e.g. (1) an optimal number of physicians or nurses or (2)
an optimal composition of the healthcare professionals
(physicians + nurses + nursing assistants + AI system
and/or care robots)—maximizing quality of care/patient
outcomes relative to employment costs in a continuum
of change in staffing levels (Park, 2017, 2018a, 2018b).
The levels serve as evidence-based informed shared
decision-making rationales, which can satisfy all parties
constituting our healthcare delivery system—i.e.
patients, nurses and/or doctors, and stakeholders—and
contribute to the patient-centered value-driven (higher
quality yet lower costs) healthcare delivery system
reformation (Park, 2017, 2018a, 2018b).
The following multi-site main study will expand its
scope and depth of the scientific reach to look for an
answer about (1) optimum ranges of patient outcomes
(or quality of care outcomes) and (2) optimum ranges of
spending, which are required for the healthcare
institutions to be included in the Central ‘Optimum
Nurse Staffing Zone’ [ONSZ]—referring to an
intersectional Optimum Nurse Staffing Zone among the
given multiple model settings (Park, 2018b, p.1232).
Figure 2. Park’s Optimized Nurse Staffing (Sweet Spot) Estimation Theory.
*Note. Park’s Optimized Nurse Staffing (Sweet Spot) Estimation Theory: Copyright ⓒ 2016 Park, Claire Su-Yeon. All Rights Reserved. The figure has been
published in the Journal of Advanced Nursing under an exclusive license agreement with John Wiley & Sons, Ltd. (see:
http://onlinelibrary.wiley.com/doi/10.1111/jan.13284/full). The original copyright has been registered in Korea [C-2016-031091] and in the U.S.A. [TX 8-371-
760] with an effective date of 06 Dec 2016; the patent is pending in Korea (Park’s User-friendly Cloud-based Intersectional Optimized Nurse Staffing (Sweet
Spot) Decision-making Support System [10-2017-0052130] with an effective date of 24 Apr 2017), and the Patent Cooperation Treaty (PCT) patent application
claiming priority of the Korean patent application [PCT/KR2018/004660] is pending with an effective date of 23 Apr 2018 (Park, 2017, p.1844). Use of the
original contents, illustrations, or ideas in Park’s Optimized Nurse Staffing (Sweet Spot) Estimation Theory, either in whole or in part, requires written permission
from the copyright/patent holder (Park, 2017, p.1844).
Optimal Safe Staffing Standard
44 © 2019, Journal of Learning and Teaching in Digital Age, 4(2), 42-44
“Science is not solving problems, but finding
problems.
Scientists are divided by their ability to detect
problems.”
(Dr. Arno Penzias, 1978 Nobel laureate in
physics)
Illustrated by Seobeen Lee
Scholars are not Oedipus solving a riddle but the
Sphinx posing one.
Figure 3. Our Role as Scientists.
Creative imagination is highly valued in the era of the
fourth industrial revolution. Accordingly,
interdisciplinary or multidisciplinary research is
necessary to create the new, innovative, and viable
solutions that will address the complexity of our social
problems. We commonly think that such
multidisciplinary research would progress nicely once
various experts got together. This is simply not true. The
fact that most of their research outcomes do not produce
new knowledge systems but are instead merely a
compilation supports this statement.
We have developed a program of research based on the
already well-established theory, Park’s Optimized
Nurse Staffing [Sweet Spot] Estimation Theory (Park,
2017), which functions as a metatheory—a well-suited
bridge between disciplines. We thus believe that our
endeavors to affect the future of workforce policy-
building and decision-making practice through this
evidence-driven win-win cooperation among concerned
parties will cause a cascade of positive change within
the health community.
REFERENCES
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
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.
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