User:Claire Su-Yeon Park

Claire Su-Yeon Park
Claire Su-Yeon Park, MSN, RN, Nursing Decision Scientist is a nursing scholar opening a new specialization in nursing, so-called “Nursing Decision Science," as well as a translator for Jang Seoknam's poems and prose in collaboration with American poet Paulette E. Bane, MA, MFA. She currently serves as CEO of the Center for Econometric Optimization in the Nursing Workforce to pinpoint "Optimal Safe Staffing Levels Maximizing Quality-Cost in the Continuum of Changes," which serves as "Evidence-based Informed shared Decision-Making Rationales"  on the optimal safe staffing levels among all interested partiesㅡi.e. patients, nurses, and stakeholdersㅡ, leading to (a) achieve the Patient-centered/perceived Value-driven Healthcare Delivery System Reform and (b) create a new academic area of specialization in nursing science, “Econometric Optimization in the Nursing Workforce [Nursing Decision Science].

Nursing Decision Science
Her program of research addresses current nursing workforce policy-making by integrating Nursing Science (Nursing Workforce); Microeconomics and Mathematical Economics; Operations Research and Advanced Applied Mathematics (including Mathematical Programming, also called “Optimization”); Decision Psychology; and Computer Science , which she has named “Nursing Decision Science” to pioneer a new specialization in nursing science. The multidisciplinary consilience can provide feasible and viable solutions—not simply right answers—to the important yet unanswered question of how to balance quality, cost, and nurse staffing in the continuum of changes for better nursing workforce practice and policy-making to mitigate global nursing shortages.

Park’s Optimized Nurse Staffing (Sweet Spot) Estimation Theory
Stage Ⅰ of the research program, named Park’s Optimized Nurse Staffing (Sweet Spot) Estimation Theory, helps determine and pinpoint a practical and applicable level of Optimal Safe Nurse Staffing—specifically, the optimum (1) number of nurses, (2) nursing care hours or nursing workloads, and (3) composition of nurse staffing (i.e., registered nurses, nursing associates, assistant nurses, and even care robots)—where nurses, patients, and healthcare organizations (or stakeholders) can all be satisfied (Park, 2018, p.237). The specific approach pinpoints “the theory driven Optimum Nurse Staffing Zone as well as the Optimized Nurse Staffing (Sweet Spot), which can be navigated by Mathematical Programming (Optimization) based on the Duality Theorem in Mathematical Economics (Diewert 1982, p. 556) using MATLAB or MAPLE” (Park, 2017, p. 1844). The “Optimized Nurse Staffing (Sweet Spot)” signifies a single best point of leverage “to achieve the maximum quality of care for patients while simultaneously delivering nurse staffing in the most cost-effective way” (Park, 2017, p. 1845). “Not only does it address a timely issue – i.e., the balanced consilience among quality, cost, and nurse staffing in the continuum of changes – in the healthcare delivery system, but it is also applicable, durable, and valuable due to the fact that Park’s Optimized Nurse Staffing (Sweet Spot) Estimation Theory can be used to determine the sweet spot among quality, nurse staffing, and cost in any healthcare/research setting” (Park, 2017, p. 1845). Park’s Optimized Nurse Staffing (Sweet Spot) Estimation Theory's transdisciplinarity (i.e., transcendent applicability across disciplines, in other words, a metatheory as a well-suited bridge between disciplines) strengthens the unique, scholarly merits. Innovation in the healthcare delivery system as well as advancing nursing practice in real-world situations would be ultimately attainable by achieving value-based nursing care resulting from improved quality yet reduced cost, leading to a transformation of our healthcare delivery system to be value-driven, justifiable, and more effective.

The evidence will also be of interest to government and nurse scientists as well as stakeholders since the optimal safe nurse staffing (in particular, composition) is directly linked to the cost of their expenditures on nurse personnel. For the government, the evidence could alleviate public concern over the potential harm from the poorer quality of care, which may be caused by accepting more assistant nurses. Since poor patient outcomes may consequently lead to an increase in a nation’s healthcare cost, particularly aggravating younger generations' socio-economic burden to support elderly people, the government also could use the data to balance costs and quality of care. Nurse scientists have a responsibility to present viable solutions to the nursing shortages, while being mindful that Registered Nurses (RNs) and assistant nurses do not have a linear relationship (i.e. “technically efficient”) in reality. Differences in quality of care and clinical decision-making, which significantly affect patient outcomes such as mortality and quality of life, can be differentiated based on nursing personnel qualification level. Study findings that do not reflect such an intricate nature of the nursing workforce system are highly possible to distort the truth. The evidence is also important in terms of significantly affecting how to reform the current nursing education system.

She published the paper in the Journal of Advanced Nursing (SCI, PIF 2.759, ISI JCR ⓒ Ranking - Top 5.93% of all Nursing SSCI journals) in May of 2017 after rigorous peer reviews and a 51-page response letter explaining two sets of revisions over 9 months. The paper won the 2017 Nursing Policy Scholarly Award from the Korean Nurses Association on 21 Feb 2018. The article was published under an exclusive license agreement with John Wiley & Sons, Limited; thus, the intellectual property for Park’s Sweet Spot Theory belongs to Claire Su-Yeon Park (Park’s Optimized Nurse Staffing [Sweet Spot] Estimation Theory: Copyright ⓒ 2016 Park, Claire Su-Yeon. All Rights Reserved.). The original copyright has been registered in both Korea [C-2016-031091] and the U.S.A. [TX 8-371-760] with an effective copyright date of 06 Dec 2016; patent-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 patent-pending date of 24 Apr 2017); the Patent Cooperation Treaty (PCT) patent claims priority over the Korean patent application [PCT/KR2018/004660] pending with an effective date of 23 Apr 2018. Use of the contents, illustrations, or ideas in Park’s Sweet Spot Theory, even in part, requires written permission from the copyright/patent holder. Furthermore, a part of an illustration of the Park’s Optimized Nurse Staffing (Sweet Spot) Estimation Theory has been featured on the front cover of the Journal of Advanced Nursing in December 2018 (Volume 74, Issue 12).

Park’s Sweet Spot Theory-driven Decision Psychological Blending Strategy
The stage Ⅰ of the research program also aimed to propose a decision psychological blending strategy to ensure that optimal safe staffing policy really works in nursing practice, so-called "Park’s Theory-driven Decision Psychological Blending Strategy." The new approach was developed by a synthesis of “Prospective Theory” —their work earned Dr. Kahneman the Nobel in economics in 2002—and  “Regulatory Focus Theory”   combined with Park’s Optimized Nurse Staffing (Sweet Spot) Theory, which can play a role as a viable solution to the nursing shortages by leading stakeholders to voluntarily compete with each other on the basis of value-based nursing sufficiency. This approach will accordingly lead the stakeholders to competitively demonstrate their value-based patient-centeredness of their institution. That is, Park’s Theory-driven Decision Psychological Blending Strategy is a practicable and effective operational approach to induce healthcare organizations to have more registered nurses.

The paper is currently under review for publication. The paper includes two ideas protected by Korea’s Unfair Competition Prevention and the Trade Secret Protection Act [2017006449; 2017006617] and the original idea, Park’s User-friendly Cloud-based Intersectional Optimized Nurse Staffing [Sweet Spot] Decision-making Support System (Korean patent-pending [10-2017-0052130] with an effective date of 24 Apr 2017 and the PCT patent-pending [PCT/KR2018/004660] claiming priority of the Korean patent application with an effective date of 23 Apr 2018). The copyright for Park’s Theory-driven Decision Psychological Blending Strategy has been registered in Korea [C-2017-024859] on 18 Oct 2017.

Stage Ⅱ of Park's Program of Research
She is now conducting the stage Ⅱ of the program of research to (1) test and advance Park’s Sweet Spot Theory using the Centers for Medicare & Medicaid Services (CMS) Home Health Compare, the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) Home Health Care Survey, and Limited Data Sets (HH agency cost reports) retrieved from the CMS Data Request Center (ResDAC) and (2) develop the Artificial Intelligence (Deep Learning) Algorithms for Park’s User-friendly Cloud-based Intersectional Optimized Nurse Staffing [Sweet Spot] Decision-making Support System. No peer-reviewed publications related to econometric optimization concepts and analytic procedures currently exist in nursing or even the broader field of health service research. As a result, her work will be the first application of this theory and analysis to healthcare.

Creating Shared Value through the Theory-driven, Evidence-based, Informed Shared Decision-making Rationales on the Optimal Safe Nurse Staffing Levels
We long for the best point of leverage balancing quality and cost; however, most studies seem to still present fragmented “snap shots” of the phenomenon of interest. We should be mindful of this because an unclear picture may lead to muddled policy-making. The evidence produced from her program of research ultimately plays a critical role in guiding the direction of how the three core parties composing our healthcare delivery system, i.e. patients, nurses, and stakeholders, should act judiciously to satisfy ever-changing patient-centered needs. That is, creating shared value among all parties is achievable through the theory-driven, evidence-based, informed shared decision-making rationales on the optimal safe nurse staffing for viable nursing workforce policy-making/materializing, leading to the balanced consilience among quality of care, nurse staffing, and cost. The evidence is accordingly linked to contribute to the World Health Organization’s (WHO) (2016) call for Robust Health Workforce Planning Models to achieve the right skilled health workforce with the optimum number of decent jobs for health workers placed in the right positions based on the right competencies as well as International Council of Nurses (ICN) (2018)’s call for Evidence-based Safe Nurse Staffing Policy and Systems to deliver safe, patient-centered care and enable society to lead better, healthier lives.

Awards
2017 Nursing Policy Scholarly Award from the Korean Nurses Association (21 Feb 2018)