Challenging rules, creating values: Park's sweet spot theory‐driven central‐‘optimum nurse staffing zone’

The nursing shortage is a serious global issue. Low birth rates and a rapidly ageing population are accelerating the demands of nursing professionals. However, the challenging aspects of nurses’ work environment—e.g., a poor salary, long shift length, heavy workload, temporary staffing or bullying—are still ongoing, leading to compromised patient care, more care left undone, adverse events, poor care quality, inequity in access to health care, longer waiting times, burnout and illness among nurses, and even unexpected yet preventable patient deaths (Ball et al., 2017; Borneo, Helm, & Russell, 2017; Canada Nurse Association, 2017; The Lancet, 2017). Not surprisingly, a high turnover rate of nurses is widespread globally. Each country's own sociopolitical and financial conflicts exacerbate the nursing shortage: e.g., “Congress's plan to cut off funding for the Title VIII nursing workforce development programs” and “massive Registered Nurses’ (RNs) retirements” in the United States (American Nurses Association, 2017; McMenamin, 2014), “Brexit” and allowance of “Nursing Associates” in the United Kingdom (Donnelly, 2016; Watts, 2017), “Comprehensive Nursing Care Services” in South Korea (Park, 2017a), the “Nurses’ Strike” in Kenya (The Lancet, 2017), and so on. Thus far, the perils of such an insufficient nursing workforce have been rigorously investigated in relation to both patient outcomes and quality of care outcomes and extensively reported in the literature (Welton, 2016), which often emphasizes the importance of having more RNs to ensure patient safety (Aiken et al., 2014). Nevertheless, in practice the nursing workforce is still insufficient, and the controversial debate about nursing efficiency continues (Aiken et al., 2011; Borneo et al., 2017). It is time for a paradigm shift in the nursing care delivery system from “volume-driven” to “value-driven” to achieve the best balance between patient-centred outcomes under a given health condition and the patient-level nursing care costs of achieving those outcomes (Lee, Campion, Morrissey, & Drazen, 2015; Porter, 2010; Welton, 2016). There is a debate on whether a well-established payment system for nursing care can lead healthcare organizations to have more nurses through proving the value of nursing in a visible way such as a value of money. This is desirable in terms of tangibly acknowledging the value of nursing and strengthening nursing's professionalism. However, a fee-for-service system has been identified as a main cause of rapidly increasing healthcare costs and could jeopardize the continuum and integration of care by creating overlapping and severance cares (Miller et al., 2017). Healthcare organizations may also choose to attain the maximum possible revenue by decreasing nurse staffing or allocating extra work to existing nurses, thereby lowering the quality of care. A fee-for-nursing care system may accordingly result in increased premiums yet poorer care with fewer nurses in practice, undermining value-based nursing care. What about establishing a law with strict regulations to secure a sufficient nursing workforce? That also cannot be a permanent solution to the nursing shortage. First, evidence-based, informed shared decision-making rationales with scientific rigor on the optimal nurse staffing are absent from the current literature, which may result in muddled policy-making (Park, 2017a). Second, we already know that many ineffectual laws and ordinances already exist, which often happen as a result of interest groups’ lobby and pressure. Healthcare organizations may meet the legal minimum requirements for the nursing workforce and then delay an appointment as long as possible. Knowing that they have met the legal responsibility, organizations may make only passive endeavours to improve morale and working conditions for nurses. Quality of care outcomes and patient outcomes reflect the complexity of the healthcare delivery system as well as each patient's medical condition, demonstrating the need to evaluate care quality from various angles and address it using multidimensional outcome indicators—even, jointly and longitudinally (Dale, Mate, & Compton-Phillips, 2017; Porter, 2010). Costs also need additional evaluative tools to estimate the true total costs over each patient's full cycle of care, which includes attribution of shared resources such as hospital staffing to each patient depending on the actual resources used for his/her care (Porter, 2010). Risk adjustment is additionally required to capture the exact value based on each medical condition, which makes relevant comparisons among patients and healthcare organizations available (Porter, 2010). However, Porter's individual patient-level approach may not be concordant with the population-level healthcare delivery system of countries with universal health coverage (Gray, 2017). The US-driven value-based healthcare delivery system does not help main stakeholders such as a health minister or payer (Gray, 2017). It also does not consider the socioeconomic benefits created by improved population health outcomes (Gray, 2017). All policy-making parties need to consider finite budgets, equitable access to care, expected socioeconomic benefits relative to losses, future-oriented preventive measures to control healthcare costs, and establishing a well-functioning health workforce (Gray, 2017) because we live together within a community. Creating a party-and-party shared value thus requires assimilating different viewpoints, making decision-making more complicated. In this regard, Mathematical Programming has a noticeable limitation in providing a reliable, stable and sturdy Optimum Nurse Staffing Zone because the technique produces only one single best optimal point (i.e., Optimized Nurse Staffing [Sweet Spot]) under a given model setting. That is, Optimized Nurse Staffing (Sweet Spot) can be continuously changed as the model setting(s), selected quality/cost variable(s), chosen reference(s) indicating the method to transform the selected quality variable(s) into a value of money, and so forth, change. Such variability in estimation of the risk-adjusted exact value may thus threaten the cogency, stability and longevity of nursing workforce decision-making and policy-building. Park's (2017b) covers this limitation by providing an intersectional Optimum Nurse Staffing Zone, a so-called “Central-’Optimum Nurse Staffing Zone’” (C-ONSZ) among the given model settings. To present a robust rationale for better, more viable decision-making in the nursing workforce, multiple iterations under multiple model settings are demanded. The uniqueness accordingly has led to the development of Park's Theory-driven Artificial Intelligence Algorithm (in progress) to address the rationale's complexity and uncertainty while maintaining scientific rigor, which will be provided in a forthcoming article.

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