No More Unimplementable Nurse Workforce Planning

Ethical Artificial Intelligence in Nursing Workforce Management and Policymaking: Bridging Philosophy and Practice

Background: Despite artificial intelligence's (AI) transformative potential in healthcare, nursing workforce scholarship lacks a cohesive theoretical foundation and well-established philosophical stances to guide safe yet ethical, effective yet efficient, and sustainable AI integration into nursing workforce management and policymaking. This gap poses significant challenges in leveraging AI's benefits while mitigating potential risks and inequities. Aim: This paper aims to (1) present a philosophical discourse centered on Park's optimized nurse staffing (Sweet Spot) theory and (2) propose a novel theoretical framework with specific methodologies for ethical AI-equipped nursing workforce management and policymaking while providing its philosophical underpinnings. Method: A rigorous philosophical discourse was performed through theoretical triangulation, grounded in Park's Optimized Nursing Staffing (Sweet Spot) Estimation Theory. This approach synthesizes diverse philosophical perspectives to create a robust foundation for ethical AI integration in nursing workforce management and policymaking. Discussion: The novel theoretical framework introduces its well-established philosophical underpinnings, bridging moderate realism with post-positivism and contextualism, for ethical AI-equipped nursing workforce management and policymaking. The framework also provides practical solutions for ethical AI integration while ensuring equity and fairness in nursing workforce practices. This approach consequently offers a groundbreaking pathway toward sustainable AI-equipped nursing workforce management and policymaking that balances safety, ethics, effectiveness, and efficiency. Implication on Nursing Management: This paper is the first to present a theoretical framework for ethically integrating AI into nursing workforce management and policymaking, grounded in its robust philosophical underpinnings. It stands out for its creativity and originality, making a significant contribution by opening new avenues for emerging research and development at the intersection of AI and healthcare. Specifically, the framework serves as a practical and pivotal resource for researchers, policymakers, and healthcare administrators navigating the complex landscape of AI integration in nursing workforce management and policymaking. Above all, it is worthwhile in that this paper contributes to the broader intellectual discourse in a thought-provoking and timely manner by addressing AI's inherent limitations in healthcare through a theoretical framework embedded in human philosophical and ethical deliberation. Unlike the current practice where AI safety and ethical risk assessment are conducted after AI solutions have been developed, this approach provides proactive guidance. Thereby, it lays the crucial groundwork for future empirical studies and practical implementations toward desirable healthcare decision-making.

Demand Forecasting of Nurse Talents in China Based on the Gray GM (1,1) Model: Model Development Study

BACKGROUND

In a global context, the shortage of nursing personnel has emerged as a significant challenge, particularly in countries such as China experiencing population aging. The inadequacy of nursing human resources has become one of the primary threats affecting the quality of health services available to Chinese residents. Therefore, forecasting the demand for nursing personnel has become an important issue.

OBJECTIVE

This study presents a Gray GM (1,1) forecasting model for predicting the future 10-year demand for nursing workforce and the number of specialized geriatric nurses, aiming to provide a scientific basis for the development of policies in health care institutions in China.

METHODS

Based on data from the China Statistical Yearbook 2022, the Gray GM (1,1) model was used to predict the demand for nursing jobs and geriatric nurses over the next 10 years (2024-2033).

RESULTS

The results indicate that from 2024 to 2033, amidst a continuous growth in the overall population and an increasingly pronounced trend of population aging, the demand for nursing workforce in China, especially for specialized geriatric nurses, is projected to steadily increase.

CONCLUSIONS

The paper provides a reference basis for the establishment of China's health care workforce system and the involvement of government departments in health care workforce planning.

On the Association Between Demographic Structural Change and the Effectiveness of Nurse Staffing Policy for Inpatient Care: Evidence from Taiwan

Purpose

This study investigates the influence of demographic changes on the effectiveness of hospital nurse staffing policy, measured by the cumulative response of inpatient care quality to adjustments in hospital nurse staffing levels in Taiwan.

Methods

The research design utilized in this study aligns with the observational time-series methodology, and a total of 99 monthly time-series observations were collected from multiple databases administered by the Taiwan government over the period from January 2015 to March 2023. Specifically, the time-varying parameter vector autoregressive and autoregressive distributed lag models were employed to investigate the association between age distribution and nurse staffing policy effectiveness.

Results

The time-varying impulse responses of the unplanned 14-day readmission rate after discharge to changes in nurse staffing levels indicate a positive association between patient-to-nurse ratios and unplanned 14-day readmission rates across various types of hospitals. Nevertheless, the effectiveness of hospitals' nurse staffing policy is observed to diminish with population aging, particularly evident in medical centers and regional hospitals.

Conclusion

Policymakers should establish lower mandated patient-to-nurse ratios, grounded in practical nurse workforce planning, to address the needs of an aging society and enhance inpatient care quality through improved nurse staffing in hospitals.

How, scholar, do you want to be remembered by future generations?: Finding your own balance between the ownership of knowledge and the freedom to pursue economic interests

To assume that copyright just protects "expressions," (unlike "ideas" protected by patents), may be misleading because a particularly ordered procedure is also protected by copyright law. The procedure as well as the image of Park's Sweet Spot Theory require prior written permission from the original copyright holder for its use. Because this would stymie future generations from building further on this idea, I returned the theory-based patent to society so that prospective research and development will have no hindrances in the establishment of new knowledge/solutions in nursing science.

"More is not always better": Park's sweet spot theory-driven implementation strategy for viable optimal safe nurse staffing policy in practice

AIM

This paper aims to propose Park's sweet spot theory-driven implementation strategy, which makes optimal safe staffing policy really work in nursing practice.

BACKGROUND

For the last 40 years, mainstream nursing workforce research has emphasized that having more registered nurses leads to better patient outcomes, and yet staffing policies have failed to implement this crucial concept. Meanwhile, global nursing shortages have become rampant, a problem that only dilutes the skill-mix ratios in the nursing workforce. Low fertility and an aging population worldwide are then accelerating these shortages. These dire circumstances may be persisting because of unclear, unsubstantiated cost-efficiency in the nursing workforce. For this reason, there remains a dearth of well-researched evidence for a clear threshold on optimal safe staffing levels that could maximize quality of care relative to cost given limited healthcare financial budgets and which could also be fitted into each care setting. Along with that, an implementation strategy for optimal safe staffing levels is nonexistent.

SOURCES OF EVIDENCE

An implementation strategy has been developed through interdisciplinary consilience-based theory synthesis of both prospective theory and regulatory focus theory combined with Park's optimized nursing staffing (sweet spot) estimation theory.

DISCUSSION/CONCLUSIONS

A theory-driven novel implementation strategy is proposed, which functions as a nudge strategy that uses the synchronous balance of quality of care, nurse staffing, and cost. It illustrates (1) how to create shared value among patients, nurses, and stakeholders through robust evidence-based, informed shared decision-making rationales on the optimal safe nurse staffing levels and (2) how to induce stakeholders to overcome resistance to innovation and improve their nursing workforce through value chain in management science.

IMPLICATIONS FOR NURSING WORKFORCE POLICY

This novel implementation strategy may be a viable solution to mitigate the nursing shortage by leading stakeholders (1) to compete with each other (on the basis of nursing sufficiency) and (2) to competitively demonstrate the patient-centered value (patient-perceived care quality relative to cost) of their institutions.