Objective: Leadership’s impact in healthcare is crucial as it notably shapes the experiences and performance of nursing staff. This study explores the dominant leadership styles among nurse managers in Hail, Saudi Arabia, as experienced by their nursing staff. The inquiry also examines how these leadership approaches directly influence critical organizational outcomes, including leader effectiveness, employee satisfaction, and staff’s willingness to exert extra effort.
Methods: A cross-sectional design involving participants recruited via convenience sampling from four government hospitals in Hail, Saudi Arabia. Data were collected using the 45-item Likert-type Multifactor Leadership Questionnaire (MLQ) and analyzed using SPSS Statistics.
Results: Among the 372 nurses analyzed, transformational leadership (2.56 ± 0.75) significantly outscored other styles (p <.001) and had the highest correlation with the leadership outcomes of effectiveness, extra effort, and satisfaction (R2 of 0.828, 0.786, and 0.760, respectively) compared to the transactional and laissez-faire leadership styles. Additionally, linear regression analysis revealed that transformational leadership explained 69% of effectiveness, 61.7% of extra effort, and 58% of satisfaction variances. Within the transformational framework, “inspirational motivation” strongly correlated with positive outcomes.
Conclusions: This study emphasizes transformational leadership’s essential role in healthcare, urging nurse leaders to embrace this style, with a focus on strategies that boost motivation. It also recommends that healthcare institutions initiate targeted programs to develop their leaders’ transformational leadership characteristics.

Objective: Compare the triage care referral accuracy of artificial intelligence (AI)-based virtual triage (VT) to rules-based triage protocols (RBTP) live telephonic triage.
Methods: Clinical vignettes were selected for a comparison of care referral accuracy of RBTPs with a widely utilized AI-based VT solution. Vignettes (149) included patient complaints, expected triage and urgency assessment. Triage levels were mapped to three triage categories (urgent care, non-emergent care and self-care). Each vignette was evaluated/completed using AI-based VT and RBTP triage modalities by a total of four physicians in series, with independent assessment for errors and inconsistencies. Triage assessment precision was analyzed by matching the expected triage assessment, sensitivity and F1 scores (harmonic mean of precision and recall).
Results: Both modalities achieved > 70% triage accuracy, and safety performance was identical at 91%. AI-based VT was more accurate in care referral for emergency and non-emergency care and overtriaged to emergency care 50% less frequently than RBTP, but was less accurate than RBTP in self-care vignettes (neither statistically significant). Both modalities demonstrated decreased sensitivity as care urgency/acuity decreased, more pronounced in AI-based VT than RBTP. AI-based VT captured four times as much information and data as RBTP.
Conclusions: AI-based VT and RBTP were comparable in care referral accuracy and disposition safety. While AI-based VT provides accurate and safe triage recommendations at a lower total cost, care organizations should assess how AI-based VT compares to a live clinical triage capability with respect to organizational priorities, budgetary considerations, characteristics of the patient/member population served, and the existing technological environment.

Objective: Recent increases in per capita income and longevity in Central and Eastern European counties (CEECs), alongside a slow-changing soviet-era public healthcare system, has led to the emergence of private hospitals. This paper investigates the differential patient service quality perceptions for private versus public hospitals, as well as for three types of healthcare services: primary, ambulatory, and inpatient care.
Methods: Data from 1,673 patients of private and public hospitals in the capital of Romania were collected in face-to-face interviews. Analysis of covariance and partial-least-squares techniques were used to examine the relationships between perceived service quality, hospital ownership status and the type of health service patients received.
Results: Over 70% of women prefer private health facilities to public hospitals (compared to less than 30% of men). While private hospitals rank higher than public hospitals on most attributes, the interaction effect of gender and hospital type reveals that assurance and empathy are the only significant attributes in driving women to private hospitals. (Physical facilities and staff appearance) as well as intangible dimensions of service quality (assurance, responsiveness, reliability, and empathy) have a positive impact on perceived overall service quality of healthcare. Improvements in perceptions of hospital’s tangibles, staff’s responsiveness and empathy have the greatest potential to enhance perceived overall service quality.
Conclusions: This paper demonstrates the importance of breaking down health services into various sub-categories both in terms of perceived healthcare attributes and in terms of tangible healthcare facilities, such as public and private hospitals.

Objective: This study investigated the differential association between nurse staffing in safety-net hospitals (SNHs) and non- SNHs.
Methods: This retrospective cross-sectional study utilized multilevel mixed-effects linear regression models and included data from 1,228 hospitals.
Results: The results showed that SNHs in the top quartile of disproportionate share hospital (DSH) payments had lower nurse staffing ratios (β = -0.86; p-value <.001), indicating a lower nurse-to-patient ratio, compared to non-SNHs. This association persisted even after adjusting for the county and hospital factors.
Conclusions: These findings suggest that nurse staffing in SNHs may be impacted by the financial challenges associated with providing uncompensated care to vulnerable populations. Understanding the differences in nurse staffing between SNHs and non-SNHs can provide insights for improving quality of care. Further research is required to explore the impact of nurse staffing on patient outcomes in SNHs.

Objective: This retrospective study explores the strategic plan formulated by AHMC Health System in California, USA, to sustain and improve quality of care and emergency department (ED) efficiency during the COVID-19 pandemic. It also analyzes the plan’s outcomes. ----Background:==== The COVID-19 pandemic has posed challenges for both individuals and healthcare industries alike, impacting decision-making and access to care. AHMC faced staff and resource shortages, patient reluctance, and difficulties adapting to rapidly evolving public health guidelines. These challenges highlighted the critical need for effective plans to maintain or improve healthcare quality and ED performance.
Methods: AHMC adopted a comprehensive three-layer strategic plan in 2020. The first layer, “Pandemic Response,” focused on leadership, staff training and education, infection control, new treatments, and employee vaccination rates. The second layer, “ED Throughput,” set objectives for metrics such as door-to-doctor (door-to-doc) time, ancillary turnaround time (TAT), ED length of stay (LOS), and the left-without-being-seen (LWBS) rates. Progress was monitored through monthly improvement meetings. The third layer, “Quality Excellence,” tracked improvements in COVID-adapted objectives on quality initiatives, based on CMS Quality Star Ratings, Leapfrog Hospital Safety Grades, and Yelp review scores.
Results: By 2023, the three-layer strategic plan had led to many improvements in the quality of care and ED efficiency. AHMC identified 22,287 positive COVID-19 cases, expanded its ventilator inventory by 50%, and enhanced patient outcomes by applying updated treatments. Additionally, AHMC saw a 3% reduction in ED wait times and sustained its overall patient satisfaction rates, CMS Quality Star Rating, and Leapfrog Hospital Safety Grade scores.
Conclusions: AHMC’s three-layer strategic plan showed effectiveness in maintaining quality of care and ED efficiency during the COVID-19 pandemic. By focusing on “Pandemic Response,” “ED Throughput,” and “Quality Excellence,” AHMC was able to adapt to the rapidly evolving public health guidelines, expand its capacity to treat COVID-19 patients and sustain its overall patient safety, satisfaction, and quality ratings. The implementation of this plan highlights the importance of proactive and comprehensive strategies in managing healthcare crises.