https://www.sciedupress.com/journal/index.php/jha/issue/feedJournal of Hospital Administration2024-01-18T17:33:55-08:00Edith Leceajha@sciedupress.comOpen Journal Systems<p><img style="float: right; padding-left: 20px; padding-right: 20px;" src="/journal/public/site/images/jha/jha-1.jpg" alt="" width="300" />Journal of Hospital Administration (PRINT ISSN 1927-6990, ONLINE ISSN 1927-7008) is an international, open access, and peer-reviewed scientific journal published by Sciedu Press. It is devoted to publishing research papers in the fields of managing practice and research in all branches of hospital administration. JHA welcomes original articles and reviews. It is published in both online and printed versions.</p><p><strong>JHA is included in:</strong></p><ul><li><a style="font-size: 10px;" href="https://scholar.google.com/citations?hl=en&user=hiyI_KUAAAAJ&view_op=list_works&gmla=AJsN-F7AzfxTQRtz9xWke39Rs8oqyGsIP1qg5yDVRsXyWqtqTSK7_NiPvumXU7cFVL0ddy9HhYKm1Py5NzLA7mZRP5R4t3DDMy2Xg1moyEqgRn7axknNtCU">Google Scholar</a></li><li>LOCKSS</li><li>PKP Open Archives Harvester</li><li>SHERPA/RoMEO</li><li>The Standard Periodical Directory</li></ul><p><strong>Areas include but are not limited to:</strong></p><ul><li>Healthcare Quality and Patient Safety</li><li>Health Economics</li><li>Health Policy</li><li>Health Services</li><li>Clinical Ethics</li><li>Clinical Risk</li><li>Health Facilities Management</li><li>Health Data Management</li><li>Healthcare Informatics</li><li>Nursing Management</li><li>Clinical Department Management</li><li>Out-patient Management</li><li>Inpatient Management</li><li>Health Insurance</li><li>Hospital Accreditation</li><li>Public Health</li></ul><p>To facilitate rapid publication and minimize administrative costs, JHA accepts <a href="/journal/index.php/jha/about/submissions#onlineSubmissions">Online submission</a> and <a href="mailto:jha@sciedupress.com">Email submission</a>. All manuscripts and any supplementary material can be submitted via the journal’s Online Submission and peer-review system or email to <a href="mailto:jha@sciedupress.com">jha@sciedupress.com</a>. For online submission, please create a new account and then follow the instructions given.</p><p><strong>Sections</strong></p><p>Original Articles, Reviews, Case Studies, Experience Exchange</p>https://www.sciedupress.com/journal/index.php/jha/article/view/24775Exploring the leadership styles of nurse managers in Hail, Saudi Arabia: A cross-sectional analysis2023-12-27T17:58:07-08:00Salwa Alrashidiafafalrimali@gmail.comWafa Aleneziafafalrimali@gmail.comAfaf Alrimaliafafalrimali@gmail.comMashael Alshammariafafalrimali@gmail.com<strong>Objective:</strong> 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.<br /><strong>Methods:</strong> 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.<br /><strong>Results:</strong> Among the 372 nurses analyzed, transformational leadership (2.56 ± 0.75) significantly outscored other styles (<em>p</em> < .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.<br /><strong>Conclusions:</strong> 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.2023-11-26T17:30:28-08:00Copyright (c) 2023 Journal of Hospital Administrationhttps://www.sciedupress.com/journal/index.php/jha/article/view/24793A comparative performance analysis of live clinical triage using rules-based triage protocols versus artificial intelligence-based automated virtual triage2024-01-18T17:33:55-08:00George A. Gellertshegfey@gmail.comKacper Kuszczyńskikacper.kuszcynski@infermedica.comNatalia MarcjaszNatalia.marcjasz@infermedica.comJakub Jaszczakjakub.jaszczak@infermedica.comTim Pricetim.price@infermedica.comPiotr M. Orzechowskipiotr.oczechowski@infermedica.com<strong>Objective:</strong> Compare the triage care referral accuracy of artificial intelligence (AI) based virtual triage (VT) to rules-based triage protocols (RBTP) live telephonic triage.<br /><strong>Methods:</strong> 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).<br /><strong>Results:</strong> 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.<br /><strong>Conclusions:</strong> 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.2023-12-27T17:56:34-08:00Copyright (c) 2023 Journal of Hospital Administration