Important concepts in leadership management related to the field of medical care management include servant leadership, charismatic leadership, and transformational leadership. Since the 2020 emergence of the coronavirus pandemic, the world has faced the immediate challenges of epidemic prevention and control. Although national government and medical system officials as well as scholars have weighed in on this issue, their leadership does not appear to line up the core ideas of leadership. Daft and Lengel (2000) examined the influence of fusion leadership on individuals and organizations. The fusion of many nuclei of leadership intentions will produce great power and influence. To elucidate the concept of integrated leadership for individuals and organizations in the post-pandemic healthcare system, this paper summarizes the defining characteristics of fusion leadership based on the conceptual analysis method of Walker and Avant (2019). Concurrently, we confirm the antecedents and consequences of fusion leadership, use different cases to illustrate the analysis, and share the reference indicators and measurements of fusion leadership to provide a reference for healthcare system administrators.
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http://dx.doi.org/10.6224/JN.202310_70(5).12 | DOI Listing |
Clin Pharmacol Ther
January 2025
Clinical Pharmacology, Genentech/Roche, South San Francisco, California, USA.
An immunogenicity risk assessment (IRA) is a relatively new expectation of health authorities that is increasingly incorporated into the drug development process across the pharmaceutical/biotech industry. The guiding principle for an IRA includes a comprehensive evaluation of product- and patient-related factors that may influence the immunogenic potential of a biotherapeutic drug and a potential action plan. The Immunogenicity Working Group from the IQ Consortium (Clinical Pharmacology Leadership Group) has conducted a survey to understand the current practices for conducting IRAs and relevant aspects of bioanalysis.
View Article and Find Full Text PDFInt J Radiat Oncol Biol Phys
January 2025
National Cancer Institute, Bethesda, MD. Electronic address:
This white paper examines the potential of pioneering technologies and artificial intelligence (AI)-driven solutions in advancing clinical trials involving radiotherapy. As the field of radiotherapy evolves, the integration of cutting-edge approaches such as radiopharmaceutical dosimetry, FLASH radiotherapy, image-guided radiation therapy (IGRT), and AI promises to improve treatment planning, patient care, and outcomes. Additionally, recent advancements in quantum science, linear energy transfer/relative biological effect (LET/RBE), and the combination of radiotherapy and immunotherapy create new avenues for innovation in clinical trials.
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Clinical Development, POINT Biopharma, a wholly owned subsidiary of Eli Lilly and Company, Indianapolis, IN, United States.
Introduction: SPLASH (NCT04647526) is a multicenter phase III trial evaluating the efficacy and safety of [Lu]Lu-PNT2002 radioligand therapy in metastatic castration-resistant prostate cancer (mCRPC). This study leveraged a lead-in phase to assess tissue dosimetry and evaluate preliminary safety and efficacy, prior to expansion into a randomized phase. Here we report those results.
View Article and Find Full Text PDFCardiovasc Diabetol
January 2025
Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: Diabetic myocardial disorder (DbMD, evidenced by abnormal echocardiography or cardiac biomarkers) is a form of stage B heart failure (SBHF) at high risk for progression to overt HF. SBHF is defined by abnormal LV morphology and function and/or abnormal cardiac biomarker concentrations.
Objective: To compare the evolution of four DbMD groups based on biomarkers alone, systolic and diastolic dysfunction alone, or their combination.
Sensors (Basel)
January 2025
Department of Instruction and Leadership, Duquesne University, Pittsburgh, PA 15282, USA.
This article examines how sensor technologies (such as environmental sensors, biometric sensors, and IoT devices) intersect with conversational AI models like ChatGPT 4.0. In particular, this article explores how data from different sensors in real time can improve AI models' comprehension of surroundings, user contexts, and physical conditions.
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