Medical errors have been identified as one of the greatest evils in the field of healthcare, causing millions of patient deaths around the globe each year, especially in developing and poor countries. Globally, the social, economic, and personal impact of medical errors leads to a multi-trillion USD loss. Undoubtedly, medical errors are serious public health concerns in modern times, which could be mitigated by taking corrective measures. Different factors contribute to an increase in medical errors, including employees' risk of burnout. Indeed, it was observed that hospital employees are more exposed to burnout situations compared to other fields. In this respect, managing hospital employees through transformational leadership (TL) may reduce the risk of burnout. However, surprisingly, studies on the relationship between TL and burnout are scarce in a healthcare system, indicating the existence of a critical knowledge gap. This study aims to fill this knowledge gap by investigating the role of TL in reducing the risk of burnout among hospital employees. At the same time, this study also tests the mediating effects of resilience and role clarity with the conditional indirect effect of intrinsic motivation in the above-proposed relationship. To test different hypotheses, a hypothetical model was developed for which we collected the data from different hospital employees ( = 398). Structural equation modeling (SEM) was considered for statistical validation of hypotheses confirming that TL significantly reduces burnout. The results further indicated that resilience and role clarity mediate this relationship significantly. Lastly, the conditional indirect effect of intrinsic motivation was also confirmed. Our results provide meaningful insights to the hospital administrators to combat burnout, a critical reason for medical errors in hospitals. Further, by incorporating the TL framework, a hospital may reduce the risk of burnout (and, hence, medical errors); on the one hand, such a leadership style also provides cost benefits (reduced medical errors improve cost efficiency). Other different theoretical and practical contributions are discussed in detail.
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http://dx.doi.org/10.3390/ijerph191710941 | DOI Listing |
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Department of Dermatology, Venereology and Leprosy, JSS Medical College and Hospital, JSS Academy of Higher Education and Research (JSSAHER) (Deemed to Be University), Mysuru, Karnataka, India.
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National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA.
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View Article and Find Full Text PDFAm J Hum Genet
January 2025
Institute of Medical Genetics and Applied Genomics, University of Tübingen, 72076 Tübingen, Germany; Center for Rare Disease, University of Tübingen, 72076 Tübingen, Germany; Genomics for Health in Africa (GHA), Africa-Europe Cluster of Research Excellence (CoRE).
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View Article and Find Full Text PDFComput Biol Med
January 2025
Division of Electronics and Information Engineering, College of Engineering, Jeonbuk National University, 567, Baekje-daero, Deokjin-gu, 54896, Jeonju, Republic of Korea. Electronic address:
Kidney stone is a common urological disease in dogs and can lead to serious complications such as pyelonephritis and kidney failure. However, manual diagnosis involves a lot of burdens on radiologists and may cause human errors due to fatigue. Automated methods using deep learning models have been explored to overcome this limitation.
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December 2024
Laboratory of Behavioral Medicine, Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas-Palanga, Lithuania.
Background: Cardiovascular diseases such as coronary artery disease (CAD) have a high prevalence of psychiatric comorbidities, that may impact clinically relevant outcomes (e.g., cognitive impairment and executive dysfunction).
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