Living organisms must evolve to continue to exist, let alone thrive. This situation is especially true when organisms are faced with a challenging environment, such as the risk management landscape. Risk management organisations need to adopt a continuous adaptation strategy to manage the dynamic threat landscape and build resilience into the risk management function. However, risk management teams do not have the luxury of being able to wait for adaptations to occur naturally because of the rapid rate of change. Risk management organisations should consider embracing strategic evolution to continuously transform their teams to handle new challenges created by societal and technological trends.
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Pilot Feasibility Stud
January 2025
Advocate Christ Medical Center, Advocate Health, Oak Lawn, IL, USA.
Background: Hypertension is the leading risk factor for cardiovascular disease (CVD). Despite advances in blood pressure management, significant racial and ethnic disparities persist, resulting in higher risks of stroke, heart disease, and mortality among non-White populations. Self-measured blood pressure (SMBP) monitoring, also known as home blood pressure monitoring, has shown promise in improving blood pressure control, especially when combined with feedback from healthcare providers.
View Article and Find Full Text PDFTrop Med Health
January 2025
Department of Community Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Background: Neurobrucellosis, a serious central nervous system infection caused by Brucella species, presents significant challenges due to its diverse clinical manifestations and the risk of long-term complications and poor outcomes. Identifying predictors of adverse outcomes is critical for improving patient management and overall prognosis.
Objectives: This study aimed to evaluate the long-term morbidity and mortality associated with neurobrucellosis and to identify key predictors of adverse outcomes.
BMC Med Inform Decis Mak
January 2025
Kenya Medical Research Institute- Center for Global Health Research (KEMRI-CGHR), P.O Box 1578-40100, Kisumu, Kenya.
Background: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities.
Methods: LDD was defined as a diarrhea episode lasting ≥ 7 days.
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