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.

Download full-text PDF

Source

Publication Analysis

Top Keywords

risk management
24
management organisations
8
management
6
risk
5
evolution risk
4
management programme
4
programme living
4
living organisms
4
organisms evolve
4
evolve continue
4

Similar Publications

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 PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!