It is that time of the year again where the daysseem to be longer than usual, we seem to be more tired with every passingminute and we have that nagging feeling the festive season break is just toofar away. My observation of colleagues from all disciplines, differenthospitals or practices and different geographical locations is the same… everyoneis hanging on by a thread.Read more: Burnout in health care professionals… a ‘burning’ issue!
There are two subsets of AI: machine learning and deep learning.1 Machine learning involves algorithms using pattern recognition to solve tasks, for example recognizing symptoms of breast cancer in lung scans. Deep learning involves neural networks using the human brain model and these algorithms are used for creating medical images. AI is already playing an important role in health and indeed in diabetes management. Examples of this include the amazing new technologies in our current insulin pump options or the fundal cameras paired with AI interpretation we use at the CDE for retinopathy screening. AI technology has assisted the radiology departments in a variety of ways…Read more: AI in Radiology: is there still a role for the radiologist?
I would like to firstly dedicate this blog to two members of our team who have experienced losses of family members. We, at the CDE, are a family as much as a team, and these losses have shaken us all to the core. I want them to know that we are praying for their comfort and strength during these trying times.Read more: Is the holistic management modal ('everyone under one roof') really a game changer?
Type 2 diabetes mellitus is a largely unseen pandemic. The International Diabetes Federation estimates that 537 million people were living with diabetes worldwide in 2021 and around 4.2 million of these were in South Africa.1 The economic burden of diabetes-related complications on health care sectors and society is profound. The physical, psycho-social and emotional toll on people with diabetes is even more concerning. As health care practitioners, we need to focus on achieving glycemic and other risk factor targets quickly and safely…
Pharmacological agents used for diabetes have evolved over the years to address more of the 11 pathophysiological pathways causing type 2 diabetes.2 The advent of sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide 1 (GLP-1) receptor agonists now allows us to address more than just glycemic control and this sets them apart from older agents. Thus, health care practitioners can now look at using these agents with enthusiasm.