Online Course
AI-Based Clinical Decision Support in Diabetes Care
Revolutionizing Diabetes Care: Harnessing AI-Based Clinical Decision Support for Personalized Management and Enhanced Healthcare Outcomes
Rating: 4.5/5
CME/CPD: 1 Credit
Duration: 1 hour
About this course
Artificial intelligence-based Clinical Decision Support tools (AI-CDS tools) hold considerable promise for advancing diabetes care.
The application of artificial intelligence systems in healthcare enables the performance of complex tasks in a manner reminiscent of human problem-solving.
Together, these innovations in AI-CDS tools signify a transformative potential for improving the management of diabetes by providing tailored, timely, and intelligent support to healthcare professionals and patients alike.
Faculty
Prof. Moshe Phillip, MD
Director of the Institute of Endocrinology and Diabetes, the National Center for Juvenile Diabetes at Schneider Children’s.
Revital Nimri, MD
Senior pediatric endocrinologist and diabetes expert focusing solely on new and innovative technologies for treatment of type 1 diabetes.
Ariana Pichardo-Lowden, MD
Associate Professor of Medicine & Public Health Sciences, Clinical Endocrinologist, and Diabetes Researcher at Penn State University, PSH Hershey Medical Center
Learning Outcomes
Understand how AI tools can complement or enhance the traditional roles of health care professionals in managing diabetes.
Examine how AI-based clinical decision support systems provide personalized, actionable recommendations to optimize care for individual patients.
Identify the application of AI tools in the inpatient setting, with the aim of reducing the length of hospital stays and potentially improving overall patient outcomes.
Lessons in this course
To learn more about this topic, we look at three very interesting presentations given at the ATTD 2022 conference around the subject of Clinical Decision Support Systems.
AI-Based Decision Support System
Behavioral Advice Given by Artificial Intelligence-Based Decision Support
Glycemic Care Optimization in the Hospital using Clinical Decision Support Helps Reduce Length of Stay
