Visual 2: Session learning short and long term goals and objectives

Session learning Goals:

Creativity

Human centred management 

Short term: brief hands on interactive exposure to descriptive models of clinical decision making in the pre AI and current AI era and challenges posed toward explainability and trust in blended learning ecosystems

More here: 

Blended learning ecosystem: https://pmc.ncbi.nlm.nih.gov/articles/PMC6163835/, 

Descriptive or predictive? https://sites.pitt.edu/~super1/lecture/lec53081/015.htm

Changing the game: http://www.pitt.edu/~super1/lecture/lec54091/001.htm

Long term: collaborate to potentially develop a special group of human centered healthcare managers trained in AI driven clinical decision making that takes into account an individual patient's internal medicine (anatomy physiology, pathology, organ systems) and external (organisational, public health, environmental exposure and community medicine) drivers to optimise their overall healthcare outcomes using tools such as creative critical realist heutagogy.

More here: 

Understanding clinical complexity in organ and organizational systems: https://pubmed.ncbi.nlm.nih.gov/37335625/

Narketpally syn (critical realist heutagogy) : https://pubmed.ncbi.nlm.nih.gov/40674544/

https://link.springer.com/chapter/10.1007/978-3-658-19925-8_10#:~:text=This%20chapter%20outlines%20the%20fundamental%20principles%20of,in%20creating%20a%20holistic%2C%20self%2Ddetermined%20learning%20environment.

Session learning objectives:

1) Demonstrate a hands on approach to AI driven clinical decision making utilising past cases as well as cases from the floor shared by the audience 

2) Discuss briefly the evolution of clinical decision making pre and post AI

3) Discuss issues around how to bridge the inferential gap between multiple stakeholders in clinical decision making such as patients, their relatives, health professionals and policy makers through AI driven explainability 

4) Discuss how to gain and maintain trust between multiple stakeholders through judicious optimization of intelligence driven explainability.