Tuesday, March 22, 1:00pm - 2:00pm (EDT)
Scientific Faculty
Host
Prof Teodora Nicolescu
President of the European Society of Computing and Technology in Anesthesiology and Intensive Care
Oklahoma University Health Sciences Center
Speakers
Prof. Elena Giovanna Bignami
Director of the School of Specialization in Anesthesia Intensive Care and Pain
President of the Nursing Course, Department of Medicine and Surgery
University Hospital of Parma
Dr. Matthieu Komorowski
Consultant in Intensive Care
Charing Cross Hospital – Imperial College London
Target Audience
Anesthesiologists, residents, nurse anesthetists
Key points
The webinar will address :
Utilization of high quality data collection as predictive models for perioperative patient management
Efficient and discrete data sampling for perioperative patient course assessment
Extrapolate the current knowledge for future data modelling and more expansive AI use(Watson)
About this webinar
The webinar will inform the audience of the newest predictive models for perioperative patient management and how to effectively use data collection to review and improve both quality of care and patient safety.
Content
Artificial intelligence has been an adjuvant for a multitude of scientific fields, anesthesiology being one of those. The aspects AI applications that are most useful are:
Depth of anesthesia monitoring and control of anesthesia
Event and risk prediction
Ultrasound guidance
Pain management
Operating room logistics
Artificial intelligence has the potential to not only impact the perioperative management but the intensive care unit as well.
The webinar will describe:
The importance of appropriate of data mining and collection, using such data for predictive modelling that aids not only in the perioperative patient management, pain control or ICU course but also in ultimately identifying risk during the preoperative patients’ evaluation.
As AI is progressing to supercomputer use, anesthesiologists will have to adapt to a new way of practice and be aware of both the advantages and limitations of AI use.
Learning Objectives
This webinar will enable anaesthesiologists and intensivists to:
Identify which data collection most effectively can aid in perioperative patient management
Describe the predictive modelling , its utilization and its impact on quality of care
Apply the principles of predictive modelling to daily patient care management
Practical skills to be acquired after attending to this Webinar
The user is able to:
Create predictive models which are effective aids
Demonstrate the impact of AI use in improving quality of care
Obtain new skills related to data mining and collection
Affective skills acquired after attending to this Webinar
The participants are aware of:
Importance of the specifications and use of data collection
Advocating for patient safety of the use of predictive models
Need to reflect on the future shaping of the field of anesthesiology by new supercomputers AI such as Watson
The participant will be able to:
Evaluate the impact of predictive models on patient management
Specify challenging aspects or limitations of models
Test the resilience of predictive models in long term utilization
Needs analysis
Anesthesiology as a field is well positioned to potentially benefit from advances in artificial intelligence as it touches on multiple elements of clinical care, including perioperative and intensive care, pain management, and drug delivery and discovery. The webinar is a scoping review of the literature at the intersection of artificial intelligence and anesthesia with the goal of identifying techniques from the field of artificial intelligence that are being used in anesthesia research and their applications to the clinical practice of anesthesiology.
Technical Settings
This webinar is available on PC, Tablet and Smartphone.
For the best viewing experience, a high-speed internet connection is required.
This Webinar is supported an unrestricted educational grant by GE Healthcare.
ESAIC, esa.galleries@gmail.com