EMHI Rounds

Hi all, Join us for a talk on core topic, a journal club, and a case study in health IT:  

via Teams : 2023-2024 link  

9am: Developing an emergency department crowding dashboard: A design science approach By: Dr. Saleh Alsaeed, health informatics emergency medicine fellow

Learning objectives: 

1. What is the method for constructing a dashboard that gathers real-time information about
Emergency Department crowding?
2. How can the dashboard be designed and organized based on functional and non-functional requirements?

​Question: What are the crowding indicators that need to be measured and displayed on the dashboard?

 10 am – Enabling Precision Dialysis Using IVC Collapsibility AI-Driven Wearable Ultrasonography By: Mohamed Elahmedi, MBBS PMP MSc(c)

Learning Objectives:

1. At the end of this presentation, attendees will recognize the value of autonomous IVC collapsibility assessment in emergency and elective settings.

2. Attendees will have learned of the role of IVC collapsibility assessment in volume depletion and overload, acute and chronic.

Question: How reliable is IVC collapsibility assessment in emergency settings? What are the potential indications for IVC collapsibility assessment?

11 am – Research Proposal: Development and Implementation of Personalized Waiting Time Prediction Tool for Emergency Department (ED) Rooms, By: Hossein Naseri PhD

Learning Objectives:

1. How natural language processing (NLP) can aid in extracting pain information from radiography images of patients with bone metastases.

2. How to integrate NLP and radiomics for predicting pain using radiography images of patients with bone metastases.

3. Potential solutions for personalized waiting time estimation.

Question: How can advanced technologies like natural language processing (NLP) be harnessed to improve the analysis of radiography images for patients with bone metastases, particularly in the context of pain prediction? What potential benefits or challenges do you foresee in implementing a personalized waiting time prediction tool in Emergency Department (ED) rooms, and how might the integration of technology play a role in addressing these factors?

BIO: Dr. Saleh Alsaeed, health informatics emergency medicine fellow, department of emergency medicine, McGill University. He is from pediatric background, finished his pediatric medicine board back home in Saudi Arabia, and pediatric emergency medicine fellowship in McMaster prior to be enrolled in his current fellowship. Dr. Saleh has an interest in workflow and human behavior

BIO: Hossein Naseri is a Medical Physics Ph.D. graduate from McGill University under the supervision of Dr. John Kildea. Hossein's doctoral research focused on using natural language processing and radiomics to predict pain in radiography images of patients with bone metastases. Today, Hossein proposes a pilot study, the development and implementation of a personalized patient waiting time prediction tool for emergency department (ED) rooms.

BIO: Dr. Mohamed Elahmedi is a general practitioner and an experienced clinical research project manager with a master’s degree in digital health innovation from McGill University. Having received surgical and family medicine training, Dr. Elahmedi conducted clinical investigation studies on endoscopic bariatric therapy devices. His interdisciplinary knowledge and experience align him with clinical research, ethics principles, medical technology and AI in Medicine project management. Dr. Elahmedi strongly believes that AI is the key to providing precise, personalized healthcare.