- Role
- Finalist
- Region
- Bay Area
- Pronouns
- he/him/his
Bahaa Al Deen Alsaadi
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Project
Bits to Bedside: An Ingenious Exploration of Machine Learning in Triage Assessments (Continued...)
Project #4303
On average, Canadians can wait up to 7.6 hours in the emergency room before being discharged (Canadian Medical Association, 2025). This project uses artificial intelligence (AI) to help solve this issue by targeting one part of the hospital process at a time: the triage assessment process in the emergency room, where incoming patients are ranked based on urgency levels. The AI model is meant to work alongside real nurses by assessing less urgent patients through a user-friendly self-check-in kiosk, leaving the real nurses more time to focus on more urgent patients. The previous prototype was mainly improved by applying different data-weighing techniques and adding options for image inputs to help the model predict more accurate results. Wait times are estimated to drop by at least 44% for urgent patients and 28% for less severe patients if these kiosks are added to hospitals using the planned precautions to avoid misuse.
- Challenge
- Digital Technology
- Category
- Senior
- Type
- Innovation
- Project Partner
- Yes