

Artificial Intelligence-Driven One-Year Mortality Risk Prediction
Thursday, March 12, 2026 8:45 AM to 9:15 AM · 30 min. (US/Pacific)
Level 3 | Murano 3304
Education Sessions
Artificial Intelligence in Healthcare
Information
This session presents Fraser Health Authority’s AI-driven one-year mortality risk model, designed to identify hospitalized patients at high risk of death within a year of admission. Built using more than 32 clinical and demographic variables, the model supports earlier palliative care interventions, smoother care transitions and reduced healthcare costs. Initial findings highlight the potential to improve patient outcomes by shifting from reactive to proactive care planning. Attendees will learn how predictive analytics can be effectively integrated into clinical workflows to enhance decision-making and optimize resource use.
Topic
Clinical AI Solutions for Care Delivery and Patient Outcomes
Target Audience
Chief Digital Officer/Chief Digital Health OfficerCMIO/CMOData Scientist
Level
Intermediate
Format
Case Study
CEU Type
ACPECAHIMSCMECNECPDHTSCPHIMSPMI/PDU
Contact Hours
1.00
Learning Objective #1
Describe the key variables and design elements used to develop a one-year mortality risk prediction model
Learning Objective #2
Explain how predictive analytics can support earlier palliative care interventions and care planning decisions
Learning Objective #3
Demonstrate how clinicians can use dashboard tools to identify high-risk patients and prioritize follow-up actions
Session #
159
Documents & Links
159 - Session Survey
