Predicting hospital admissions for ambulatory care sensitive conditions using primary care EMR data

Data to Enable a Learning Health System
In Progress
Artificial Intelligence, Data collection


Hospitalizations related to ambulatory care sensitive conditions (ACSC) are common, costly, to some degree preventable with high-quality primary care, and are thus used as a health system performance indicator in Canada. We aim to develop and evaluate predictive algorithms for one-year non-elective ACSC hospitalizations among community-dwelling adults using primary care EMR data in British Columbia, Manitoba and Ontario. This is a multi-jurisdictional feasibility and validation study.


Our algorithm could be used by primary care providers to identify high-risk patients who may benefit from more proactive models of care aimed at preventing health decline and the undue distress of avoidable hospitalization.

Team Members