Predicting hospital admissions for ambulatory care sensitive conditions using primary care electronic medical record data: a multi-jurisdictional feasibility and validation study (Machine learning to predict hospital admissions)

Data to Enable a Learning Health System
In Progress
Electronic Medical Records, Hospital Admission, Machine Learning, Prediction
December 2020 - Undetermined | Funders: Health Data Research Network | Partners: Participating Practice-Based Research Networks: Manitoba: MaPCRen, British Columbia: BC-PHCRN, Ontario: UTOPIAN, EON, OPEN


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. 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 an avoidable hospitalization.

In The Media

Team Members

  • Andrew Pinto (PrincipalInvestigator)
  • Alex Singer (Manitoba lead) (Co-Investigator)
  • Sabrina Wong (BC lead) (Co-Investigator)
  • David Barber (Co-Investigator)
  • Simone Dahrouge (Co-Investigator)
  • MichelleGriever (Co-Investigator)
  • Michael Green (Co-Investigator)
  • Kathryn Nicolson (Co-Innvestigator)
  • Christopher Meaney (Co-Investigator)
  • Rahim Moineddin (Co-Investigator)
  • Kevin Thorpe (Co-Investigator)
  • Karen Tu (Co-Investigator)
  • Tyler Williamson (Co-Investigator)
  • Walter Wodchis (Co-Investigator)

Contact Information