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Building on Routinely Collected Health Data for Precision Population Care in Inflammatory Bowel Disease
Gemma Postill1, Eric I Benchimol2
1Gemma Postill is an ICES student at ICES Central and an MD/Phd candidate at the Institute for Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON.
Abstract:
The growing burden of multimorbidity among individuals with inflammatory bowel disease (IBD) presents a critical challenge for health systems, as it signals the need to shift from single-disease-focused care to integrated multimorbidity-focused care. Recent Canadian population-based research has demonstrated that multimorbidity in IBD follows distinct patterns and that these patterns can be leveraged using machine learning (ML) to predict premature mortality. This column draws on two recent studies evaluating populations with IBD - one identifying multimorbidity clustering among individuals with IBD and another applying ML to predict premature death from non-IBD chronic conditions - to explore how these findings can inform health system planning, clinical care and policy decision-making.
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