title: Predicting patients with high risk of becoming high-cost healthcare users in Ontario (Canada).
abstract: Literature and original analysis of healthcare costs have shown that a small proportion of patients consume the majority of healthcare resources. A proactive approach is to target interventions towards those patients who are at risk of becoming high-cost users (HCUs). This approach requires identifying high-risk patients accurately before substantial avoidable costs have been incurred and health status has deteriorated further. We developed a predictive model to identify patients at risk of becoming HCUs in Ontario. HCUs were defined as the top 5% of patients incurring the highest costs. Information was collected on various demographic and utilization characteristics. The modelling technique used was logistic regression. If the top 5% of patients at risk of becoming HCUs are followed, the sensitivity is 42.2% and specificity is 97%. Alternatives for implementation of the model include collaboration between different levels of healthcare services for personalized healthcare interventions and interventions addressing needs of patient cohorts with high-cost conditions.
authors.affiliation: Senior Methodologist, Health Analytics Branch, Ontario Ministry of Health and Long-Term Care Toronto, ON.
authors.forename: Yuriy
authors.initials: Y
authors.lastname: Chechulin
authors.affiliation: Methodologist, Health Analytics Branch, Ontario Ministry of Health and Long-Term Care Toronto, ON.
authors.forename: Amir
authors.initials: A
authors.lastname: Nazerian
authors.affiliation: Senior Methodologist, Health Analytics Branch, Ontario Ministry of Health and Long-Term Care Toronto, ON.
authors.forename: Saad
authors.initials: S
authors.lastname: Rais
authors.affiliation: Senior Manager, Health Analytics Branch, Health System Information Management and Investment, Ontario Ministry of Health and Long-Term Care Toronto, ON.
authors.forename: Kamil
authors.initials: K
authors.lastname: Malikov
mesh_terms: D017281:Cost of Illness; D005260:Female; D005544:Forecasting / Q000379:methods; D017048:Health Care Costs / Q000639:trends; D006296:Health Services / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016017:Odds Ratio; D009864:Ontario
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