CCHE Seminar: A population perspective on high resource users in Ontario: Evidence from Ontario
University of Toronto
Friday, March 29th, 2019, 10:30 AM – 12:30 PM, HS 412 (155 College Street)
Abstract: The majority of health care spending is concentrated among a small proportion of the population, irrespective of the type of health care system in which the costs are incurred. Within the single-payer care system in Ontario, Canada, the top 5% of health care users account for almost 50% of total health care spending. Furthermore, most patients in the health system are likely to struggle with multiple chronic disease diagnoses by the end of their life. This talk will cover a range of studies using different analytic approaches and data sources focused on high resource users (HRUs) to capture the upstream determinants of high resource use and suggest how that information can be used to guide future direction in health system planning. Several studies will be presented and summarized into three sections (1) Linkage studies that use population-based data sources and person-based costing methods to capture social, clinical and behavioural characteristics associated with future high resource use; (2) Development and validation of a population-based risk prediction tool for transition to the top 5% of health resource users across the major sectors of health care spending, including inpatient hospitalizations, physician visits, complex continuing care, long-term care, home care services, and assistive devices; (3) Demonstrate how population models can be used to inform health system planning for HRUs.
Dr. Laura Rosella is an Associate Professor in the Dalla Lana School of Public Health at the University of Toronto where she holds a Canada Research Chair in Population Health Analytics and is the Scientific Director of the Population Health Analytics Lab. She is the Site Director for ICES at U of T and a Faculty Affiliate at the Vector Institute for Artificial Intelligence. Dr. Rosella’s research is focused on using analytics and data to inform system actions that improve population health, health equity and fiscal sustainability. She specializes in the development of population risk tools to inform decision-making focused on applications for the reduction of chronic disease and premature mortality.