Grant: Fellowship: Bisby (Prize)

The impact of chronic medical conditions such as hypertension, diabetes, heart disease, and stroke are a major concern for health systems in Canada and abroad. These conditions often occur together and are associated with poor health outcomes including hospitalization and death. Readmission to hospital is also common and results in adverse outcomes and high costs in patients with chronic disease. The ability to accurately predict readmission after hospital discharge is important, as it would enable health care workers to focus post-discharge interventions on patients at highest risk of readmission. Unfortunately, validated prediction tools are either not currently available or are limited by the use of a single clinical data source. Linking population-based survey data with national administrative data will provide a detailed and rich data source that will enable exploration of patient characteristics, clinical factors, and environmental factors that place patients at risk for hospital readmission. It will also be used to develop a tool to predict the risk of readmission amongst Canadians with chronic disease. This novel methodology has important implications for patient care and health policy. The development of a prediction tool will not only assist clinicians and hospital administrators in identifying patients at risk of readmission, it will also enable discharge instructions and interventions to be targeted to reduce this risk and ultimately improve patient care.