Grant: Travel Awards - Institute Community Support

Despite substantive financial investments in the Canadian health care system, the expected impact in terms of access, equity and health outcomes has yet to be achieved. There is an urgent need for innovative and effective approaches to financing, organizing, and delivery of health care services in Canada. Health research has an important role in accelerating this health care system transformation. However, population health and health services research that uses administrative data often does not integrate the perspectives of multiple and diverse actors. These actors have extensive knowledge of the health sector, which could shape how research is conducted. This applied research project incorporated perspectives of health sector actors in the development stage of a secondary data analysis project. The overall objective of this project was to identify research considerations and/or limitations that could then be addressed before the development of a predictive model for chronic disease management. This study used a sequential mixed methods study design to explore the adoption and use of data-driven solutions, such as those using artificial intelligence, in the context of population health management in Ontario. The project included semi-structured interviews with public health and health systems actors. Using reflexive thematic analysis, interviews were inductively coded, research considerations were extracted and a descriptive summary was added. Considerations were then categorized into high-level domains and mitigation strategies were proposed for each consideration. The feasibility of implementing each mitigation strategy was determined, using the development of a predictive model for kidney care management in Ontario as a use case. This study presents a novel approach to conducting secondary data analysis. This approach can be applied broadly; thus, strengthening health services research by ensuring findings are relevant and applicable to health care systems.