Intelligent Decision Support Systems for Prioritization in Health Care

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A waiting list is a queue of individuals who are on a list awaiting a service that is often in short supply. Such lists exist in many service-providing settings such as banks, retail stores, hospitals, and health and social services clinics.The challenge is to define the individual's order of priority to receive the service. The process of ranking individuals on the waiting list according to predefined criteria (e.g. importance and significance) is referred to as prioritization. In the context of real-world, making prioritization decisions happens under uncertainty in complex and dynamic systems (such as healthcare and social services) and requires considering big amount of data that we have, associated risks, multiple (sometime conflicting) objectives and supply constraints, is thus, extremely challenging. The overarching objective of this research program is to better understand and analyze real-world prioritization problems (e.g., in primary healthcare settings), and to develop innovative solutions for prioritization challenges by integrating operational research (OR) with artificial intelligence (AI) methods. This research program has three research directions. In the Research Direction 1, we will develop robust, data-driven optimization solutions for prioritization decisions under uncertainty and in dynamic and complex environments. In the Research Direction 2, we will develop interpretable solutions for prioritization, using both OR and AI methods. Designing interpretable solutions for prioritization is important because such solutions make it possible for the user to understand the logic behind the decisions made. This can increase users' trust in the developed solutions and this is important in critical settings such as healthcare. Lastly, there has been an increasing interest in business and research regarding big data and the potential value of its use. Statistical and optimization methods can help in analyzing these big data. In the Research Direction 3, using these methods we will develop hybrid solutions to deal with big data in prioritization. The aim of this research program is to lead ground-breaking advances in the field of OR by integrating AI methods to develop innovative solutions for prioritization problems, and applying them in the primary healthcare setting. Each research direction has clear intended milestones and outputs that we will use to assess progress and ensure we meet our objectives as planned. This program's impact will be measured by: (a) contributions to the methodology and knowledge in the OR and AI fields; (b) number of publications in top tier scientific journals; (c) number of highly qualified personal trained and their progress; (d) the effective transfer of the developed knowledge and insights to other researchers and also decision makers in practice; and ultimately (e) the deliverables in the short and long terms will lead to academic, economic, social, training and health-related impacts in Canada.