Trajectory Analytics for Data-Driven Predictions and Sequential Decision-Making Under Sequence Uncertainty

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Background. Healthcare trajectories are a series of healthcare operations processes (such as prevention, diagnostic, and treatment activities) and clinical pathways (such as disease progression) executed in the healthcare system. Identifying, analyzing, and improving the healthcare trajectories has a significant impact on the healthcare costs and patient outcomes by contributing to a) improved system performance, b) controlling disease progression, and c) improved transparency. Healthcare trajectories, which are hidden in the healthcare system's event logs, are highly complex and subject to tremendous variations. Therefore, a data-driven approach, which I refer to as TRajectory Analytics (TRA), is crucial for the characterization, analysis, and improvements of healthcare trajectories through the utilization of the enormous healthcare data and the exponentially-growing computing power we have seen over the last decade. TRA aims at analyzing data and extracting hidden trajectory patterns to generate knowledge regarding the complex sequence of events and actions. Nevertheless, the field of TRA, especially in the healthcare domain, is still in its infancy stages and struggles with various limitations in terms of providing reference data-driven modeling frameworks that encompass multiple analytical scopes, including descriptive, predictive, and prescriptive tasks. Objectives. Addressing the referenced methodological and practical gaps in the context of healthcare TRA is highly aligned with my overarching long-term objectives in developing analytical frameworks for blending predictive and prescriptive analytics to improve data-driven decision-making. In the short-term, I will undertake the following steps as the research objectives over the proposed five-year program of research: 1.Predictive TRA: Developing novel method(s) for discovering and predicting healthcare trajectories from data. 2.Descriptive TRA: Proposing methods for summarizing, visualizing, and reducing the complexity of the identified trajectories in (1) by identifying the clusters of the most common trajectories in optimal fashions. 3.Prescriptive TRA: Proposing optimization frameworks and solution procedures for sequential decision-making based on the explicit utilization of the information gained in (1)-(2). This program is possible through the application of my methodological expertise/experience in healthcare analytics and the availability of rich datasets unique to my lab. Impact. On the scientific side, this research program will deliver several novel methodologies in TRA and illustrates explicit connections between the various scopes of data analytics. On the practical side, it provides the healthcare sector with the tools, scalable to other sectors, that facilitate data-driven decision-making and evidence-based leadership. On the pedagogical side, it provides unique opportunities for training Canadian-trained HQPs in Artificial Intelligence (AI) with expertise in TRA.