Continuity of care (COC) refers to the delivery of seamless services, continuous caring relationships, and information sharing across care providers. A disruption in COC—that is, care fragmentation (CF)—is an important cause of inefficiency in the Canadian healthcare system; such disruption leads to increased healthcare costs and reduced quality of care. Addressing this issue is particularly challenging among older adults, who often have medically complex needs; such patients can require many care transitions across multiple care settings. An effective strategy for COC improvements is to optimize discharge planning among older adults. However, this is hampered by the imperfect understanding of older patients’ needs, which are associated with their health complexity. Therefore, making early predictions about the patients’ health complexity and incorporating this information into discharge planning decisions can potentially improve COC. In this thesis, I develop data-driven predictive–prescriptive analytics frameworks that leverage machine learning (ML) approaches and a rich, massive set of longitudinal data collected over a decade. The first essay in this dissertation studies the early prediction of older patients’ complexity in hospital pathways using ML. It also examines whether we can conduct accurate prognostics with current information on patient complexity. The second study examines how two common measures of patient complexity—multimorbidity and frailty—concurrently affect post-discharge readmission and mortality among older patients. It also investigates the dependency of the outcomes on other essential socio-demographic factors. Finally, the third study examines the feasibility of predicting patients at risk of fragmented readmission—that is, readmission to a different hospital than the initial one. It uses this predictive information to derive optimal policies for preventing CF while addressing disparities in the decision-making process. The findings highlight the feasibility, utility, and performance of predicting patient complexity and important adverse outcomes, potentially undermining COC. This thesis shows that advanced knowledge and explicit utilization of this information could support decision-making and resource planning toward a targeted allocation at the system level; moreover, it informs actions that affect patient-centered care transition at the service level to optimize patient outcomes and facilitate upstream discharge processes, thereby improving COC. / Thesis / Doctor of Philosophy (PhD) / The aging population in Canada is growing significantly relative to the population as a whole, and several challenges are involved in providing aging people with proper healthcare services. One of these challenges is disruptions in continuity of care. Older adults are often medically complex or frail; they may have multiple diseases and require many care transitions across healthcare settings. Poor continuity of care among these patients leads to health deterioration during care trajectories, resulting in reduced quality of care and increased healthcare costs and inefficiencies. This thesis includes three essays that provide practical insights and solutions regarding the issue of continuity of care disruptions, spanning from predicting the issue to strategies to prevent it in a data-driven manner.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27810 |
| Date | January 2022 |
| Creators | Ghazalbash, Somayeh |
| Contributors | Zargoush, Manaf, Health Management |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.008 seconds