Background. Advances in healthcare have resulted in a growing population of Children with Medical Complexities (CMC). Medical management of the complex needs of CMC has traditionally taken place in hospital settings, such as clinics, emergency departments, and hospital wards, by specialized teams of healthcare clinicians. While access to expert-level care is necessary, the hospital-based model of care is expensive and inconvenient and has resulted in harms such as medical errors conferred upon CMC. Models of care that allow for expert-delivered hospital-to-home care for CMC are needed. The aim of this dissertation was to investigate the needs, performance, and perceptions of CMC families and clinicians when using a hospital-to-home digital health system called DigiComp Kids. Methods. This study was guided by A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies, which attends to technological, human, and contextual variables influencing virtual care. A scoping review was conducted to provide an overview of how digital healthcare has been used with medically fragile children, before co-designing the DigiComp Kids system with CMC family members and clinicians. Usability testing of DigiComp Kids was conducted measuring user effectiveness, efficiency, satisfaction, and experiences. Results. Across studies in this dissertation, technological, human, and contextual factors each played a role in the usability of digital health systems, including DigiComp Kids. In the scoping review, these factors influenced the acceptability, accessibility, and implementation success of digital healthcare systems for CMC. During DigiComp Kids co-design and usability testing, participants emphasized how these factors affected their willingness to engage with the system, how it fit into their lives and workflows, and where improvements could be made. Conclusions. This study highlights the importance of engaging with end-users as well as attending to technological, human, and contextual factors when designing and testing digital health systems. / Thesis / Doctor of Philosophy (PhD)

The Engineer to Order (ETO) model is used by a significant number of manufacturers across multiple sectors. Indeed, ETO firms comprise approximately one fourth of all North American manufacturing and are growing at a rate of twenty percent (Cutler [10]). In the ETO environment, the engineering process is the largest controllable consumer of lead-time in ETO firms. Since one half of the total lead-time is typically consumed by the engineering process, it is a critical task to accurately set the due date and later sequence the jobs in queue. However, unlike other manufacturing models such as Make to Stock or Make to Order, the product for each order is unique. Hence the resulting design is not realized until after the engineering process has been completed an the only information available is limited to information which has been gathered during the quoting stage of the order fulfillment process. These facts drive uncertainty into the front-end process. Therefore, the question becomes how does one predict the job difficulty let alone the due date in a complex transactional process when the job has not even been designed yet? In regard to the state of the art for the topic of design complexity, due date setting and sequencing, there is an abundance of research. Unfortunately little of it is aimed at the ETO environment. Additionally, there is not an agreed upon way in the literature to define complexity nor is there one overarching methodology for assessing complexity. Therefore, this research investigates the topics of job complexity, due date setting and job sequencing in the context of the Engineer to Order model. Analytical research is conducted with in conjunction with multiple ETO firms and several common factors are identified which drive complexity in the ETO engineering environment. These complexity factors are can be used is as an input to the accurate prediction of flow times for the ETO engineering process as well as sequencing. The research results in new innovative approaches for complexity assessment, due date setting and sequencing which outperform existing approaches.

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