This study considers an expanded meaning of "uncertainty" as it affects decision-makers. The definition adopted is based on a decision-maker who is uncertain, i.e. aware of the insufficiency of her knowledge for the purpose of rationally determining which option to choose. A taxonomy of uncertainties is developed from this definition. The first stage is a Generalized Decision Model, which expands on a standard decision model often assumed in technical works by allowing uncertainty over components of the model that are assumed to be perfectly known in the standard model. These additional potential "subjects" of uncertainty include the feasibility of options, the authority of the decision-maker to effect a choice, membership of and probability distributions over the set of possible future states of the world, and considerations about how the consequences are to be valued. The taxonomy also describes possible "sources" of uncertainty, dividing them into characteristics of the world (e.g. variability), evidence the decision-maker has (e.g. ambiguity or imprecision), or characteristics of the decision-maker himself. Other important ways in which uncertainties can vary is whether they are hard (irreducible in principle) or soft, whether a decision is unique or repeatable, and the role time has in the decision and in the resolving of the uncertainties. A finding of this work is that many uncertainties in addition to the uncertainty in the standard decision model over the future state of the world can keep a procedure for implementing rational choice from being decisive, thus requiring another (nonrational) process to complete the selection of an option. Other insights: (1) Deciding is only part of being rational, and in many instances is not the most important part. (2) Uncertainty may complicate decision-making, but is by no means always bad for the decision-maker. (3) Rationality is inescapably subjective in any implementation. (4) True "decision under certainty" does not exist. (5) Uncertainties vary sufficiently that no single treatment can be prescribed; it is hoped that this work contributes to a survey of the territory of uncertainty that facilitates Smithson's (1988) "suburbanization" or subdivision into smaller tracts to be developed individually.

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A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service.

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