MIT Theses https://hdl.handle.net/1721.1/7582 2020-12-04T13:20:01Z 2020-12-04T13:20:01Z Individual and organizational Uses of Evidence-Based Practice in healthcare settings Fingerhut, Henry Alan. https://hdl.handle.net/1721.1/128641 2020-11-25T03:11:46Z 2020-01-01T00:00:00Z Individual and organizational Uses of Evidence-Based Practice in healthcare settings Fingerhut, Henry Alan. In the three decades since its introduction, Evidence-Based Practice (EBP) has become standard clinical practice and the subject of targeted interventions at all levels of the health system. Despite its prevalence, EBP is frequently challenged on philosophical, practical, empirical, and normative grounds. And EBP is often underused in practice relative to the considerable investment in training and sophisticated organizational interventions to implement EBP. In this dissertation, I identify what the concept of EBP means to health system stakeholders as a partial explanation for this persistent gap in EBP use and implementation outcomes. Through interviews with clinicians and healthcare administrators, I identify how providers and organizations use EBP in practice to clinical ends and in inter-professional relationships. First, I find that in contrast to the theoretical model, stakeholders vary in how they operationalize EBP for individual-level clinical use.; Stakeholders endorse a range of what I call implicit mental models of EBP that imply different approaches to clinical decision-making. Respondents' implicit mental models of EBP each emphasize an incomplete aspect of the full EBP model: Resource-Based EBP emphasizes specific evidence artifacts, Decision-Making EBP emphasizes the decision-making process, and EBT-Based EBP emphasizes specific Evidence-Based Treatments. These implicit models represent the decision inputs, process, and outputs, respectively. Second, I describe how and why healthcare organizations conduct EBP interventions, despite its initial design as an individual-level clinical decision-making model. I document a range of different organizational EBP activities and interventions, including disseminating resources, training providers, and implementing local standards. These organizational EBP activities both support individual EBP use and address broader organizational ends, which may conflict.; Finally, EBP takes on social and inter-professional meanings beyond its intended scope as a clinical decision-making model, which emerge in context and affect how providers understand and use EBP. Specifically, providers may renounce their standing to evaluate evidence, demonstratively use EBP, and administrators claim standing to evaluate evidence. This dissertation therefore demonstrates the varied uses of EBP that emerge in practice, contributing to our understanding of the challenges and contradictions that arise in applying general knowledge to individual cases and systematizing strategies for the same at the organization level. Thesis: Ph. D. in Engineering Systems: Technology, Management, and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, February, 2020; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (pages 135-145). 2020-01-01T00:00:00Z Effects of hardware and soft features on the performance evolution of low-carbon technologies Klemun, Magdalena Maria. https://hdl.handle.net/1721.1/128640 2020-11-25T03:25:53Z 2020-01-01T00:00:00Z Effects of hardware and soft features on the performance evolution of low-carbon technologies Klemun, Magdalena Maria. This dissertation studies how physical and non-physical features of low-carbon technologies evolve and influence performance evolution. This fundamental question about the role of hardware- and non-hardware ('soft') innovations in technological progress remains largely unanswered despite the societal importance of improved technology. Multiple low-carbon technologies exhibit rising shares of soft costs, and understanding their determinants is thus critical to support climate mitigation. However, building this understanding is challenging. Technologies evolve through multi-faceted knowledge-generating processes, in which both endogenous factors, such as a technology's design, and exogenous factors, such as policies and research, play roles.; To capture this complexity, a new conceptual and quantitative model of technology performance evolution is developed, where performance change (e.g., cost change) is the outcome of changes in physical and non-physical ('soft') features ('variables'), both of which can affect the performance of hardware and processes needed to deploy technologies. While physical variables -- material usage ratios, efficiencies --; describe the tangible aspects of technologies, soft variables (e.g., task durations, wages) characterize the performance of intangibles, including deployment processes and services. In contrast to physical variables, soft variables can change after the factory gate due to locational differences in technology management or labor costs. By defining hardware and soft performance as functions of both hardware and soft variables, and separating their contributions to cost change when multiple variables change, this framework disentangles the effects of physical and non-physical forms of improvement at multiple conceptual levels --; from changes in hardware or soft features, to the specific physical and non-physical innovations that drive these changes, to the higher-order improvement processes in which many innovations originate (e.g., research and development). This approach addresses shortcomings in current methods to analyze and track cost change in technologies, which often treat the performance of hardware (e.g., equipment costs) and of deployment processes (e.g., soft costs) separately. However, features of hardware not only affect the cost of equipment, but also the cost of deploying this equipment, and accounting for such interdependencies can change assessments of the sources of past and future technology improvement ... Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, February, 2020; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (pages 295-328). 2020-01-01T00:00:00Z Market design opportunities for an evolving power system Schneider, Ian Michael. https://hdl.handle.net/1721.1/128639 2020-11-25T03:20:25Z 2020-01-01T00:00:00Z Market design opportunities for an evolving power system Schneider, Ian Michael. The rapid growth of renewable energy is transforming the electric power sector. Wind and solar energy are non-dispatchable: their energy output is uncertain and variable from hour-to- hour. New challenges arise in electricity markets with a large share of uncertain and variable renewable energy. We investigate some of these challenges and identify economic opportunities and policy changes to mitigate them. We study electricity markets by focusing on the preferences and strategic behavior of three different groups: producers, consumers, and load-serving entities. First, we develop a game-theoretic model to investigate energy producer strategy in electricity markets with high levels of uncertain renewable energy. We show that increased geographic dispersion of renewable generators can reduce market power and increase social welfare. We also demonstrate that high-quality public forecasting of energy production can increase welfare. Second, we model and explain the effects of retail electricity competition on producer market power and forward contracting. We show that increased retail competition could decrease forward contracting and increase electricity prices; this is a downside to the general trend of increased access to retail electricity competition. Finally, we propose new methods for improving demand response programs. A demand response program operator commonly sets customer baseline thresholds to determine compensation for individual customers. The optimal way to do this remains an open question. We create a new model that casts the demand response program as a sequential decision problem; this formulation highlights the importance of learning about individual customers over time. We develop associated algorithms using tools from online learning, and we show that they outperform the current state of practice. Thesis: Ph. D. in Social and Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, February, 2020; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (pages 117-126). 2020-01-01T00:00:00Z Variability in the emissions savings potential of battery electric vehicles across regions and individuals Miotti, Marco,Ph. D.Massachusetts Institute of Technology. https://hdl.handle.net/1721.1/128638 2020-11-25T03:05:10Z 2020-01-01T00:00:00Z Variability in the emissions savings potential of battery electric vehicles across regions and individuals Miotti, Marco,Ph. D.Massachusetts Institute of Technology. Personal vehicles account for almost 25% of U.S. greenhouse gas emissions, and this share is increasing. The increase is due to several factors, including a growth in transportation demand and the decarbonization of electricity by 30% since 2007. Alternative technologies for road vehicles, such as battery electric, plug-in hybrid, and fuel cell powertrains have the potential to achieve significant emission reductions. Yet questions remain about the emissions and costs of these alternative technologies. This thesis evaluates the emissions reduction potential of vehicles with electrified powertrains, focusing on battery electric vehicles (BEVs). It evaluates this potential taking into account heterogeneous regional conditions and consumer behavior. Consumers help determine vehicle fleet emissions through their purchasing and driving decisions, which are guided in part by the costs of different options.; Therefore, the costs of ownership of BEVs in comparison to conventional vehicles inform the emissions reduction potential of BEVs. Here, we measure the lifecycle greenhouse gas emissions and costs of ownership of BEVs across different vehicle models as a function of travel patterns, driving styles, and properties of the natural, built, and institutional environment. We compare these costs and emissions to gasoline combustion engine vehicles (ICEVs), and then ask whether and under which condition electric vehicle adoption can play a central role in meeting emission targets for the transportation sector. The current literature does not cover all the interdependent sources of variation in the emissions and costs of BEVs compared to ICEVs. In particular, the effects of annual travel distance and fuel efficiency related to individual travel behavior and the wide variety of available vehicle models have not been assessed.; In addition, this variation in emissions and costs of personal vehicles has only been studied across regions, but not across individual vehicles within each region due to vehicle-specific driving patterns. This work addresses these gaps by developing several interlinked models. This includes the construction of a parametrized lifecycle emissions and cost of ownership model (Chapter 2), an algorithm to measure driving style linked to a vehicle energy model (Chapter 3), and a model to quantify the variability in annual travel distance and fuel consumption of different types of vehicles across regions within the United States, encoded as zipcodes, and across individual vehicles within those zipcodes (Chapter 4). Chapter 5 then ties Chapters 2 and 4 together and complements them with additional information to assess the overall heterogeneity in the emissions reduction potential of BEVs. The central results of the thesis are threefold.; First, a rapid decarbonization of electricity in conjunction with an electrification of powertrains will likely be required to meet emission targets for the U.S. transportation sector. Measures that relate to heterogeneous consumer behavior, such as improving driving style and nudging consumers towards purchasing smaller vehicles, can help to reduce greenhouse gas emissions. Second, the electrification of powertrains can come at little to no additional expense to consumers with today's technology and prices. In most parts of the country, BEVs are substantially cheaper than comparable ICEVs. Within regions, the individuals for which BEVs offer the greatest emissions savings would also tend to experience the largest cost savings, since both emissions savings and cost savings are correlated with annual travel distance. Third, emission reductions achieved by BEVs and their costs relative to ICEVs are highly heterogeneous.; The within-region variation in emissions and costs of BEVs compared to ICEVs due to individual driving patterns is at least as large as the variation across regional averages. As a result, a 10% share of BEVs in the fleet can lead to anywhere between 1% and 10% emission reductions, depending on which types of vehicles are being replaced by electric vehicles, by whom, and where. A key application of this work is to inform tools that provide localized and personalized information about the environmental and economic performance of different vehicle models. In Chapter 6, we discuss such a tool that was built as part of this work, called Carboncounter.com. Results from a survey launched on Carboncounter add to existing evidence that providing such information to consumers can help inform a transition to a cleaner light-duty vehicle fleet. These findings further confirm the importance of understanding heterogeneous human behaviors to inform decarbonization strategies for personal transport. Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, February, 2020; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (pages 219-232). 2020-01-01T00:00:00Z 狠狠躁天天躁中文字幕_日韩欧美亚洲综合久久_漂亮人妻被中出中文字幕