<p> The popularity of the concept of patient-career rose at the same time as more people were using chiropractic care in the United States. Yet, patient-career has yet to be applied in a sociological manner to those who seek out chiropractic care. Semi-structured in-depth interviews with 19 patients of chiropractic care reveal that pain, to the point of interference with daily-life, is what drove them to seek out help outside traditional medicine. This research investigates how holding preconceived notions of chiropractic care (positive or negative), the influence of social networks, and beliefs surrounding health and pain influence the direction of the individual careers in chiropractic care. Several key findings emerged throughout the interview process such as understanding the impact of pain on daily-life, the validation of this pain from a caregiver, the importance of connections and shared understandings in healthcare, and the paradox of a chiropractic adjustment as a blend of science, religion, and magic.</p><p>

In many branches of the economy, including transportation, lodging, and more recently cloud computing, users can reserve resources in advance. Although advance reservations are gaining popularity, little is known about the strategic behavior of customers facing the decision whether to reserve a resource in advance or not. Making an advance reservation can reduce the waiting time or the probability of not getting service, but it is usually associated with an additional cost. To evaluate this trade-off, we develop a game-theoretic framework, called advance reservation games, that helps in reasoning about the strategic behavior of customers in systems that allow advance reservations. Using this framework, we analyze several advance reservation models, in the context of slotted loss queues and waiting queues. The analysis of the economic equilibria, from the provider perspective, yields several key insights, including: (i) If customers have no a-priori information about the availability of servers, then only customers granted service should be charged a reservation fee; (ii) Informing customers about the exact number of available servers is less profitable than only informing them that servers are available; (iii) In many cases, the reservation fee that leads to the equilibrium with maximum possible profit leads to other equilibria, including one resulting with no profit; (iv) If the game repeats many times and customers update their strategy after observing actions of other customers at previous stage, then the system converges to an equilibrium where no one makes an advance reservation, if such an equilibrium exists. Else, the system cycles and yields positive profit to the provider Finally, we study the impact of information sharing in M/M/1 queues with strategic customers. We analyze the intuitive policy of sharing the queue length with customers when it is small and hiding it when it is large. We prove that, from the provider perspective, such a policy is never optimal. That is, either always sharing the queue length or always hiding it maximizes the average number of customers joining the queue.

This is a conceptual dissertation which addresses itself to the criticism that the social systems framework is highly abstract and theoretical, and as such relevant only to academics and specialists. The primary purpose of this paper is to operationalise the social system framework, to illustrate its application and to highlight its unique potential. It represents an attempt to enlarge, even redefine, the frameworks used for studying and transforming organisations. In order to best highlight the differences between the social systems framework and other models implicit in traditional approaches, a specific component of organisation reality is focused on, namely the performance appraisal. After discussing and illustrating the models behind most research on the topic, an alternative holistic framework for performance appraisal is sketched. Following this, an actual performance improvement intervention is described in a case study. This provides a practical illustration of the points made in the paper. / Industrial and Organisational Psychology / M. Com. (Industrial Psychology)

<p> Technology is essential to organizations, especially computer-mediated communications, allowing for globalization, improved quality, and increased profits. Knowledge workers rely on technology as well as computer-mediated communication tools. Research on the use of computer-mediated communications among adolescents and in social settings has suggested that there is a lack of effective interaction and relatedness when technology is used. Research further posits that this can cause a breakdown in trust and commitment. This study used a survey to collect data about organizational commitment levels and relatedness and affiliation needs in the workplace for knowledge workers, and to what extent computer-mediated communications could explain these variables. The research question was: What is the extent of relationship between computer-mediated communications, relatedness and affiliation needs, and organizational commitment of knowledge workers? There were 199 responses collected by the researcher and analyzed with linear regression. The survey and structural model utilized a composite of existing instruments to measure computer-mediated communications, relatedness and affiliation needs in the work place, and commitment levels to the organization. Two structural models examined the relationships for the constructs and resulted in two null hypotheses being rejected. The first model tested computer-mediated communications use and relatedness and affiliation needs. The results supported a significant relationship between these variables. The second research question tested the relationship between computer-mediated communications use and normative and affective organizational commitment levels. The second model also rejected the null hypothesis and discovered a significant relationship exists between these variables. Future research was recommended to further analyze the results based on sex and age and include different classifications of knowledge workers.</p><p>

<p> Over the last decade, healthcare supply chain (SC) costs have increased by 40 percent in the United States. A typical hospital&rsquo;s SC costs account for 38 percent of the total, compared to less than 10 percent for most industries. Supply chain costs are the healthcare organizations&rsquo; second biggest expense. Healthcare centers are, therefore, becoming supply chain-sensitive organizations, leading to inefficiency and limited access to quality patient healthcare. This problem is exacerbated by the fact that healthcare SC cost drivers are almost unknown, which makes the work of healthcare SC managers more difficult. This study focuses on uncovering SC cost drivers and provides appropriate cost-reduction strategies tailored to confront each identified driver. Primary data were collected from health centers and secondary data was collected from databases such as Health Care Cost Institute (HCCI), Data Resources Agency for Healthcare Research &amp; Quality, National Health Expenditure Data, and Centers for Medicare &amp; Medicaid. The study looked at the attributes that explain the most variation in each contributing factor. A multiple regression was developed to predict the costs along with F tests and Student t-tests to determine the model goodness-of-fit and each factor&rsquo;s contribution significance. The results of the study might lead to improved efficiency in healthcare organizations and increased access to quality healthcare for the population.</p><p>

<p> The United States (U.S.) healthcare system is the most expensive in the world. To improve the quality and safety of care, health information technology (HIT) is broadly adopted in hospitals. While EHR systems form a critical data backbone for the facility, we need improved 'work-flow' coordination tools and platforms that can enhance real-time situational awareness and facilitate effective management of resources for enhanced and efficient care. Especially, these IT systems are mostly applied for reactive management of care services and are lacking when they come to improving the real-time "operational intelligence" of service networks that promote efficiency and quality of operations in a proactive manner. In particular, we leverage operations research and predictive analytics techniques to develop proactive coordination mechanisms and decision methods to improve the operational efficiency of bed management service in the network spanning the emergency department (ED) to inpatient units (IUs) in a hospital, a key component of healthcare in most hospitals. The purpose of this study is to deepen our knowledge on proactive coordination empowered by predictive analytics in dynamic healthcare environments populated by clinically heterogeneous patients with individual information changing throughout ED caregiving processes. To enable proactive coordination for improved resource allocation and patient flow in the ED-IU network, we address two components of modeling/analysis tasks, i.e., the design of coordination mechanisms and the generation of future state information for ED patients. </p><p> First, we explore the benefits of early task initiation for the service network spanning the emergency department (ED) and inpatient units (IUs) within a hospital. In particular, we investigate the value of proactive inpatient bed request signals from the ED to reduce ED patient boarding. Using data from a major healthcare system, we show that the EDs suffer from severe crowding and boarding not necessarily due to high IU bed occupancy but due to poor coordination of IU bed management activity. The proposed proactive IU bed allocation scheme addresses this coordination requirement without requiring additional staff resources. While the modeling framework is designed based on the inclusion of two analytical requirements, i.e., ED disposition decision prediction and remaining ED length of stay (LoS) estimation, the framework also accounts for imperfect patient disposition predictions and multiple patient sources (besides ED) to IUs. The ED-IU network setting is modeled as a fork-join queueing system. Unlike typical fork-join queue structures that respond identically to a transition, the proposed system exhibits state-dependent transition behaviors as a function of the types of entities being processed in servers. We characterize the state sets and sequences to facilitate analytical tractability. The proposed proactive bed allocation strategy can lead to significant reductions in bed allocation delay for ED patients (up to ~50%), while not increasing delays for other IU admission sources. We also demonstrate that benefits of proactive coordination can be attained even in the absence of highly accurate models for predicting ED patient dispositions. The insights from our models should give confidence to hospital managers in embracing proactive coordination and adaptive work flow technologies enabled by modern health IT systems. </p><p> Second, we investigate the quantitative modeling that analyzes the patterns of decreasing uncertainty in ED patient disposition decision making throughout the course of ED caregiving processes. The classification task of ED disposition decision prediction can be evaluated as a hierarchical classification problem, while dealing with temporal evolution and buildup of clinical information throughout the ED caregiving processes. Four different time stages within the ED course (registration, triage, first lab/imaging orders, and first lab/imaging results) are identified as the main milestone care stages. The study took place at an academic urban level 1 trauma center with an annual census of 100,000. Data for the modeling was extracted from all ED visits between May 2014 and April 2016. Both a hierarchical disposition class structure and a progressive prediction modeling approach are introduced and combined to fully facilitate the operationalization of prediction results. Multinomial logistic regression models are built for carrying out the predictions under three different classification group structures: (1) discharge vs. admission, (2) discharge vs. observation unit vs. inpatient unit, and (3) discharge vs. observation unit vs. general practice unit vs. telemetry unit vs. intensive care unit. We characterize how the accumulation of clinical information for ED patients throughout the ED caregiving processes can help improve prediction results for the three-different class groups. Each class group can enable and contribute to unique proactive coordination strategies according to the obtained future state information and prediction quality, to enhance the quality of care and operational efficiency around the ED. We also reveal that for different disposition classes, the prediction quality evolution behaves in its own unique way according to the gain of relevant information. (Abstract shortened by ProQuest.) </p><p>

abstract: This research is to address the design optimization of systems for a specified reliability level, considering the dynamic nature of component failure rates. In case of designing a mechanical system (especially a load-sharing system), the failure of one component will lead to increase in probability of failure of remaining components. Many engineering systems like aircrafts, automobiles, and construction bridges will experience this phenomenon. In order to design these systems, the Reliability-Based Design Optimization framework using Sequential Optimization and Reliability Assessment (SORA) method is developed. The dynamic nature of component failure probability is considered in the system reliability model. The Stress-Strength Interference (SSI) theory is used to build the limit state functions of components and the First Order Reliability Method (FORM) lies at the heart of reliability assessment. Also, in situations where the user needs to determine the optimum number of components and reduce component redundancy, this method can be used to optimally allocate the required number of components to carry the system load. The main advantage of this method is that the computational efficiency is high and also any optimization and reliability assessment technique can be incorporated. Different cases of numerical examples are provided to validate the methodology. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2016

abstract: When preparing for and responding to disasters, humanitarian organizations must run effective and efficient supply chains to deliver the resources needed by the affected population. The management of humanitarian supply chains include coordinating the flows of goods, finances, and information. This dissertation examines how humanitarian organizations can improve the distribution of information, which is critical for the planning and coordination of the other two flows. Specifically, I study the diffusion of information on social media platforms since such platforms have emerged as useful communication tools for humanitarian organizations during times of crisis. In the first chapter, I identify several factors that affect how quickly information spreads on social media platforms. I utilized Twitter data from Hurricane Sandy, and the results indicate that the timing of information release and the influence of the content’s author determine information diffusion speed. The second chapter of this dissertation builds directly on the first study by also evaluating the rate at which social media content diffuses. A piece of content does not diffuse in isolation but, rather, coexists with other content on the same social media platform. After analyzing Twitter data from four distinct crises, the results indicate that other content’s diffusion often dampens a specific post’s diffusion speed. This is important for humanitarian organizations to recognize and carries implications for how they can coordinate with other organizations to avoid inhibiting the propagation of each other’s social media content. Finally, a user’s followers on social media platforms represent the user’s direct audience. The larger the user’s follower base, the more easily the same user can extensively broadcast information. Therefore, I study what drives the growth of humanitarian organizations’ follower bases during times of normalcy and emergency using Twitter data from one week before and one week after the 2016 Ecuador earthquake. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2018

abstract: ABSTRACT There has been plenty written on the topic of process management in business. This study focuses more on the need to research and develop a model to establish "balance" in process. Reengineering process and investing capital into current technology does not improve the outcome of process alone. The actual process activity coupled with human interface combined with technology determines the outcome of processes, however they do not indicate whether it is a balanced process or not. Wherein the word balance really means sustainable for long periods of time and easily reproduced by others. This study argues for the need of new research in the topic matter and its affects on company profitability, sustainability over long periods of time and tenure with employee. / Dissertation/Thesis / M.S. Engineering 2012

Motivated by collaborative recycling efforts for non-profit agencies, we study a variant of the periodic location routing problem, in which one decides the set of open depots from the customer set, the capacity of open depots, and the visit frequency to nodes, in an effort to design networks for collaborative pickup activities. We formulate this problem, highlighting the challenges introduced by these decisions. We examine the relative dfficulty introduced with each decision through exact solutions and a heuristic approach which can incorporate extensions of model constraints and solve larger instances. The work is motivated by a project with a network of hunger relief agencies (e.g., food pantries, soup kitchens and shelters) focusing on collaborative approaches to address their cardboard recycling challenges collectively. We present a case study based on data from the network. In this novel setting, we evaluate collaboration in terms of participation levels and cost impact. These insights can be generalized to other networks of organizations that may consider pooling resources.