This thesis aims to establish the principles that should govern the conception of health websites in Aboriginal context. For Aboriginal Canadians in remote communities, it is essential to gain accurate, reliable and relevant access to health information. Health websites customized to the preferences of users and to the cultural particularities of Aboriginal culture are a possible means to vehicle this information to them. A user-centered approach is used to gather data on the preferences of presentation types and content on health websites for Aboriginal people. Remote focus group sessions were conducted using videoconferencing. Different Web presentation types, mainly videos and text-based Web pages, from Diabetes and COPD websites, are presented to the users during the focus group sessions. A qualitative data analysis is done to examine user preferences and what factors have an influence on those preferences. Key findings include: (1) the type of disease being presented on the websites has an influence on the preferences of users; (2) the preferences of users vary based on the different disease aspects presented; (3) the different factors related to the living location, such as the limited access to healthcare and the Internet, the inaccessibility of certain foods and the diversity of cultures of the different remote communities also affect the preferences of users; (4) users prefer websites to contain elements related to their culture. The findings of this research have allowed for the development of the principles for the conception of health websites. Health website owners should apply these principles to use the appropriate presentation types, add content that is more meaningful to its target audience, and reorganize the content so that the most relevant information is more easily accessible to the users. As a consequence of conducting qualitative research, the main limitation of this research is the small sample size. Future research should be conducted with a larger sample size that is more representative of the general population and of the heterogeneity of Aboriginal culture to confirm the results of this study.

<p> The demand for psychiatry is driven by the amount of patients that require mental health services. In the United States, there is a growing need for mental health services. One in every 5 adults in America suffers from some type of mental illness; 1 in every 20 has a serious mental illness. In 2014, 60% of adults living with a mental illness did not receive treatment for it. The amendment of the Mental Health Parity and Addiction Equity Act by the Patient Protection and Affordable Care Act has expanded access to mental health services. However, although the accessibility to psychiatric services has increased, a shortage of psychiatrists has resulted in long wait times, creating challenges to meet the demand. Pacific Psychiatric Group is a direct pay, outpatient practice that offers mental health services at competitive, fixed cash rates. Insurance will not be accepted. The burden of overhead dealing with the bureaucracy and administration of insurance will be eliminated, which helps to keep costs low and allows providers to focus on the patient. Pacific Psychiatric Group&rsquo;s mission is to alleviate accessibility challenges and provide timely, high quality, personalized, patient-focused mental health services. This proposed business plan will demonstrate how Pacific Psychiatric Group plans to improve accessibility, transparency, and quality of psychiatric services in an effort to reduce the number of untreated individuals.</p><p>

This dissertation investigates whether differences in organizational innovation amongst health care providers can explain the huge variation in costs and outcomes. I specifically consider two facets of organizational innovation: the deployment of information technology and the relationships between hospitals and physicians. In the first chapter, I investigate IT adoption in a service setting by considering the impact of electronic medical records (EMRs) on the length of stay and clinical outcomes of patients in US hospitals. To uncover the distinct impacts of EMRs on operational efficiency and care coordination, I present evidence of heterogeneous effects by patient complexity. I find that EMRs have the largest impact for relatively less complex patients. Admission to a hospital with an EMR is associated with a 2\% reduction in length of stay and a 9\% reduction in thirty-day mortality for such patients. In contrast, there is no statistically significant benefit for more complex patients. However, I present three additional results for complex cases. First, patients returning to the same hospital benefit relative to those who previously went to a different hospital, which could be due to easier access to past electronic records. Second, computerized order entry is associated with higher billed charges. Finally, hospitals that have a high share of publicly insured patients, and hence a bigger incentive to curb resource use, achieve a greater reduction in length of stay for complex patients after EMR adoption. In the second chapter, co-authored with Robert Huckman, I investigate the role of process specialists in guiding customers through such complex service transactions by considering the management of patients admitted to U.S hospitals. Traditionally, a patient's primary care physician has been in charge of his or her hospital admission. Over the past decade, however, there has been a steady rise in the use of hospitalists - physicians who spend all their professional time at the hospital - in managing inpatient care. Using data from the American Hospital Association and the Agency for Healthcare Research and Quality's Nationwide Inpatient Sample (NIS) database, we find that hospitals with hospitalist programs achieve reductions in the risk-adjusted length of stay of inpatients over the time period 2003 to 2010. The effect is strongest for complex patients who have a higher number of comorbidities. Our findings support the view that process specialists such as hospitalists are particularly beneficial for complex transactions that entail a greater degree of coordination. In the final chapter, I document the positive relationship between consolidation in the health care industry and technology adoption. I propose several mechanisms that could explain the association between the adoption of electronic medical records and greater hospital-physician integration. I show that the positive correlation between technology adoption and hospital consolidation has been increasing over time. I show that hospitals located in concentrated markets are more likely to adopt electronic medical records and to use hospitalists. Moreover, for a limited set of hospitals, the quality of management is positively associated with the adoption of electronic medical records and the use of hospitalists. / Business Economics

In this dissertation, we propose methodology to account for missing data as well as a strategy to account for outcome heterogeneity. Missing data occurs frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not at random (MNAR) if, conditional on the observed variables, the missing data mechanism still depends on the unobserved outcome. In such settings, identification is generally not possible without imposing additional assumptions. Identification is sometimes possible, however, if an exogeneous instrumental variable (IV) is observed for all subjects such that it satisfies the exclusion restriction that the IV affects the missingness process without directly influencing the outcome. In chapter 1, we provide necessary and sufficient conditions for nonparametric identification of the full data distribution under MNAR with the aid of an IV. In addition, we give sufficient identification conditions that are more straightforward to verify in practice. For inference, we focus on estimation of a population outcome mean, for which we develop a suite of semiparametric estimators that extend methods previously developed for data missing at random. Specifically, we propose inverse probability weighted estimation, outcome regression based estimation and doubly robust estimation of the mean of an outcome subject to MNAR. For illustration, the methods are used to account for selection bias induced by HIV testing refusal in the evaluation of HIV seroprevalence in Mochudi, Botswana, using interviewer characteristics such as gender, age and years of experience as IVs. The development of coherent missing data models to account for nonmonotone missing at random (MAR) data by inverse probability weighting (IPW) remains to date largely unresolved. As a consequence, IPW has essentially been restricted for use only in monotone MAR settings. In chapter 2, we propose a class of models for nonmonotone missing data mechanisms that spans the MAR model, while allowing the underlying full data law to remain unrestricted. For parametric specifications within the proposed class, we introduce an unconstrained maximum likelihood estimator for estimating the missing data probabilities which is easily implemented using existing software. To circumvent potential convergence issues with this procedure, we also introduce a constrained Bayesian approach to estimate the missing data process which is guaranteed to yield inferences that respect all model restrictions. The efficiency of standard IPW estimation is improved by incorporating information from incomplete cases through an augmented estimating equation which is optimal within a large class of estimating equations. We investigate the finite-sample properties of the proposed estimators in extensive simulations and illustrate the new methodology in an application evaluating key correlates of preterm delivery for infants born to HIV infected mothers in Botswana, Africa. When a risk factor affects certain categories of a multinomial outcome but not others, outcome heterogeneity is said to be present. A standard epidemiologic approach for modeling risk factors of a categorical outcome typically entails fitting a polytomous logistic regression via maximum likelihood estimation. In chapter 3, we show that standard polytomous regression is ill-equipped to detect outcome heterogeneity, and will generally understate the degree to which such heterogeneity may be present. Specifically, nonsaturated polytomous regression will often a priori rule out the possibility of outcome heterogeneity from its parameter space. As a remedy, we propose to model each category of the outcome as a separate binary regression. For full efficiency, we propose to estimate the collection of regression parameters jointly by a constrained Bayesian approach which ensures that one remains within the multinomial model. The approach is straightforward to implement in standard software for Bayesian estimation. / Biostatistics

Social hierarchies can prevent teams from hearing and using all of their members’ contributions. They are also ubiquitous and difficult to change, reinforced by conscious and unconscious factors as well as social-structural systems. Social hierarchies in teams, however, can and do change. This dissertation diverges from recent research focused on the stability of social hierarchies to argue that social hierarchies in teams can become more dynamic over time; it also explores why and how this shift comes about and how it impacts team member relationships and interaction patterns. In chapter 2, “Toward a more dynamic conceptualization of social hierarchy in teams,” I theorize about the antecedents and processes that allow teams to shift their social hierarchy, focusing on the importance of socialized schemas, identity, emotions, and behaviors. Chapters 3 and 4 draw from a 31-month ethnographic investigation into these processes in three multidisciplinary “change teams” in primary health care clinics. These teams were specifically charged with moving their organization toward a more dynamic social hierarchy to remain competitive in their industry. I studied how team members did this within their own team. In chapter 3, “Microwedges: Moving teams from rigid to dynamic social hierarchy,” I identify and theorize about the process through which an extra-role behavior, over time, helps to create cognitive changes in team members, prompting them to change their task strategies, role responsibilities, and communication patterns to promote dynamic social hierarchy in the team. Chapter 4, “The changing nature of social hierarchy and voice” follows a change team on a weekly basis over 22 months to document a shift to dynamic social hierarchy and to theorize about the relationship between social hierarchy and voice and silence via “opening” and “closing” behaviors and the team conversation structure. My dissertation extends and generates theory about social hierarchy and voice. It introduces the concepts of dynamic social hierarchy and the microwedge process to further our understanding of how teams and their members change over time. It also has practical implications for how team members can engage with the social hierarchy in which they are embedded, alter their teams’ processes, and help their organizations rethink entrenched assumptions about the capabilities and preferences of their members.

This study was to evaluate the development level of Clinical Engineering Departments (CEDs) in hospitals in developing countries. The method of data collection was a survey done by structured questionnaire sent by Email and Listserv. In total, 61 responses (9% response rate) were grouped into two regions: Latin America (27 from Venezuela, Mexico, Brazil) and Asia (34 from India, Bangladesh, P.R. China, Indonesia, Saudi Arabia, South Africa); The responses from those developing countries were compared with those from developed countries acquired in previous studies done by Frize and Glouhova. In this study, results indicate that CEDs that responded to the survey from developing countries have similar organizational structure as developed countries, but there are differences in personnel educational levels, responsibilities, and resources. We also identified differences in the level of development of CEDs in respondents from Asia and those from Latin America. The latter were more advanced overall than those in Asia, but CEDs in both regions need to improve their level of development. Future research should focus on collecting more data from CEDs of developing countries, and expand the quantitative analysis that will be possible with a larger sample.

Health care reform is an essential and continuous process as a nation's medical system adjusts to meet changing needs in the population. It has been recognized that the Canadian health care system is in need of revolutionary change. Many other countries have had similar experiences to Canada in both resource shortages and reform process. The population of Ontario is growing and becoming older. Older persons place greater demands on the health care system. At the same time the Ontario physician workforce has decreased in size, increased in age and has more female entrants. Elderly physicians reduce their workloads as they reach the age of 65 years. Female physicians take on lower workloads than male physicians, especially in the early part of their careers. Over the past eight years there have been less entrants to Family Practice and more to specialty services. As well, in the past five years nearly 60 percent of Family Practice physicians have reduced their scope of care by limiting their practices. Taken together these observations demonstrate that while there is an increased need for primary and secondary care physicians there has also been a concurrent reduction of the number of physicians both in terms of census and also in terms of workload. In the past decade the Universities of Ontario have increased tuition fees significantly with respect to the other provinces. Financial pressures and control of hours (lifestyle control) may further limit the intake of post-graduate Family Practice physicians. Reversing these trends will require increasing the physician workforce and the development of programs to encourage entry into primary care.

A clinical caremap is a cost-effective tool for clinical process improvement that has been accepted in hospitals and various healthcare organizations in many countries. However, compared to the literature describing the initial development of the clinical caremaps, the evaluation of the impact of the variances in the caremap pathway on the patient's expected outcomes and the patient's length of stay (LOS) remains relatively less analyzed. In this research, we deal with the issue of variances in the clinical caremap by building a Bayesian belief network named BBN_RPC to model the radical prostatectomy caremap. The BBN_RPC model provides insight into probabilistic dependencies that exist among the activities (variables) in the caremap. We then use the BBN_RPC model to analyze possible variances and to make inferences. The results show that most of the activities in the caremap are related with each other and to some extent linked with the patient's length of stay (LOS), whereas different activities have different weights on the LOS. Using radical prostatectomy patients' data from a retrospective chart study conducted at the Ottawa Civic Hospital, we have applied the BBN_RPC model to predict a patient's future conditions and the LOS, based on the current observations. Predictive accuracy of the BBN_RPC model was evaluated by cross validation tests, which showed the accuracy for predicting the patient's LOS, given the patient's observations during the first two post-op days, is at approximately 94% level.

Little is known about the relative cost-effectiveness (CE) of different cardiac rehabilitation (CR) program designs and how CE is influenced by a patient's clinical and demographic characteristics. The aim of this study was to assess the 2-year incremental cost-utility of a distributed (12-month, 33-session) CR program to that of a standard (3-month, 33-session) CR program as assessed from the perspective of the cardiac health care system. 306 Patients (mean age = 58.4 years, SD+/-9.7) with CAD were randomized to either standard or distributed CR. Program delivery costs, cardiac health care use, QALYS were tracked over a two-year period. The standard CR intervention was found to be dominant, resulting in both a cost saving and larger gains in QALYs in the 2-years following initiation of CR. Important differences were noted in CE of CR across cardiac risk strata and diagnosis groups, suggesting patients may benefit from triage to available CR models.

This thesis provides an economic and mathematical framework, and the computing tools to compare the effects, costs and incremental cost-effectiveness of acute or preventative interventions for cardiovascular disease. A Finite Space Markov Chain Decision Analysis Model is designed by integrating a Decision Trees Model and a Markov Chain Model. The model and Cost-Effectiveness Analysis are implemented by using SAS/IML both on a PC with one processor and on a machine with multiple processors of the High Performance Computing Virtual Laboratory. A sample case with four states and eight intervention policies is studied to illustrate the framework, which is composed of (1) life path simulation, (2) cost and effectiveness estimation, (3) cost-effectiveness analysis, (4) sensitivity analysis, and (5) performance analysis on different platforms. Solution of delay effects, correlation among risk factors, and fluctuation in discount rate are viewed as limitations of the thesis and rewarding areas for further research.