Seeking healthcare is an act of trust: patients reveal private information, pain, and vulnerability to physicians who have specialized knowledge and skills. Patients may endure risk and uncertain treatment outcomes based on the assurance of a trusted physician. Physicians’ professional oaths compel them to protect patients’ welfare first, and the power imbalance in these relationships is tolerable precisely because of the bond of trust. When this trust is protected, it is a powerful tool: patients are more engaged, benefit more from medical interventions, and are healthier overall. Yet these healthcare relationships are contained within larger institutions – hospitals, insurance companies, government programs – that may circumscribe physicians’ abilities to protect patients’ trust to the fullest and even contribute to negative medical experiences. Because trust and vulnerability characterize patients’ interactions with healthcare institutions, institutional actions and inactions that contribute to negative medical experiences constitute institutional betrayal. In this dissertation I address this largely unexamined issue in healthcare research by drawing on research and theory in trauma psychology. I report the results of a study based on the survey responses of 707 American adults. Institutional betrayal in healthcare was reported by two-thirds of the participants and predicted lower trust in participants’ own physicians, doctors in general, and healthcare organizations. These negative effects were more pronounced for patients who reported higher levels of trust in healthcare institutions prior to the betrayal and did not seem to be influenced by a general tendency to trust others. However, the effects of institutional betrayal on trust in healthcare organizations were buffered by trust in one’s own physician. Institutional betrayal also predicted worse physical health and increased symptoms of depression, dissociation, and post-traumatic distress – both directly and through disengagement from healthcare. Consistent with betrayal trauma theory, participants who experienced institutional betrayal were five times more likely to report some difficultly remembering that betrayal and negative medical experiences. This unawareness may allow patients to continue to seek necessary medical care, even in the presence of institutional betrayal. In order to understand what contributes to patient trust and engagement in healthcare and why some patients experience worse mental and physical health outcomes, institutional betrayal must be taken into account.

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In the last decade modern information technology systems have been introduced to healthcare in order to improve it. The aim of this study is to present the impact of such information system’s adoption on patient safety and efficiency within healthcare. Interviews, observations along with literature study were conducted in order to study the impact of the adoption on patient safety and efficiency at hospital’s wards where a new information system is implemented. The conclusion of this study is that such information technology systems can improve patient safety. However it is believed that the information technology system can improve efficiency in some aspects such as the communication among medical care personnel while other aspects within efficiency can be achieved if some improvements are made. Moreover the ability to access Electronic Healthcare Records is considered to be important to improve the medical care, which can increase patient safety.

Risk reduction processes in healthcare remain at the core of 21st century health care provision, though the continuing scale of the problem gives little room for complacency. While other areas of complex technological activity such as air transportation can demonstrate improvements in safety performance, comparable progress eludes modern healthcare. A review of risk reduction techniques within healthcare identifies that there exists a lack of tools involving simulation of risk. It has been necessary in the context of the research to establish many wholly original information structures representing healthcare activity and associated risk related interactions This Thesis describes a new risk simulation environment for the Critical Care Unit of University Hospital, Coventry which is a 1200 bed modern acute hospital which fully opened in 2006. Available sets of patient admission/discharge information and records of patient treatment records used for cost charging together with extensive direct observation of clinical activity are used to create simulated patient episodes within the Critical Care environment. Specific patient interventions are sub divided into a series of up to 7 sub tasks which are associated with sub competencies and a linked adverse effect. Such sub competencies can be coded to reflect three levels of task complexity. Separate codes can be allocated to identify sub competencies which are supervised and sub competencies for which additional competency can be requested from other team members. A fuzzy logic framework has been adopted to combine empirically derived mathematical functions which for a specific sub task, translate values of individual effectiveness, distraction, competency mismatch of individual/team together with the level of supervision to a specific risk value for each adverse effect. This fuzzy logic framework, referenced as the ‘risk engine’ has specific responses for levels of sub task complexity and can be modified by indicators relating to sub task supervision and competency sharing. In addition, each sub task/competency is associated with an adverse effect whose probability of occurrence can be reduced through identified safe working practices which are referenced as ‘preventive measures’. Individual effectiveness is identified as being influenced by cirdadian rhythm, physical effort, emotional/stress effort, intellectual effort, sleep deficit and long term factors. Organisational factors influencing individual effectiveness are identified as patient admission and shift handover. The risk simulation process is implemented within a 10 bed Critical Care Unit which utilises a specifically designed nurse rostering process for 12 hour shift periods. Sub grades of nurse skills (1 to 15) are used to structure skill mix within each rostered group and which are based on representative nurse grades (band 5, 6 and 7). Available competencies of nursing staff for a specific sub task are allocated on the basis of sub grade value and the parameter of individual competency mismatch is derived from values of required competency and available competency for each sub task. The team competency mismatch for a specific sub task linked to a specific individual is derived from the maximum available competency within the active nursing team. Nursing staff are allocated to patients on the basis of clinical need at the start of each shift. A novel feature of the model identifies modes of interaction between nursing individuals on a ‘bed to bed’ basis as relating to parameters of distraction, supervision and competency sharing and which are related to the physical layout of the active clinical area. A fuzzy logic sub system for determining values of such interaction coefficients and which uses the same design methodology as the ‘risk engine’ is described.

This thesis concerns healthcare management and specifically addresses the problems of operating room planning and waiting list management. The operating room department is one of the most expensive areas within the healthcare system which necessitates many expensive resources such as staff, equipment and medicine. The planning of operating rooms is a complex task involving many dependencies and conflicting factors and hence careful operating room planning is critical to attain high productivity. One part of the planning process is to determine a Master Surgery Schedule (MSS). An MSS is a cyclic timetable that specifies the allocation of the surgical groups into different blocks of operating room time. Using an optimization-based approach, this thesis investigates whether the MSS can be adapted to better meet the varying surgery demand. Secondly, an extended optimization-based approach, including post-operative beds, is presented in which different policies related to priority rules are simulated to demonstrate their affect on the average waiting time. The problem of meeting the uncertainty in demand of patient arrival, as well as surgery duration, is then incorporated. With a combination of simulation and optimization techniques, different policies in reserving operating room capacity for emergency cases together with a policy to increase staff in stand-by, are demonstrated. The results show that, by adopting a certain policy, the average patient waiting time and surgery cancellations are decreased while operating room utilization is increased. Furthermore, the thesis focuses on how different aspects of surgery pre-conditions affect different performance measures related to operating room planning. The emergency surgery cases are omitted and the studies are delimited to concern the elective healthcare process only. With a proposed simulation model, an experimental tool is offered, in which a number of analyses related to the process of elective surgeries can be conducted. The hypothesis is that, sufficiently good estimates of future surgery demand can be assessed at the referral stage. Based on this assumption, an experiment is conducted to explore how different policies of managing incoming referrals affect patient waiting times. Related to this study, possibility of using data mining techniques to find indicators that can help to estimate future surgery demand is also investigated. Finally, in parallel, an agent-based simulation approach is investigated to address these types of problems. An agent-based approach would probably be relevant to consider when multiple planners are considered. In a survey, a framework for describing applications of agent based simulation is provided.

Dr. Jim Luecke, a rural family physician in Alpine, Texas, is one of six doctors responsible for thousands of patients across a sprawling 25,000 square foot remote region of the state. He is a community doctor that travels between three towns to treat patients with various illnesses, injuries and income levels. But his type of general medicine is a dying practice in Texas, especially in rural areas. Texas, with a primary care and family physician shortage likely to get worse over the next several years, faces continued obstacles in providing access to quality healthcare in some of its most isolated areas. Luecke, while he embodies some of the challenges that come with practicing rural medicine, is in some ways an exception to those challenges. / text

Open access to health data can bring enormous social and economical benefits. However, such access can also lead to privacy breaches, which may result in discrimination in insurance and employment markets. Privacy is a subjective and contextual concept, thus it should be interpreted from both systemic and information perspectives to clearly understand potential breaches and consequences. This dissertation investigates three popular use cases of healthcare data: specifically, 1) synthetic data publication, 2) aggregate data utilization, and 3) privacy-aware API implementation. For each case, we develop statistical models that improve the privacy-utility Pareto frontier by leveraging a variety of machine learning techniques such as information theoretic privacy measures, Bayesian graphical models, non-parametric modeling, and low-rank factorization techniques. It shows that much utility can be extracted from health records while maintaining strong privacy guarantees and protection of sensitive health information. / text

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