The historical evolution of South Africa’s health sector, dating back to the 17th century, is significantly different from that of other African countries. Throughout the four centuries of development there have been numerous advances in health policy, legislative instruments and health system progress. Against this background this dissertation critically analysed the management of records in public health care institutions in South Africa. The study did this by addressing three objectives: assess the legislative, policy and regulatory contextual framework of South Africa’s health care system; assess the effectiveness of records management within public health care institutions; and identify appropriate interventions to address the challenges facing records management in the health care system. The study used purposive sampling to identify respondents with diverse expertise in three main sectors: the public sector, the private sector as well as in academic and research institutions. Using interview research technique the study solicited data that was analysed in order to provide a composite picture in addressing the research objectives. The analysis of data revealed three overarching themes. First, there is substantial legislative and regulatory dissonance in the management of health records in the country. While there are extensive legislative, regulatory and policy instruments that could be used to manage records, many lack coherence with records management issues such as records retention. Second, understanding the complex interplay of different legal and regulatory instruments is a critical first step, but it remains the beginning of the process towards building a sophisticated implementation process. For this process to be successful, study respondents argued that records compliance would have to be the backbone of all other compliance processes. Third, while there were substantial areas of weakness in the management of records in South Africa’s public health sector, there have been a number of pockets of excellence. These include the efforts towards complying to access to information legislation by the Limpopo Department of Health and Social Development as well as the successful introduction of Enterprise Content Management systems in health care institutions by the Western Cape Department of Health / Information Science / D. Litt. et Phil. (Information Science)

Methicillin-resistant Staphylococcus aureus (MRSA) is an antibiotic-resistant bacterium of epidemiologic importance in Canadian healthcare facilities. The contact between MRSA colonized or infected patients with other patients, healthcare workers (HCWs) and/or the healthcare environment can result in MRSA transmission and healthcare-associated MRSA (HA-MRSA) infections in hospitals. These HA-MRSA infections are linked with increased length of hospital stay, economic burden, morbidity and mortality. Although infection prevention and control programs initiated in 2009 in Canada and other developed countries (e.g., UK, France, Belgium, Denmark, etc.) have been relatively successful in reducing the rate of HA-MRSA infections, they continue to pose a threat to patients, especially to the more vulnerable in long term care and geriatric institutions. Historically, MRSA was a problem mainly in hospital settings but after mid-1990s new strains of MRSA have been identified among people without healthcare-related risks and have been classified as community-associated MRSA (CA-MRSA). Furthermore, the distinction between HA-MRSA and CA-MRSA strains is gradually waning due to both the introduction of HA-MRSA in communities, and the emergence of CA-MRSA strains in hospitals. The purpose of this thesis was to explore the feasibility of constructing healthcare networks to evaluate the role of healthcare providers (e.g., physicians) and places (e.g., patient rooms) in the transmission of MRSA in a large tertiary care facility. Method of investigation: a secondary data case-control study, using individual characteristics and network structure measures, conducted at The Ottawa Hospital (TOH) between April 1st, 2013 and March 31th, 2014. Results: It was feasible to build social networks in a large tertiary care facility based on electronic medical records data. The networks' size (represented by the number of vertices and lines) increased during the outbreak period (period 1) compared to the pre-outbreak period (period 0) for both groups and at all three TOH campuses. The calculated median degree centrality showed significant increase in value for both study groups during period 1 compared to period 0 for two of the TOH campuses (Civic and General). There was no significant difference between the median degree centrality calculated for each study group at the Heart Institute when compared for the two reference periods. The median degree centrality of the MRSA case group for period 0 showed no significant difference when compared to the same measure determined for the control group for all three TOH campuses. However, the median degree centrality calculated for period 1 was significantly increased for the control group compared to the MRSA case group for two TOH campuses (Civic and General) but showed no significant difference between the two groups from the Heart Institute. In addition, there was a correlation between the two network measures (degree centrality and eigenvector centrality) calculated to determine the most influential person or place in the MRSA case group networks. However, there was no correlation between the two network’s measures calculated for physicians included in MRSA case group networks. Conclusions: It is feasible to use social network analysis as an epidemiologic analysis tool to characterize the MRSA transmission in a hospital setting. The network's visible changes between the groups and reference periods were reflected by the network measures and supported also by known hospital patient movements after the outbreak onset. Furthermore, we were able to identify potential source cases and places just prior of the outbreak start. Unfortunately, we were not able to show the role of healthcare workers in MRSA transmission in a hospital setting due to limitations in data collection and network measure chosen (eigenvector centrality). Further research is required to confirm these study findings.

Research in risk awareness has been relatively neglected in the health informatics literature, which tends largely to examine project managers’ perspectives of risk awareness; very few studies explicitly address the perspectives held by senior executives such as directors. Another limitation evident in the current risk literature is that studies are often based on American data and/or they are restricted to American culture. Both factors highlight the need to examine how senior executives (i.e., directors) who oversee or direct eHealth projects in Canada perceive risk awareness. This research explores and discusses the perspectives of risk awareness (i.e., identification, analysis, and prioritization) held by directors and project managers who implement Canadian eHealth projects. Semi-structured interviews with nine directors and project managers uncovered six key distinctions in these two groups’ awareness of risk. First, all project managers valued transparency over anonymity, whereas directors believed that an anonymous reporting system for communicating risks had merit. Secondly, most directors emphasized the importance of evidence-based planning and decision making when balancing risks and opportunities, an aspect none of the project managers voiced. Thirdly, while project managers noted that the level of risk tolerance may evolve from being risk-averse to risk-neutral, directors believed that risk tolerance evolved toward risk-seeking. Directors also noted the importance of employing risk officers, a view that was not shared by project managers. Directors also believed the risk of too little end-user engagement and change management was the most important risk, whereas project managers ranked it as the least important. Finally, when directors and project managers were asked to identify and define the root cause(s) of eHealth risks, directors identified the complexity of health care industry, while project managers attributed it to political pressure and a lack of resources where eHealth projects are concerned. This research proposes that the varied perspectives of risk awareness held by directors and project managers must be considered and integrated to properly align expectations and build partnerships for successful eHealth project outcomes. Understanding risk awareness offers a means to systematically identify and analyze the complex nature of eHealth projects by embracing uncertainties, thereby enabling forward thinking (i.e., staying one step ahead of risks) and the ability to prevent avoidable risks and seize opportunities. / Graduate / 0723 / 0489 / 0454 / randbpark@gmail.com

Implant terms are terms like "pacemaker" which indicate the presence of artifacts in the body of a human. These implant terms are key to determining if a patient can safely undergo Magnetic Resonance Imaging (MRI). However, to identify these terms in medical records is time-consuming, laborious and expensive, but necessary for taking the correct precautions before an MRI scan. Automating this process is of great interest to radiologists as it ideally saves time, prevents mistakes and as a result saves lives. The electronic medical records (EMR) contain the documented medical history of a patient, including any implants or objects that an individual would have inside their body. Information about such objects and implants are of great interest when determining if and how a patient can be scanned using MRI. This information is unfortunately not easily extracted through automatic means. Due to their sparse presence and the unusual structure of medical records compared to most written text, makes it very difficult to automate using simple means. By leveraging the recent advancements in Artificial Intelligence (AI), this thesis explores the ability to identify and extract such terms automatically in Swedish EMRs. For the task of identifying implant terms in medical records a generally trained Swedish Bidirectional Encoder Representations from Transformers (BERT) model is used, which is then fine-tuned on Swedish medical records. Using this model a variety of approaches are explored two of which will be covered in this thesis. Using this model a variety of approaches are explored, namely BERT-KDTree, BERT-BallTree, Cosine Brute Force and unsupervised NER. The results show that BERT-KDTree and BERT-BallTree are the most rewarding methods. Results from both methods have been evaluated by domain experts and appear promising for such an early stage, given the difficulty of the task. The evaluation of BERT-BallTree shows that multiple methods of extraction may be preferable as they provide different but still useful terms. Cosine brute force is deemed to be an unrealistic approach due to computational and memory requirements. The NER approach was deemed too impractical and laborious to justify for this study, yet is potentially useful if not more suitable given a different set of conditions and goals. While there is much to be explored and improved, these experiments are a clear indication that automatic identification of implant terms is possible, as a large number of implant terms were successfully discovered using automated means.