This thesis will examine the manual hospital document retrieval system and electronic document scanning system. From this examination, requirements will be listed for the Administrative Reporting for the Hospital Document Scanning System which will provide better service and reliability than the previous systems. To assure that the requirements can be met, this will be developed into a working system which is named as the Administrative Reporting for the Hospital Document Scanning System(ARHDSS). / Department of Computer Science

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2011. / The research was conducted as part of a socio-tech initiative undertaken at the Cape Peninsula University of Technology. The socio-tech initiative overall focus was on addressing issues faced by rural and under-resourced communities in South Africa, specifically looking at Home-Based Healthcare (HBHC) primarily in the Western Cape. As research into the HBHC context in rural and under-resourced communities continued numerous issues around data and data-elements came to light. These data issues were especially prevalent in relation to the various paper forms being used by the HBHC initiatives that attempt to deliver care in these communities. The communities have the tendency to suffer from poor access to formal healthcare services and healthcare facilities. The data issues were primarily in terms of how data was defines and used within the HBHC initiatives. Within the HBHC initiatives that cater for rural and under-resourced communities there was a clear prevalence of paper-based systems, and a very low penetration of IT-based solution. Because similar and related data-elements are used throughout the paper forms and within different context these data-elements are inconsistently used and presented. The paper forms further obfuscate these inconsistencies as the paper forms regularly change due to internal and external factors. When these paper forms are changed date elements are added or removed without the changes to the underlying ontologies being considered.

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016. / In the Western Cape, South Africa, despite the prospective benefits that e-Health information systems (e-Health IS) offer to support the healthcare sector; there are limitations in terms of usability, functionality and peculiar socio-technical factors. Thus, healthcare professionals do not make the most use of the implemented e-Health IS. Unfortunately, explanations remain tentative and unclear, yet non-usage of the e-Health IS defeats the objectives of its adoption, in the sense that the plan to improve and deliver quality healthcare service in the public sector may not be achieved as envisaged. The aim of the study was to acquire explanations to the causes of the limitations regarding the adoption and, particularly, the use (or non-use) of e-Health IS by clinical staff in the public healthcare institutions in South Africa. The choice of research approach was informed by the research problem, objectives, and the main research question. By the reasons of the subjective and socio-technical nature of the phenomenon, a deductive approach was adopted for this investigation. The nominalist ontology and interpretivist epistemology positions were taken by the researcher as a lens to conduct this research; which informed a qualitative methodology for this investigation. The purposive sampling technique was used to identify the appropriate participants from different hospital levels consisting of Hospital Administrative staff, and Clinical staff (Clinicians and Nurses) of relative experiences in their clinical units. Subsequently, the Unified Theory of Acceptance and Use of Technology (UTAUT) and content analysis technique were used to contextualize, simplify, and analysis the text data transcripts. The findings indicate that healthcare professionals have a high level of awareness and acceptance to use implemented e-Health IS. There are positive perceptions on the expected outcomes, that e-Health IS would improve processes and enhance healthcare services delivery in the public healthcare sector. Also, findings indicate that social influence plays a vital role especially on the willingness of individuals (or groups); as the clinical staff are influenced by their colleagues despite the facilitating conditions provided by the hospital management. Further, findings indicate that it is somewhat problematic to maintain balance in running a parallel paper-electronic system in the hospital environment. Hence, the core factors that influence successful adoption and use of e-Health IS include; willingness of an individual (or group) to accept and use a technology, the performance expectancy, social influence among professionals in the healthcare scenery and adequate facilitating conditions. In summary, it is recommended that there should be an extensive engagement inclusive of all respective stakeholders involved in the adoption processes. This would ensure that e-Health IS are designed to meet both practical organizational and clinical needs (and expectations) with respect to the hospital contexts.

Whereas there are various initiatives to standardize the storage, processing and use of electronic patient information in the South African health sector, the sector is fragmented through the adoption of various approaches on national, provincial and district levels. Divergent IT systems are used in the public and private health sectors (“Recommendations of the Committee on …” 2003). Furthermore, general practitioners in some parts of the country still use paper as a primary means of documentation and storage. Nonetheless, the use of computerized systems is increasing, even in the most remote rural areas. This leads to the exposure of patient information to various threats that are perpetuated through the use of information technology. Irrespective of the level of technology adoption by practitioners in private healthcare practice, the security and privacy of patient information remains of critical importance. The disclosure of patient information whether intentional or not, can have dire consequences for a patient. In general, the requirements pertaining to the privacy of patient information are controlled and enforced through the adoption of legislation by the governing body of a country. Compared with developed nations, South Africa has limited legislation to help enforce privacy in the health sector. Conversely, Australia, New Zealand and Canada have some of the most advanced legislative frameworks when it comes to the privacy of patient information. In this dissertation, the Australian, New Zealand, Canadian and South African health sectors and the legislation they have in place to ensure the privacy of health information, will be investigated. Additionally, codes of practice and guidelines on privacy of patient information for GPs, in the afore-mentioned countries, will be investigated to form an idea as to what is needed in creating and formulating a new code of practice for the South African GP, as well as a pragmatic tool (checklist) to check adherence to privacy requirements.

Traditionally, health records have been stored in paper folders at the physician’s consulting rooms – or at the patient’s home. Some people stored the health records of their family members, so as to keep a running history of all the medical procedures they went through, and what medications they were given by different physicians at different stages of their lives. Technology has introduced better and safer ways of storing these records, namely, through the use of Personal Health Records (PHRs). With time, different types of PHRs have emerged, i.e. local, remote server-based, and hybrid PHRs. Web-based PHRs fall under the remote server-based PHRs; and recently, a new market in storing PHRs has emerged. Cloud computing has become a trend in storing PHRs in a more accessible and efficient manner. Despite its many benefits, cloud computing has many privacy and security concerns. As a result, the adoption rate of cloud services is not yet very high. A qualitative and exploratory research design approach was followed in this study, in order to reach the objective of proposing guidelines that could assist PHR providers in selecting a secure Cloud Service Provider (CSP) to store their customers’ health data. The research methods that were used include a literature review, systematic literature review, qualitative content analysis, reasoning, argumentation and elite interviews. A systematic literature review and qualitative content analysis were conducted to examine those risks in the cloud environment that could have a negative impact on the secure storing of PHRs. PHRs must satisfy certain dimensions, in order for them to be meaningful for use. While these were highlighted in the research, it also emerged that certain risks affect the PHR dimensions directly, thus threatening the meaningfulness and usability of cloud-based PHRs. The literature review revealed that specific control measures can be adopted to mitigate the identified risks. These control measures form part of the material used in this study to identify the guidelines for secure cloud-based PHRs. The guidelines were formulated through the use of reasoning and argumentation. After the guidelines were formulated, elite interviews were conducted, in order to validate and finalize the main research output: i.e. guidelines. The results of this study may alert PHR providers to the risks that exist in the cloud environment; so that they can make informed decisions when choosing a CSP for storing their customers’ health data.

A Personal Health Record (PHR) is an electronic record of a patient’s health-related information that is managed by the patient. The patient can give access to other parties, such as healthcare providers and family members, as they see fit. These parties can use the information in emergency situations, in order to help improve the patient’s healthcare. PHRs have an important role to play in ensuring that a patient’s complete health history is available to his healthcare providers at the point of care. This is especially true in South Africa, where the majority of healthcare organizations still rely on paper-based methods of record-keeping. Research indicates that physicians play an important role in encouraging the adoption of PHRs amongst patients. Whilst various studies have focused on the perceptions of South African citizens towards PHRs, to date no research has focused on the perceptions of South African physicians. Considering the importance of physicians in encouraging the adoption of PHRs, the problem being addressed by this research project thus relates to the lack of information relating to the perceptions of South African physicians of PHRs. Physicians with private practices at private hospitals in Port Elizabeth, South Africa were surveyed in order to determine their perceptions towards PHRs. Results indicate perceptions regarding benefits to the physician and the patient, as well as concerns to the physician and the patient. The levels of trust in various potential PHR providers and the potential uses of a PHR for the physician were also explored. The results of the survey were compared with the results of relevant international literature in order to describe the perceptions of physicians towards PHRs.

The fragmented nature of modern health care provision makes it increasingly difficult to achieve continuity of care. This is equally true in the context of the South African healthcare landscape. This results in a strong emphasis on the informational dimension of continuity of care which highlights the importance of the continuity of medical records. Paper-based methods of record keeping are inadequate to support informational continuity of care which leads to an increased interest in electronic methods of record keeping through the adoption of various Health Information Technologies (HITs). This research project investigates the role that various HITs such as Personal Health Records (PHRs), Electronic Medical Records (EMRs), and Health Information Exchanges (HIEs) can play in improving informational continuity of care resulting in the development of a standards-based technological model for the South African healthcare sector. This technological model employs appropriate HITs to address the problem of informational continuity of care in the South African healthcare landscape The benefits that are possible through the adoption of the proposed technological model can only be realized if the proposed HITs are used in a meaningful manner once adopted and implemented. The Delphi method is employed to identify factors that need to be addressed to encourage the adoption and meaningful use of such HITs in the South African healthcare landscape. Lastly, guidelines are formulated to encourage the adoption and meaningful use of HITs in the South African healthcare landscape to improve the continuity of care. The guidelines address both the technological requirements on a high level, as well as the factors that need to be addressed to encourage the adoption and meaningful use of the technological components suggested. These guidelines will play a significant role in raising awareness of the factors that need to be addressed to create an environment conducive to the adoption and meaningful use of appropriate HITs in order to improve the continuity of care in the South African healthcare landscape.

Indiana University-Purdue University Indianapolis (IUPUI) / With the availability of electronic medical records, medication dispensing data offers an unprecedented opportunity for researchers to explore complex relationships among longterm medication use, disease progression and potential side-effects in large patient populations. However, these data also pose challenges to existing statistical models because both medication exposure status and its intensity vary over time. This dissertation focused on flexible models to investigate the association between time-varying exposures and different types of outcomes. First, a penalized functional regression model was developed to estimate the effect of time-varying exposures on multivariate longitudinal outcomes. Second, for survival outcomes, a regression spline based model was proposed in the Cox proportional hazards (PH) framework to compare disease risk among different types of time-varying exposures. Finally, a penalized spline based Cox PH model with functional interaction terms was developed to estimate interaction effect between multiple medication classes. Data from a primary care patient cohort are used to illustrate the proposed approaches in determining the association between antidepressant use and various outcomes. / NIH grants, R01 AG019181 and P30 AG10133.

Real-world evidence (RWE) plays a crucial role in understanding the impact of medical interventions and uncovering disparities in clinical practice. However, confounding bias, especially unmeasured confounding, poses challenges to inferring causal relationships from observational data, such as estimating treatment effects and treatment responses. Various methods have been developed to reduce confounding bias, including methods specific for detecting and adjusting for unmeasured confounding. However, these methods typically rely on assumptions that are either untestable or too strong to hold in practice. Some methods also require domain knowledge that is rarely available in medicine. Despite recent advances in method development, the challenge of unmeasured confounding in observational studies persists. This dissertation provides insights in adjusting for unmeasured confounding by exploiting correlations within electronic health records (EHRs). In Aim 1, we demonstrate a novel use of probabilistic model for inferring unmeasured confounders from drug co-prescription pattern. In Aim 2, we provide theoretical justifications and empirical evidence that adjusting for all (pre-treatment) covariates without explicitly selecting for confounders, as implemented in the large-scale propensity score (LSPS) method, offers a more robust approach to mitigating unmeasured confounding. In Aim 3, we shift focus to the problem of evaluating fairness of treatment allocation in clinical practice from a causal perspective. We develop a causal fairness algorithm for assessing treatment allocation. By applying this fairness analysis method to a cohort of patients with coronary artery disease from EHR data, we uncover disparities in treatment allocation based on gender and race, highlighting the importance of addressing fairness concerns in clinical practice. Furthermore, we demonstrate that social determinants of health, variables that are often unavailable in EHR databases and are potential unmeasured confounders, do not significantly impact the estimation of treatment responses when conditioned on clinical features from EHR data, shedding light on the intricate relationship between EHR features and social determinants of health. Collectively, this dissertation contributes valuable insights into addressing unmeasured confounding in the context of evidence generation from EHRs. These findings have significant implications for improving the reliability of observational studies and promoting equitable healthcare practices.

Real world data have enhanced healthcare research, improving our understanding of disease progression, aiding in diagnosis, and enabling the development of personalized and targeted treatments. In recent years, multi-omics data and electronic health record (EHR) data have become increasingly available, providing researchers with a wealth of information to analyze. The use of machine learning methods with EHR and multi-omics data has emerged as a promising approach to extract valuable insights from these complex data sources. This dissertation focuses on the development of supervised and unsupervised learning methods, as well as their applications to EHR and multi-omics data, with a particular emphasis on early detection of clinical outcomes and identification of novel cancer subtypes. The first part of the dissertation centers on developing a risk prediction tool using EHR data that enables disease early detection so that preventive treatments can be taken to better manage the disease. For this goal, we developed a similarity-based supervised learning method with two applications to predict end-stage kidney disease (ESKD) and aortic stenosis (AS). In the second part of the dissertation, we expanded our goal to a phenome-wide prediction task and developed a patient representation based deep learning method that is able to predict phenotypes across the phenome. Through a weighting scheme, this approach is conducting tailored disease phenotype prediction computationally efficiently with good prediction performance. In the final part of the dissertation, I shifted the focus with the goal to identify clinical meaningful novel disease subtypes with unsupervised learning methods using multi-omics data. We tackled this goal through integrating multiple patient graphs being generated from multiple omics data with molecular level features for an improved disease subtyping. This dissertation has significantly contributed to the development of data-driven approaches to healthcare and biomedical research using EHR data and multi-omics data. The new methodologies developed with applications in multiple diseases using EHR and multi-omics data advanced our knowledge in disease diagnosis, vulnerable groups identification, and ultimately improve patient care.