Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016. / Health information policies are constantly becoming a key feature in directing information usage in healthcare. After the passing of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 and the Affordable Care Act (ACA) passed in 2010, in the United States, there has been an increase in health systems innovations. Coupling this health systems hype is the current buzz concept in Information Technology, „Big data‟. The prospects of big data are full of potential, even more so in the healthcare field where the accuracy of data is life critical. How big health data can be used to achieve improved health is now the goal of the current health informatics practitioner. Even more exciting is the amount of health data being generated by patients via personal handheld devices and other forms of technology that exclude the healthcare practitioner. This patient-generated data is also known as Personal Health Records, PHR. To achieve meaningful use of PHRs and healthcare data in general through big data, a couple of hurdles have to be overcome. First and foremost is the issue of privacy and confidentiality of the patients whose data is in concern. Secondly is the perceived trustworthiness of PHRs by healthcare practitioners. Other issues to take into context are data rights and ownership, data suppression, IP protection, data anonymisation and reidentification, information flow and regulations as well as consent biases. This study sought to understand the role of data policies in the process of data utilisation in the healthcare sector with added interest on PHRs utilisation as part of big health data.

Indiana University-Purdue University Indianapolis (IUPUI) / BACKGROUND: The pathophysiology of diabetes mellitus and the need for vigilant monitoring of serum glucose levels lends itself well to prompt medical intervention by healthcare providers that can significantly reduce morbidity and mortality and improve patient quality of life. The effect of intervention in diabetes can be assessed by following objective laboratory measurements such as hemoglobin A1C, which is abnormal with poorly controlled diabetes and returns to normal with proper management. There are mobile technologies now available that allow for self-monitoring and intervention in this patient population. Using a systematic approach this paper will assess the benefits of Short Message Services and mobile technology in managing patients with diabetes and improving adherence and other outcomes. OBJECTIVE: To assess the benefits and disadvantages the use of mobile technology could have in the management of diabetes. METHODOLOGY: A systematic review of articles on this topic was performed. A total of 759 articles were initially identified by searching various search engines, from which only 39 articles met all of the inclusion/exclusion criteria of this systematic review. FINDINGS: The initial review of literature indicated that the use of mobile technology in patients with diabetes resulted in improved disease outcomes as indicated by parameters such as a decrease in hemoglobin A1C, and an increase in sustainable blood glucose levels. CONCLUSION: Mobile technology is found to be a promising tool in the management of diabetes, but further research is needed because there is a lack of reliable studies, trials, and systematic reviews. Physicians and other healthcare professionals are rapidly adopting mobile technology for use in clinical practice because they understand the rising phenomenon of mobile technology.

Indiana University-Purdue University Indianapolis (IUPUI) / Physical activity monitoring using wearable sensors give valuable information about patient's neuro activities. Fall among ages of 60 and older in US is a leading cause for injury-related health issues and present serious concern in the public health care sector. If the emergency treatments are not on time, these injuries may result in disability, paralysis, or even death. In this work, we present an approach that early detect fall occurrences. Low power capacitive accelerometers incorporated with microcontroller processing units were utilized to early detect accurate information about fall events. Decision tree algorithms were implemented to set thresholds for data acquired from accelerometers. Data is then verified against their thresholds and the data acquisition decision unit makes the decision to save patients from fall occurrences. Daily activities are logged on an onboard memory chip with Bluetooth option to transfer the data wirelessly to mobile devices. In this work, a system prototype based on neurosignal activities was built and tested against seven different daily human activities for the sake of differentiating between fall and non-fall detection. The developed system features low power, high speed, and high reliability. Eventually, this study will lead to wearable fall detection system that serves important need within the health care sector. In this work Inter-Integrated Circuit (I2C) protocol is used to communicate between the accelerometers and the embedded control system. The data transfer from the Microcontroller unit to the mobile device or laptop is done using Bluetooth technology.