The path to improving the quality of laboratory documentation : (a case study from Cameroon)

Health care systems nowadays are affected by quality problems, most of which occur in developing countries due to the lack of adequate infrastructural, human, and financial resources. This has also caused the data quality generated in developing countries to be often poor. As a result, most governments in developing countries are in the process of improving quality in their health care systems through the introduction of Information Technology (IT) support systems. This thesis explored the challenges and opportunities involved in the path to improving the quality of laboratory documentation in a Cameroonian hospital. The study employed the qualitative research approach whereby interpretive research methods were used during data collection. These consisted of participant observations, interviews, and document analysis. A total of 24 respondents were interviewed comprising of 19 hospital staff and 5 patients. The data was collected at the medical laboratory department of the Regional Hospital Bamenda over a period of two months. The theories of Information Infrastructures and Actor Network guided the study, that is, they were used to discuss the laboratory documentation, and the implementation of the IT support system in the everyday work practice. The study findings primarily revealed certain quality-related lapses in the laboratory documentation. For example, illegible laboratory test orders, common errors in laboratory test ordering and result reporting, just to name a few. It further revealed that IT support systems have great potential to improve upon the quality of the laboratory documentation. Thus, it suggested that a tailored IT support system could be implemented to address this issue. However, the greatest challenge discovered was the lack of resources to make this happen. Based on these findings, it was suggested that if resources are made available to implement this system, the socio-technical approach should be employed in order to ensure success. This is because this approach has proven to be effective since it does not only take into consideration the new technology implemented, but also the interaction between the technology and its users

Investigation of the use of ICT in the modernization of the health care sector : a comparative analysis

This Ph.D project started from a broad analysis aiming at investigating the key issues in the development of Information and Communication Technologies (ICT) in the health care sector, with the aim of making an in depth investigation to evaluate the effects of Electronic Medical Record (EMR) implementation on the organizations adopting them. Furthermore the study examined two study settings which have adopted the same EMR system produced by the same provider. This comparative study aims, in particular, to analyse how EMR systems are adopted by different health organizations focusing on the antecedents of the EMR project, on the implementation processes used and on the impacts produced. Diffusion theory, through the lens of socio-technical approach, represents the theoretical framework of the analysis. The research results are based on policy evaluation and case studies. The two hospitals selected for the case study analysis are the Regional Hospital of Local Health Authority in Aosta, Italy and the Royal Infirmary of Edinburgh, Scotland. In conducting the data collection several strategies have been used: documentary analysis, interviews and observations have been carried out. This work provides an overview of the key issues arising over e-health policy development through a comparative analysis of the UK and Italy and provides an insight into how EMR systems are adopted, implemented and evaluated within acute care organizations. The thesis is a comparative international research about the development of e-health and the use of ICT in health care sector. This approach makes a both a theoretical and methodological contribution. By focusing, in particular, on EMR systems, it offers to practitioners and policy makers a better basis of analysing ICT usage and its impacts on health care service delivery

The Anesthesia Continuing Education Market and the Value Creation From a Sustainable Unified Platform

Practicing anesthesia professionals in the United States are all governed by various profession-specific regulatory bodies that mandate continuing education (CE) requirements. To date, no unified resource exists for anesthesia professionals (i.e., Anesthesiologists, Certified Registered Nurse Anesthetists, and Anesthesiologist Assistants) to explore the CE offerings available within the marketplace. This study endeavored to convey the potential value of a unified anesthesia CE resource. It investigated how to cultivate a sustainable platform to potentially improve how anesthesia professionals search available CE offerings and to potentially enhance how anesthesia CE providers reach anesthesia professionals. This qualitative study was conducted utilizing an integrative review of the literature. The key concepts identified and investigated were network effect, segmentation, first to market, best of breed, search costs, transaction costs, minimally viable product, evolutionary phases of platforms, platform theory, platform business model, platform economy, and types of platforms. Inductive content analysis was chosen as the organizational method for the resultant qualitative data. The goal of the analysis was to create a conceptual, practical, and strategically applicable platform paradigm for the anesthesia CE marketplace driven by the insights and amalgamations from the literature. The analyzed concepts, dimensions, and indicators of platform successes and their applications potentially facilitate anesthesia professionals’ CE explorations and CE providers’ marketing efforts, as well as contextualize the overarching impacts and implications onto the anesthesia CE industry and beyond. The conclusion portrays these impacts and implications

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field

AI alignment and generalization in deep learning

This thesis covers a number of works in deep learning aimed at understanding and improving generalization abilities of deep neural networks (DNNs). DNNs achieve unrivaled performance in a growing range of tasks and domains, yet their behavior during learning and deployment remains poorly understood. They can also be surprisingly brittle: in-distribution generalization can be a poor predictor of behavior or performance under distributional shifts, which typically cannot be avoided in practice. While these limitations are not unique to DNNs -- and indeed are likely to be challenges facing any AI systems of sufficient complexity -- the prevalence and power of DNNs makes them particularly worthy of study. I frame these challenges within the broader context of "AI Alignment": a nascent field focused on ensuring that AI systems behave in accordance with their user's intentions. While making AI systems more intelligent or capable can help make them more aligned, it is neither necessary nor sufficient for alignment. However, being able to align state-of-the-art AI systems (e.g. DNNs) is of great social importance in order to avoid undesirable and unsafe behavior from advanced AI systems. Without progress in AI Alignment, advanced AI systems might pursue objectives at odds with human survival, posing an existential risk (``x-risk'') to humanity. A core tenet of this thesis is that the achieving high performance on machine learning benchmarks if often a good indicator of AI systems' capabilities, but not their alignment. This is because AI systems often achieve high performance in unexpected ways that reveal the limitations of our performance metrics, and more generally, our techniques for specifying our intentions. Learning about human intentions using DNNs shows some promise, but DNNs are still prone to learning to solve tasks using concepts of "features" very different from those which are salient to humans. Indeed, this is a major source of their poor generalization on out-of-distribution data. By better understanding the successes and failures of DNN generalization and current methods of specifying our intentions, we aim to make progress towards deep-learning based AI systems that are able to understand users' intentions and act accordingly.Cette thèse discute quelques travaux en apprentissage profond visant à comprendre et à améliorer les capacités de généralisation des réseaux de neurones profonds (DNN). Les DNNs atteignent des performances inégalées dans un éventail croissant de tâches et de domaines, mais leur comportement pendant l'apprentissage et le déploiement reste mal compris. Ils peuvent également être étonnamment fragiles: la généralisation dans la distribution peut être un mauvais prédicteur du comportement ou de la performance lors de changements de distribution, ce qui ne peut généralement pas être évité dans la pratique. Bien que ces limitations ne soient pas propres aux DNN - et sont en effet susceptibles de constituer des défis pour tout système d'IA suffisamment complexe - la prévalence et la puissance des DNN les rendent particulièrement dignes d'étude. J'encadre ces défis dans le contexte plus large de «l'alignement de l'IA»: un domaine naissant axé sur la garantie que les systèmes d'IA se comportent conformément aux intentions de leurs utilisateurs. Bien que rendre les systèmes d'IA plus intelligents ou capables puisse aider à les rendre plus alignés, cela n'est ni nécessaire ni suffisant pour l'alignement. Cependant, être capable d'aligner les systèmes d'IA de pointe (par exemple les DNN) est d'une grande importance sociale afin d'éviter les comportements indésirables et dangereux des systèmes d'IA avancés. Sans progrès dans l'alignement de l'IA, les systèmes d'IA avancés pourraient poursuivre des objectifs contraires à la survie humaine, posant un risque existentiel («x-risque») pour l'humanité. L'un des principes fondamentaux de cette thèse est que l'obtention de hautes performances sur les repères d'apprentissage automatique est souvent un bon indicateur des capacités des systèmes d'IA, mais pas de leur alignement. En effet, les systèmes d'IA atteignent souvent des performances élevées de manière inattendue, ce qui révèle les limites de nos mesures de performance et, plus généralement, de nos techniques pour spécifier nos intentions. L'apprentissage des intentions humaines à l'aide des DNN est quelque peu prometteur, mais les DNN sont toujours enclins à apprendre à résoudre des tâches en utilisant des concepts de «caractéristiques» très différents de ceux qui sont saillants pour les humains. En effet, c'est une source majeure de leur mauvaise généralisation sur les données hors distribution. En comprenant mieux les succès et les échecs de la généralisation DNN et les méthodes actuelles de spécification de nos intentions, nous visons à progresser vers des systèmes d'IA basés sur l'apprentissage en profondeur qui sont capables de comprendre les intentions des utilisateurs et d'agir en conséquence

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality