The use of electronic narratives records to support the decision-making process in oncology care at private hospitals in Cape Town

Thesis (MTech (Information technology))--Cape Peninsula University of Technology, 2019Electronic narratives are recognised for their significant contribution to healthcare – emphasising that the patient’s narrative should not only be included, but valued. The survival rate of cancer patients in the UK, USA, Italy and Australia are improving, making it necessary to investigate the use of electronic narratives in private oncology centres. This research, conducted in Cape Town, South Africa, started off by critically analysing available scientific information. Subsequently, a gap was identified regarding the use of electronic narratives as a way of acquiring important data from patients – something that is crucial in the treatment process (from the pre-diagnosis to the follow-up), and in decision-making. The lack of narratives in electronic health records (EHRs) could affect the quality of the decision-making process, particularly for chronic non-communicable diseases (NCD); which could result in administering incorrect dosages of medication leading to deterioration of the patient’s health, and in some cases, even death. The purpose of this research was to explore the use of narratives in electronic health records to support the decision-making process by healthcare professionals in private oncology care. The study was qualitative; hence interviews were used for data collection. A purposive sample of eighteen healthcare professionals (oncologists, psychiatrists and general practitioners) was used in this study. The data was then analysed thematically, and the interpretation thereof done subjectively. The key findings of this study indicate that electronic health records are used considerably in private oncology care due to benefits such as real-time access to information and easy back-up. Healthcare professionals acknowledge that narratives are present in oncology care, and mainly used in the diagnosis phase. These narratives are mostly in note format (hand-written on paper). These written notes are then later recorded into the patient’s electronic health record which, in many cases, results in the omission of important information, because not everything the patient said is transcribed into medical jargon. The current system in private oncology care does not support electronic narratives even though healthcare professionals express an interest in using this. The findings further suggest that to successfully implement electronic narratives, there are basic prerequisites such as a computer or tablet, recording devices and software. Furthermore, the findings show that electronic narratives are often not used due to limited knowledge, lack of interest, specific cultural practices, and the fear of change. To alter and positively transform healthcare professionals’ and patients’ views of electronic narratives, the researcher recommends educating healthcare professionals about the value of patients’ narratives. In other words, providing training is crucial as narratives contain values that aid constructive decision-making. Furthermore, since narratives involve patients, extending training to the patients will be beneficial. The findings of this study contribute to the current literature on electronic health records and narratives in private oncology care of South Africa

Data virtualization design model for near real time decision making in business intelligence environment

The main purpose of Business Intelligence (BI) is to focus on supporting an organization‘s strategic, operational and tactical decisions by providing comprehensive, accurate and vivid data to the decision makers. A data warehouse (DW), which is considered as the input for decision making system activities is created through a complex process known as Extract, Transform and Load (ETL). ETL operates at pre-defined times and requires time to process and transfer data. However, providing near real time information to facilitate the data integration in supporting decision making process is a known issue. Inaccessibility to near realtime information could be overcome with Data Virtualization (DV) as it provides unified, abstracted, near real time, and encapsulated view of information for querying. Nevertheless, currently, there are lack of studies on the BI model for developing and managing data in virtual manner that can fulfil the organization needs. Therefore, the main aim of this study is to propose a DV model for near-real time decision making in BI environment. Design science research methodology was adopted to accomplish the research objectives. As a result of this study, a model called Data Virtualization Development Model (DVDeM) is proposed that addresses the phases and components which affect the BI environment. To validate the model, expert reviews and focus group discussions were conducted. A prototype based on the proposed model was also developed, and then implemented in two case studies. Also, an instrument was developed to measure the usability of the prototype in providing near real time data. In total, 60 participants were involved and the findings indicated that 93% of the participants agreed that the DVDeM based prototype was able to provide near real-time data for supporting decision-making process. From the studies, the findings also showed that the majority of the participants (more than 90%) in both of education and business sectors, have affirmed the workability of the DVDeM and the usability of the prototype in particular able to deliver near real-time decision-making data. Findings also indicate theoretical and practical contributions for developers to develop efficient BI applications using DV technique. Also, the mean values for each measurement item are greater than 4 indicating that the respondents agreed with the statement for each measurement item. Meanwhile, it was found that the mean scores for overall usability attributes of DVDeM design model fall under "High" or "Fairly High". Therefore, the results show sufficient indications that by adopting DVDeM model in developing a system, the usability of the produced system is perceived by the majority of respondents as high and is able to support near real time decision making data

Intensive care nurses’ attitudes, beliefs and reported practices relating to patient sleep: A descriptive study

Empirical evidence suggests that patients treated in the intensive care unit (ICU) experience chronic sleep disturbance, leading to sleep deprivation. Multiple intrinsic and external factors contribute to poor quantity and quality of sleep among critically ill patients. Noise, light and clinical interventions are some of the external factors most disruptive to patient sleep in the ICU. Given that nurses are the gatekeepers to the ICU, understanding their perceptions and practices relating to patient sleep is necessary to elicit change. However, ICU nurses’ attitudes, beliefs and practices relating to sleep are poorly understood. Using a descriptive survey method, this study investigated the self-reported attitudes, beliefs and practices of ICU nurses in a tertiary hospital in the metropolitan area of Perth, Western Australia. A questionnaire with quantitative and qualitative elements was used as the instrument for data collection. Eighty-four nurses from a target population of 180 participated in this study (47%). Over half of the respondents held postgraduate qualifications and nearly all had worked in other ICU settings, given that the study ICU had only opened in 2014. The majority of respondents had not received any education on patient sleep and had not worked in an ICU with a clinical practice guideline or sleep-promotion protocol. The findings suggest that the nurses believed it is important for patients to achieve adequate quantity and quality sleep while in the ICU; however, the sleep patients currently experience is insufficient and adversely affects a multitude of patient outcomes, including the development of delirium. The nurses believed that patients are concerned about sleep disturbance; however, they were divided in opinion regarding whether their colleagues were equally concerned. Sleep assessment in this setting is difficult and occurs without the use or knowledge of sleep assessment tools. Most sleep-promotion practices are considered important, yet are not performed consistently. A plethora of barriers to patient sleep were identified, with nurses describing a lack of control in managing these in the ICU setting. A focus on solutions was identified, with respondents unequivocally suggesting that education and routine, policy and culture change is required to better support patient sleep. The results of this study contribute to the growing body of knowledge on patient sleep in the ICU and the modifiable factors that contribute to sleep disruption. These insights will inform the development of education, policy and protocol to support sleep in the critically ill patient

Effects of an e-learning programme on osteopaths’ back pain attitudes: a mixed methods feasibility study

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy.i. Background Guidelines recommend the biopsychosocial (BPS) model for managing non-specific low back pain (NSLBP) but the best method for teaching this model is unclear. Printed material and face-to-face learning have limited effects on practitioners’ attitudes to back pain. An alternative way is needed and e-learning is a promising option. E-learning is becoming an important part of teaching, but little guidance is available to the osteopathic profession. ii. Purpose This study had four aims. First to assess the feasibility of running a main trial to test the effectiveness of an e-learning programme on the BPS model for NSLBP on experienced practitioners’ attitudes to back pain; secondly, to assess the acceptability of the e-learning programme and the use of the internet as a mode of CPD; thirdly to provide an effect size estimate; and finally to explore the participants’ views on the e-learning programme and its possible impact on their reported behaviour. iii. Methods First a scoping review of the BPS factors and assessment methods for NSLBP was conducted. It informed the content of an e-learning programme that was designed and developed, and informed by a behaviour change model and an e-learning developmental model. An explanatory mixed methods feasibility study was conducted: first, a pilot Randomised Controlled Trial (RCT) assessed experienced osteopaths’ attitudes before and after the intervention, using the Pain Attitudes and Beliefs Scale (PABS) and the Attitudes to Back Pain Scale for musculoskeletal practitioners (ABS-mp); then semi-structured interviews explored participants’ views on the e-learning programme and its possible impact on their reported practice behaviours. ii iv. Results 45 osteopaths, each with at least 15 years of experience consented to, and took part in, the study. The two trial arms were: a 6-week e-learning programme (intervention group) and a waiting-list group (control group). 9 participants were interviewed for the qualitative strand. The feasibility of conducting a main trial was good, the intervention was well accepted and the adherence to the intervention was good. An effect size estimate was calculated to inform sample size for a main trial. In the qualitative strand, participants’ views on the BPS model fell in with the themes of being Not structural enough, being Part of existing practice and being Transformative. v. Conclusion(s) This study provided new knowledge that had not been reported before in several areas:  how an e-learning programme for experienced manual practitioners should be developed,  a new intervention was reported (e-learning programme), including its design and acceptability,  osteopaths’ views on using the internet as a form of CPD,  information on the challenges faced in implementing a BPS approach

Exploring Factors Impacting Veterans\u27 Hypertension Control

Exploring factors impacting veterans’ hypertension (HTN) control is essential in reducing the common cardiovascular disease event of stroke, heart attack, chronic kidney disease and mortality. The purpose of this study was to describe multidimensional factors impacting veterans’ HTN control. Utilizing a descriptive, exploratory, cross-sectional, retrospective, convenience sampling research design, 331 electronic medical records in a VA dashboard data set were reviewed for men and women veterans between the age 24 and 98 years (M=62, SD=13) in a Southern California Veterans Affair primary care clinic from October 17, 2014 through October 17, 2016. Data were analyzed by univariate, bivariate and multivariate statistics. The study found that self-reported medication adherence to antihypertensive drugs (p \u3c .001) was an independent predictor to veterans’ final systolic blood pressure. Future HTN control research may focus on some theory-guided multidimensional determinants of patients’ adherence to HTN treatments and HTN control outcomes fully, utilizing a consistent HTN definition defined by JNC-7

Exploring Factors Impacting Veterans\u27 Hypertension Control

Exploring factors impacting veterans’ hypertension (HTN) control is essential in reducing the common cardiovascular disease event of stroke, heart attack, chronic kidney disease and mortality. The purpose of this study was to describe multidimensional factors impacting veterans’ HTN control. Utilizing a descriptive, exploratory, cross-sectional, retrospective, convenience sampling research design, 331 electronic medical records in a VA dashboard data set were reviewed for men and women veterans between the age 24 and 98 years (M=62, SD=13) in a Southern California Veterans Affair primary care clinic from October 17, 2014 through October 17, 2016. Data were analyzed by univariate, bivariate and multivariate statistics. The study found that self-reported medication adherence to antihypertensive drugs (p \u3c .001) was an independent predictor to veterans’ final systolic blood pressure. Future HTN control research may focus on some theory-guided multidimensional determinants of patients’ adherence to HTN treatments and HTN control outcomes fully, utilizing a consistent HTN definition defined by JNC-7

How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Reportcard [in press]

Within the last decade, the application of supervised machine learning (SML) has become increasingly popular in the field of information systems (IS) research. Although the choices among different data preprocessing techniques, as well as different algorithms and their individual implementations, are fundamental building blocks of SML results, their documentation—and therefore reproducibility—is inconsistent across published IS research papers. This may be quite understandable, since the goals and motivations for SML applications vary and since the field has been rapidly evolving within IS. For the IS research community, however, this poses a big challenge, because even with full access to the data neither a complete evaluation of the SML approaches nor a replication of the research results is possible. Therefore, this article aims to provide the IS community with guidelines for comprehensively and rigorously conducting, as well as documenting, SML research: First, we review the literature concerning steps and SML process frameworks to extract relevant problem characteristics and relevant choices to be made in the application of SML. Second, we integrate these into a comprehensive “Supervised Machine Learning Reportcard (SMLR)” as an artifact to be used in future SML endeavors. Third, we apply this reportcard to a set of 121 relevant articles published in renowned IS outlets between 2010 and 2018 and demonstrate how and where the documentation of current IS research articles can be improved. Thus, this work should contribute to a more complete and rigorous application and documentation of SML approaches, thereby enabling a deeper evaluation and reproducibility / replication of results in IS research

Prediction of user behaviour on the web

The Web has become an ubiquitous environment for human interaction, communication, and data sharing. As a result, large amounts of data are produced. This data can be utilised by building predictive models of user behaviour in order to support business decisions. However, the fast pace of modern businesses is creating the pressure on industry to provide faster and better decisions. This thesis addresses this challenge by proposing a novel methodology for an effcient prediction of user behaviour. The problems concerned are: (i) modelling user behaviour on the Web, (ii) choosing and extracting features from data generated by user behaviour, and (iii) choosing a Machine Learning (ML) set-up for an effcient prediction. First, a novel Time-Varying Attributed Graph (TVAG) is introduced and then a TVAG-based model for modelling user behaviour on the Web is proposed. TVAGs capture temporal properties of user behaviour by their time varying component of features of the graph nodes and edges. Second, the proposed model allows to extract features for further ML predictions. However, extracting the features and building the model may be unacceptably hard and long process. Thus, a guideline for an effcient feature extraction from the TVAG-based model is proposed. Third, a method for choosing a ML set-up to build an accurate and fast predictive model is proposed and evaluated. Finally, a deep learning architecture for predicting user behaviour on the Web is proposed and evaluated. To sum up, the main contribution to knowledge of this work is in developing the methodology for fast and effcient predictions of user behaviour on the Web. The methodology is evaluated on datasets from a few Web platforms, namely Stack Exchange, Twitter, and Facebook