Phenomenological Assessment of Integrative Medicine Decision-making and the Utility of Predictive and Prescriptive Analytics Tools

The U.S. Healthcare system is struggling to manage the burden of chronic disease, racial and socio-economic disparities, and the debilitating impact of the current global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). More patients need alternatives to allopathic or “Western” medicine focused on fighting disease with mechanism, pharmaceuticals, and invasive measures. They are seeking Integrative Medicine which focuses on health and healing, emphasizing the centrality of the patient-physician relationship. In addition to providing the best conventional care, IM focuses on preventive maintenance, wellness, improved behaviors, and a holistic care plan. This qualitative research assessed whether predictive and prescriptive analytics (artificial intelligence tools that predict patient outcomes and recommend treatments, interventions, and medications) supports the decision-making processes of IM practitioners who treat patients suffering from chronic pain. PPA was used in a few U.S. hospitals but was not widely available for IM practitioners at the time of this research. Phenomenological interviews showed doctors benefit from technology that aggregates data, providing a clear patient snapshot. PPA exposed historical information that doctors often miss. However, current systems lacked the design to manage individualized, holistic care focused on the mind, body, and spirit. Using the Future-Focused Task-Technology Fit theory, the research suggested PPA could actually do more harm than good in its current state. Future technology must be patient-focused and designed with a better understanding of the IM task and group characteristics (e.g., the unique way providers practice medicine) to reduce algorithm aversion and increase adoption. In the ideal future state, PPA will surface healthcare Big Data from multiple sources, support communication and collaboration across the patient’s support system and community of care, and track the various objective and subjective factors contributing to the path to wellness

Opportunities and Benefits of People Analytics for HR Managers and Employees: Signals in the Grey Literature

With its promise to help leaders better understand and optimize their workforce, People Analytics is attracting increasing attention in Human Resource (HR) Management and has been recently defined as one of the top 10 HR technology disruptions that could transform the way we work and manage organizations. Despite this optimism, and the growing market in People Analytics tools and services, recent literature reviews show that it has been largely unexplored as a research topic, and it is little understood beyond HR innovators. We are currently analyzing social media, and the ‘grey literature’ it points to, to obtain insights into how scholars, business innovators, and HR are talking about the benefits and opportunities of People Analytics and the key sources of knowledge or evidence guiding this narrative. The provisional results reported here illustrate how we analyzed relevant Tweets with reference to an existing framework for classifying PA benefits for different HRM practices. This analysis, and our broader scoping review, aim to provide new insights for HR practitioners and academic researchers

Identifying Psychological Mental Disorders through Machine Learning

Psychology is the Study of Human Behavior and the various ways of thinking in it’s both consciousness and unconsciousness mind as this field is still growing to various fields such as Sports, Criminal and even Administrative Psychology through, Furthermore, This Study aims to shape and accommodate both fields of Sciences together, There are very few Scholars and Researches done on Medical Mental Health from a Data Analytics perspective, examining the datasets of the patients ICD-11 or DSM-5 scores and correlating it with the patients well-being, family medical history or any accident the patient have been through, so the current study approaches these strategies and enable the practitioners to discover the patients vulnerability of mental illness through data analytics, the literature review represented in this research provided an insight of the world practices and methodological examinations are being executed in approaching the patients through their care centers and illustrating the severeness of the Mental Illness the revising the risk factors that might lead the patients to even worst conditions, The Capstone Projects will analyze the influence of pandemics on the population\u27s mental health, The Second question will reveal the most important characteristics that may turn a mentally healthy individual into a mental patient. Data Analysis and Data Mining Methods were used to translate Tweets and Internet Searches of Human Interactions into data to detect their Mental Health Condition and assess the inmate\u27s greatest causes to encounter any sort of mental illness

A Systematic Review of Knowledge Visualization Approaches Using Big Data Methodology for Clinical Decision Support

This chapter reports on results from a systematic review of peer-reviewed studies related to big data knowledge visualization for clinical decision support (CDS). The aims were to identify and synthesize sources of big data in knowledge visualization, identify visualization interactivity approaches for CDS, and summarize outcomes. Searches were conducted via PubMed, Embase, Ebscohost, CINAHL, Medline, Web of Science, and IEEE Xplore in April 2019, using search terms representing concepts of: big data, knowledge visualization, and clinical decision support. A Google Scholar gray literature search was also conducted. All references were screened for eligibility. Our review returned 3252 references, with 17 studies remaining after screening. Data were extracted and coded from these studies and analyzed using a PICOS framework. The most common audience intended for the studies was healthcare providers (n = 16); the most common source of big data was electronic health records (EHRs) (n = 12), followed by microbiology/pathology laboratory data (n = 8). The most common intervention type was some form of analysis platform/tool (n = 7). We identified and classified studies by visualization type, user intent, big data platforms and tools used, big data analytics methods, and outcomes from big data knowledge visualization of CDS applications

Integrative Learning and Interdisciplinary Information Systems Curriculum Development in Accounting Analytics

This paper develops the structure for an integrative model information systems curriculum on Accounting Analytics, which affords students the opportunities to develop domain knowledge along with application of data analytics. As industry experiences rapid technological change, university curricula must remain current in order to be effective. Curriculum content is further advanced and established with input from industry organizations that employ graduates of the programs. The paper output includes a curriculum review of top accounting programs, course curriculum map, accounting data skills matrix, and professional opportunities. The curriculum review utilizes an empirical text analytics methodological approach to extract patterns and develop additional insights for the advancement of accounting information systems research. To minimize curricular disruption, existing courses can be utilized as core curriculum, enhancing key courses to complete undergraduate, graduate, or certificate programs. The Accounting Analytics customized curriculum provides students an opportunity to take advantage of the growing interdisciplinary field and student interest among accounting and analytical career paths. The integrative curriculum is developed to better prepare graduates with the critical knowledge, skills, and abilities to excel in this new-age workforce

SIGNIFICANCE OF ANALYTICS

Despite the number of times marketers have been using analytics as a tool to predict and drive consumer behavior, it is a relatively new application of the science. Technology continues to evolve and offers even more data choices and metrics for analysis, increasing the abilities of marketers to reach their audience. This article expands on several sectors of use, including real estate, social media, and healthcare, and theorizes the impact that analytics will have in the future as the technological means to interpret data catches up with the sheer amount of real-time information available for potential use, especially with the development of the Internet of Things, and rising concerns around data use, regarding data protection and copyright.  Article visualizations

A Data Science approach to behavioural change: large scale interventions on physical activity and weight loss

This PhD thesis is a quantitative investigation combining Behaviour Change Science with a Data Science approach in search of more effective large scale, multi-component behavioural interventions for health and well-being. There is limited evidence about how technology-based interventions (including those using wearable physical activity monitors and apps) are efficacious for increasing physical activity and nutrition. The relevance of this research is the systematic approach to overcome previous studies’ limitations in method and measurement: restricted research about multi-component interventions, limited analysis about the impact of social networking, the inclusion of components without sufficient evidence about the components’ effectiveness, the absence of a control group(s), small sample sizes, subjective physical activity reporting, among other limitations. The research was done in conjunction with Tictrac Ltd as the industrial partner, and the UCL Centre for Behaviour Change. Tictrac Ltd builds platforms for the collection and aggregation of personal data generated by the users’ devices and mobile apps. The collaboration with the UCL Centre for Behaviour Change has been instrumental to design, implement, evaluate and analyse behaviour change interventions that impact wellbeing and health. The thesis comprises three areas of research: 1. Computational platforms for large scale behavioural interventions. To support this research, computational platforms were designed, built, deployed and used for randomised behavioural interventions with control groups. The interventions were implemented as experiments related to the behavioural impact on physical activity, weight loss and change in diet. / 2. Behaviour change experiments. The two experiments use the Behaviour Change Wheel framework for behaviour change, intervention design and evaluation. A Data Science approach was used to test hypotheses, determine and quantify the effect of the fundamental intervention components and their interactions. The effective use of tracking devices and apps was determined by comparing the results of ‘structured intervention’ –vs- those of the control group. / Experiment 1: Large scale intervention in a corporate wellness setting. Multi-component behavioural intervention with: control group, self-defined goals, choice architecture and personal dashboards for physical activity and weight loss. The analysis covers network effects of social interactions, the role of being explicit about a type of goal, the impact of making part of team, among other relevant outcomes. / Experiment 2: Identification of critical factors of a technology-based intervention. Multi-component behavioural intervention with simultaneous target behaviours related to weight loss and physical activity, inspired by factorial design for the determination of critical factors and effective components. The analysis comprises: components’ interactions (coach, challenge, team, action plans, forum), non-linear relationships (BMI, change in diet habit), five personality traits, among other relevant results. / 3. Frameworks for future large scale interventions in behaviour change. The implementation of both experiments required an applied use of theoretical and practical principles for the design of the experimental computational platforms. As a result, two frameworks were suggested for future interventions: an implementation framework and a data strategy framework