Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

Embedding patient safety in a scaffold of interprofessional education; a qualitative study with thematic analysis

BACKGROUND

Regardless of a proliferation of interest in reducing unsafe practices in healthcare, threats to patient safety (PS) remain high. Moreover, little attention has been paid towards the role of interprofessional education (IPE) in enhancing PS. This qualitative study was conducted to unfold the insights of the senior medical, dental and health sciences students at the University of Sharjah (UoS) in the United Arab Emirates (UAE) about PS in an online IPE-based workshop.

METHODS

This inductive thematic analysis study was conducted on senior medical and health students at the Colleges of Medicine, Dental Medicine, Health Sciences, and Pharmacy of UoS. During an online workshop, students discussed plausible solutions for four real practice-based clinical scenarios with elements of unsafe healthcare practices. During the breakout rooms, the students exhibited high level of articulation and proactively participated in discussions. The data from the online workshop were transcribed and then coding, categorizing, and labelling of recurrent themes were carried out. Multiple individual deliberations, consolidation, incorporation of the identified preliminary themes, and merging and reorganizing sub-themes led to a final thematic framework.

RESULTS

This work delved into the perspectives of 248 students regarding teamwork, communication, problem-solving, and other aspects concerning PS in interprofessional settings in an online workshop. The iterative process of data transcription, curating and qualitative analysis surfaced 32 codes. Later, the inductive themaric analysis yielded five themes with distinct yet interconnected nested subthemes in the context of PS in IPE settings. These themes of information sharing and grounding (problem-solving, social skills), maintaining communication (clinical reasoning, shared mental model), executing interprofessional activities (collaborative practice, collaboration scripts), professional cognitive abilities (cognitive maturity, metacognition), and negotiating professional identities (systematic change, socio-economic scaffolding) emerged as fundamental pillars for enhancing PS in healthcare.

CONCLUSION

Our study demonstrated the outcome of an innovative and team-based workshop which embedded PS within a scaffold of IPE environment. This research calls for incorporation of the emerging areas of clinical reasoning, problem solving, collaborative practice, and shared mental model into medical curricula for structured IPE in improving PS domains in medical education. These findings underscore the need for multifaceted dimensions of IPE imperatives for cultivating collaborative competence.

The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19

In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th-11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics-IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.

Sustainable development goals for gender equality: Extracting associations among the indicators of SDG 5 using numerical association rule mining

The United Nations prescribed the Sustainable Development Goals (SDGs) to various nations to provide enduring answers to widespread problems and to give long-lasting solutions to common issues being faced across the globe. SDG 5 in particular was aimed at minimizing gender inequality by employing 9 targets and 14 indicators. The indicators serve as a yardstick to measure the progress of each of the 9 targets. This research takes an in-depth look at the perspectives of SDG 5 –Gender Inequalities, its targets, and indicators. Furthermore, explanatory data analysis and numerical association rule mining alongside QuantMiner are applied to the generated Indian datasets on SDG 5 to extract patterns and associations among the fourteen indicators of SDG 5. The association rule mining carried out on the indicators reveals the pattern of association among these indicators. Legal provision for women and the rate of crimes against women have a perfect association of 100% while the association between legal provision for women and women who have experienced physical violence stands at 80%. The full relationships of all the 14 indicators are discussed extensively in the result and discussion section. Overall, it is established that these indicators are interdependent. This will make it easier for academics, the general public, and governmental and non-governmental organizations to understand the trends and form informed opinions on issues relating to gender inequality and SDG 5.

Decision-Making Using Big Data Relevant to Sustainable Development Goals (SDGs)

Policymakers, practitioners, and researchers around the globe have been acting in a coordinated manner, yet remaining independent, to achieve the seventeen Sustainable Development Goals (SDGs) defined by the United Nations. Remarkably, SDG-centric activities have manifested a huge information silo known as big data. In most cases, a relevant subset of big data is visualized using several two-dimensional plots. These plots are then used to decide a course of action for achieving the relevant SDGs, and the whole process remains rather informal. Consequently, the question of how to make a formal decision using big data-generated two-dimensional plots is a critical one. This article fills this gap by presenting a novel decision-making approach (method and tool). The approach formally makes decisions where the decision-relevant information is two-dimensional plots rather than numerical data. The efficacy of the proposed approach is demonstrated by conducting two case studies relevant to SDG 12 (responsible consumption and production). The first case study confirms whether or not the proposed decision-making approach produces reliable results. In this case study, datasets of wooden and polymeric materials regarding two eco-indicators (CO2 footprint and water usage) are represented using two two-dimensional plots. The plots show that wooden and polymeric materials are indifferent in water usage, whereas wooden materials are better than polymeric materials in terms of CO2 footprint. The proposed decision-making approach correctly captures this fact and correctly ranks the materials. For the other case study, three materials (mild steel, aluminum alloys, and magnesium alloys) are ranked using six criteria (strength, modulus of elasticity, cost, density, CO2 footprint, and water usage) and their relative weights. The datasets relevant to the six criteria are made available using three two-dimensional plots. The plots show the relative positions of mild steel, aluminum alloys, and magnesium alloys. The proposed decision-making approach correctly captures the decision-relevant information of these three plots and correctly ranks the materials. Thus, the outcomes of this article can help those who wish to develop pragmatic decision support systems leveraging the capacity of big data in fulfilling SDGs.

Are Global Environmental Uncertainties Inevitable? Measuring Desertification for the SDGs

Continuing uncertainty about the present magnitudes of global environmental change phenomena limits scientific understanding of human impacts on Planet Earth, and the quality of scientific advice to policy makers on how to tackle these phenomena. Yet why global environmental uncertainties are so great, why they persist, how their magnitudes differ from one phenomenon to another, and whether they can be reduced is poorly understood. To address these questions, a new tool, the Uncertainty Assessment Framework (UAF), is proposed that builds on previous research by dividing sources of environmental uncertainty into categories linked to features inherent in phenomena, and insufficient capacity to conceptualize and measure phenomena. Applying the UAF shows that, based on its scale, complexity, areal variability and turnover time, desertification is one of the most inherently uncertain global environmental change phenomena. Present uncertainty about desertification is also very high and persistent: the Uncertainty Score of a time series of five estimates of the global extent of desertification shows limited change and has a mean of 6.8, on a scale from 0 to 8, based on the presence of four conceptualization uncertainties (terminological difficulties, underspecification, understructuralization and using proxies) and four measurement uncertainties (random errors, systemic errors, scalar deficiencies and using subjective judgment). This suggests that realization of the Land Degradation Neutrality (LDN) Target 15.3 of the UN Sustainable Development Goal (SDG) 15 (“Life on Land”) will be difficult to monitor in dry areas. None of the estimates in the time series has an Uncertainty Score of 2 when, according to the UAF, evaluation by statistical methods alone would be appropriate. This supports claims that statistical methods have limitations for evaluating very uncertain phenomena. Global environmental uncertainties could be reduced by devising better rules for constructing global environmental information which integrate conceptualization and measurement. A set of seven rules derived from the UAF is applied here to show how to measure desertification, demonstrating that uncertainty about it is not inevitable. Recent review articles have advocated using ‘big data’ to fill national data gaps in monitoring LDN and other SDG 15 targets, but an evaluation of a sample of three exemplar studies using the UAF still gives a mean Uncertainty Score of 4.7, so this approach will not be straightforward.

The Use of Big Data via 5G to Alleviate Symptoms of Acute Stress Disorder Caused by Quarantine Measures

This article investigates the role of Big Data in situations of psychological stress such as during the recent pandemic caused by the COVID-19 health crisis. Quarantine measures, which are necessary to mitigate pandemic risk, are causing severe stress symptoms to the human body including mental health. We highlight the most common impact factors and the uncertainty connected with COVID-19, quarantine measures, and the role of Big Data, namely, how Big Data can help alleviate or mitigate these effects by comparing the status quo of current technology capabilities with the potential effects of an increase of digitalization on mental health. We find that, while Big Data helps in the pre-assessment of potentially endangered persons, it also proves to be an efficient tool in alleviating the negative psychological effects of quarantine. We find evidence of the positive effects of Big Data on human health conditions by assessing the effect of internet use on mental health in 173 countries. We found positive effects in 110 countries with 90 significant results. However, increased use of digital media and exclusive exposure to digital connectivity causes negative long-term effects such as a decline in social empathy, which creates a form of psychological isolation, causing symptoms of acute stress disorder.

Vinculum of Sustainable Development Goal Practices and Firms’ Financial Performance: A Moderation Role of Green Innovation

The 2030 Agenda for Sustainable Development (SDGs) has been established to alter our world by addressing the challenges faced by humanity in order to promote wellbeing, economic prosperity, and the protection of the environment. The SDGs provide a holistic and multi-dimensional approach to development compared to conventional development plans that focus on a limited range of dimensions. As a result, linkages between the SDGs may result in differing outcomes. This research is the first to investigate the direct relationship of environmental and social SDGs with firms’ financial performance and the moderating role of green innovation. Data from 67 companies from five continents (Europe, Australia and New Zealand, Asia, North America, and Africa) and their top five blue-chip firms were collected through content analysis. Generalized least squares (GLS) were used to test for direct relationships. The results showed a positive correlation between environmental SDGs and the negative significance of social SDGs on firms’ financial performance. However, mixed findings regarding the moderation variable green innovation over SDGs and firms’ financial performance were found. The new findings extend the SDG literature and provide empirical evidence to practitioners and policymakers.

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

Big-Data Management: A Driver for Digital Transformation?

The rapid evolution of technology has led to a global increase in data. Due to the large volume of data, a new characterization occurred in order to better describe the new situation, namel. big data. Living in the Era of Information, businesses are flooded with information through data processing. The digital age has pushed businesses towards finding a strategy to transform themselves in order to overtake market changes, successfully compete, and gain a competitive advantage. The aim of current paper is to extensively analyze the existing online literature to find the main (most valuable) components of big-data management according to researchers and the business community. Moreover, analysis was conducted to help readers in understanding how these components can be used from existing businesses during the process of digital transformation.