Comparison of Voluntary versus Mandatory Vaccine Discussions in Online Health Communities: A Text Analytics Approach

Vaccines are vital health interventions. However, they are controversial and some people support them while others reject them. Social media discussion and big data are a rich source to understand people’s insights about different vaccines and the related topics that concern most of them. This study aims to explore the online discussions about mandatory and voluntary vaccines using text analysis techniques. Reddit social platform is popular in online health discussion and thus data from Reddit is analyzed. The results show that different aspects are discussed for different types of vaccines. The discussion of mandatory vaccines is more interactive and is focused on the risks associated with them. Voluntary vaccines’ discussion is focused on their effectiveness and whether to get them or not. The study have important implications for health agencies and researchers as well as for healthcare providers and caregivers

Knowledge in transition in healthcare

Organizations are challenged by the need to transform Dynamic Knowledge, embedded in each worker, into Static Knowledge, rooted in factual documental information. However, innovation and knowledge creation seem to be facilitated by the personal knowledge and life experiences of people, which appear to be dynamic. The tensions between Dynamic and Static Knowledge in facilitating the transfer and sharing of knowledge arise as compelling research as well as practical topic for organizations. Our paper aims to investigate such tensions by employing a case study. We decided to deepen such dynamics in the healthcare field, given its importance for business and society. In more detail, we analyzed one Emergency Room (ER) department through a series of interviews. Our findings highlight the importance of the right balance between Static and Dynamic Knowledge. On the one hand, the healthcare organization recognized the need to incorporate knowledge into practical and tangible instruments. On the other hand, the flows of Dynamic Knowledge must be fostered through a culture of knowledge translation and sharing, and the development of soft skills

Use of Artificial Intelligence in Healthcare Delivery

In recent years, there has been an amplified focus on the use of artificial intelligence (AI) in various domains to resolve complex issues. Likewise, the adoption of artificial intelligence (AI) in healthcare is growing while radically changing the face of healthcare delivery. AI is being employed in a myriad of settings including hospitals, clinical laboratories, and research facilities. AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognised opportunities for clinical practitioners and health service organisations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. Yet, there is an incomplete understanding of AI and even confusion as to what it is? Also, it is not completely clear what the implications are in using AI generally and in particular for clinicians? This chapter aims to cover these topics and also introduce the reader to the concept of AI, the theories behind AI programming and the various applications of AI in the medical domain

An evidence-based management framework for business analytics

It is said that knowledge is power, yet often, decision makers ignore information that ought to be considered. The phenomenon known as Semmelweis reflex occurs when new knowledge is rejected because it contradicts established norms. The goal of evidence-based management (EBMgt) is to help overcome Semmelweis reflex by integrating evaluated external evidence with stakeholder preference, practitioner experiences, and context. This evaluated external evidence is the product of scientific research. In this paper, we demonstrate an EBMgt business analytics model that uses computer simulation to provide scientific evidence to help decision makers evaluate equipment replacement problems, specifically the parallel machine replacement problem. The business analytics application is demonstrated in the form of a fleet management problem for a state transportation agency. The resulting analysis uses real-world data allowing decision makers to unfreeze their current system, move to a new state, and re-freeze a new system

Opportunities and Challenges in Healthcare Information Systems Research: Caring for Patients with Chronic Conditions

To prepare for the 2030 “baby-boomer challenge”, some governments have begun to implement healthcare reforms over the past two decades. These reforms have led healthcare information systems (HIS) to evolve into a major research area in our discipline. This research area has an increasing individual, organizational, and economic impact. Due to the 2030 “baby-boomer challenge”, the number of elderly individuals continues to increase, and they may have chronic illnesses, such as eye problems and Alzheimer’s disease. Given the practical need for HIS that support chronic care, we decided to conduct a literature synthesis and identify opportunities for HIS research. Specifically, we present the chronic care model and analyze how IS researchers have discussed HIS to address the needs of patients with chronic illness. Further, we identify research gaps and discuss the research topics on HIS that future work can extend and customize to support these patients. Our results stimulate and guide future research in the HIS area. This paper has the potential to strengthen the body of knowledge on HIS

Deriving Value from Big Data Analytics in Healthcare: A Value-focused Thinking Approach

With the potential to generate more insights from data than ever before, big data analytics has become highly valuable to many industries, especially healthcare. Big data analytics can make important contributions to many areas, such as enhancements in the quality of patient care and improvements in operational efficiencies. Big data analytics provides opportunities to address concerns such as disease diagnoses and prevention. However, it has posed challenges such as data security and privacy issues. Also, healthcare institutions have concerns about deriving the greatest benefit from their big data analytics endeavors. Therefore, identifying actionable objectives that can help healthcare organizations derive the maximum value from big data analytics is needed. Using the value-focused thinking (VFT) approach, we interviewed individuals associated with data analytics in healthcare to identify actionable objectives that one needs to consider to derive value from big data analytics, which practitioners can use for their own endeavors and provide opportunities for future research

A Multi-Agent System to Support Evidence Based Medicine and Clinical Decision Making via Data Sharing and Data Privacy

Evidence based medicine is the modern standard for clinical decision making where the use of medical evidence combined with clinical expertise and research is leveraged for clinical decisions. Supporting evidence based medicine (EBM) and clinical decision making (CDM) requires access to accurate predictive models and a multi-dimensional patient view that is aggregated from multiple sources in a multitude of configurations. Data sharing in healthcare remains a challenge due to widespread privacy concerns. Despite abundant research in privacy preserving data mining, healthcare organizations are unwilling to release their medical data on account of the Health Insurance Portability and Accountability Act (HIPAA) requires protecting the confidentiality and security of healthcare data. Further, sensitive data spanning multiple organizations result in not only the data syntax and semantic heterogeneity but also diverse privacy requirements, posing additional challenges to data sharing and integration. In overcoming these challenges, a multi-agent approach is a viable alternative. Despite its potential for addressing the aforementioned issues, little research has been conducted in integrating a multi-agent architecture with privacy preserving data mining in big healthcare data spanning multiple organizations. This research proposes a multi-agent architecture coupled with privacy preserving techniques to facilitate data sharing while preserving privacy. Results indicate that our design artifact is capable of overcoming the aforementioned challenges, thereby facilitating data sharing for knowledge discovery in healthcare and supporting evidence based medicine and clinical decision making via improving predictive models

Themes and Participants’ Role in Online Health Discussion: Evidence From Reddit

Health-related topics are discussed widely on different social networking sites. These discussions and their related aspects can reveal significant insights and patterns that are worth studying and understanding. In this dissertation, we explore the patterns of mandatory and voluntary vaccine online discussions including the topics discussed, the words correlated with each of them, and the sentiment expressed. Moreover, we explore the role opinion leaders play in the health discussion and their impact on participation in a particular discussion. Opinion leaders are determined, and their impact on discussion participation is differentiated based on their different characteristics such as their connections and locations in the social network, their content, and their sentiment. We apply social network analysis, topic modeling, sentiment analysis, machine learning, econometric analysis, and other techniques to analyze the collected data from Reddit. The results of our analyses show that sentiment is an important factor in health discussion, and it varies between different types of discussions. In addition, we identified the main topics discussed for each vaccine. Furthermore, the results of our study found that global opinion leaders have more influence compared to local opinion leaders in elevating the health discussion. Our study has important theoretical and practical implications

Cross-Border Collaboration in Disaster Management

Wenn sich eine Katastrophe ereignet, ist eine schnelle und koordinierte Reaktion der verschiedenen Krisenmanagementakteure unerlässlich, um die vorhandenen Ressourcen bestmöglich einzusetzen und somit ihre Auswirkungen zu begrenzen. Dieses Zusammenspiel wird erschwert, wenn die Katastrophe mehrere Länder betrifft. Neben den unterschiedlichen Regelungen und Systemen spielen dann auch kulturelle Einflüsse wie Sprachbarrieren oder mangelndes Vertrauen eine entscheidende Rolle. Obwohl die Resilienz von Grenzgebieten von fundamentaler Bedeutung ist, wird diese in der wissenschaftlichen Literatur immer noch unterschätzt. Im ersten Teil dieser Arbeit wird ein agentenbasiertes Modell zur Untersuchung der organisationsübergreifenden Zusammenarbeit bei Katastropheneinsätzen in einer Grenzregion vorgestellt. Indem Kommunikationsprotokolle aus der Literatur auf den Kontext der grenzüberschreitenden Kooperation erweitert werden, analysiert das Modell die globale Dynamik, die aus lokalen Entscheidungen resultiert. Ein szenariobasierter Ansatz zeigt, dass höheres Vertrauen zwar zu signifikant besseren Versorgungsraten führt, der Abbau von Sprachbarrieren aber noch effizienter ist. Insbesondere gilt dies, wenn die Akteure die Sprache des Nachbarlandes direkt sprechen, anstatt sich auf eine allgemeine Lingua franca zu verlassen. Die Untersuchung der Koordination zeigt, dass Informationsflüsse entlang der hierarchischen Organisationsstruktur am erfolgreichsten sind, während spontane Zusammenarbeit durch ein etabliertes informelles Netzwerk privater Kontakte den Informationsaustausch ergänzen und in dynamischen Umgebungen einen Vorteil darstellen kann. Darüber hinaus verdoppelt die Einbindung von Spontanfreiwilligen den Koordinationsaufwand. Die Koordination über beide Dimensionen, zum einen die Einbindung in den Katastrophenschutz und zum anderen über Grenzen hinweg, führt jedoch zu einer optimalen Versorgung der betroffenen Bevölkerung. In einem zweiten Teil stellt diese Arbeit ein innovatives empirisches Studiendesign vor, das auf transnationalem Sozialkapital und Weiners Motivationstheorie basiert, um prosoziale Beziehungen der Menschen über nationale Grenzen hinweg zu quantifizieren. Regionale Beziehungen innerhalb der Länder werden dabei als Vergleichsbasis genommen. Die mittels repräsentativer Telefoninterviews in Deutschland, Frankreich und der deutsch-französischen Grenzregion erhobenen Daten belegen die Hypothese, dass das Sozialkapital und die Hilfsbereitschaft über die deutsch-französische Grenze hinweg mindestens so hoch ist wie das regionale Sozialkapital und die Hilfsbereitschaft innerhalb der jeweiligen Länder. Folglich liefert die Arbeit wertvolle Erkenntnisse für Entscheidungsträger, um wesentliche Barrieren in der grenzüberschreitenden Kooperation abzubauen und damit die grenzüberschreitende Resilienz bei zukünftigen Katastrophen zu verbessern. Implikationen für die heutige Zeit in Bezug auf Globalisierung versus aufkommendem Nationalismus sowie Auswirkungen von (Natur-) Katastrophen werden diskutiert