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This study investigates the pathways for adopting IoTs and BDA technologies to improve healthcare management.

The study relied on 445 healthcare professionals' perspectives to explore different causal pathways to IoTs and BDA adoption and usage for daily healthcare management. The Fussy-set Qualitative Comparative Analysis was adopted to explore the underlying pathways for healthcare management.

The empirical analysis revealed six different configural paths influencing the acceptance and use of IoTs and BDA for healthcare improvement. Two key user topologies from the six configural paths, digital literacy and ease of use and social influence and behavioural intentions, mostly affect the paths for using digital health technologies by healthcare physicians.

Despite this study's novel contributions, limitations include the fsQCA methodology, perceptual data and the context of the study. The fsQCA methodology is still evolving with different interpretations, although it reveals new insights and as such further studies are required to explain the configural paths of social phenomena. Additionally, future research should consider other constructs beyond the UTAUT and digital literacy to illustrate configural paths to healthcare technology acceptance and usage. Again, the views of healthcare professionals are perceptual data. Hence future research on operational data will support significant contributions towards pathways to accept and use emerging technologies for healthcare improvement. Lastly, this study is from a developing country perspective where emerging digital healthcare technology is still emerging to support healthcare management. Hence, more investigation from other cross-country analyses of configural paths for digital technology deployment in healthcare will enhance the conversation with IoTs and BDA for healthcare management.

Holistically, the acceptance and use of healthcare technologies and platforms is not solely on their capabilities, but a combination of distinct factors driven by users' perspectives. This offers healthcare administrators and institutions to essentially reflect on the distinct combinations of conditions favourable to health professionals who can use IoTs and BDA for healthcare improvement.

This study is among the few scholarly works to empirically investigate the configural paths to support healthcare improvement with emerging technologies. Using fsQCA is a unique contribution to existing information system literature for configural paths for healthcare improvement with emerging digital technologies.

Purpose: This study investigates the role of telemedicine in sustaining healthcare systems in Europe, during the COVID-19 pandemic. It focusses on how telemedicine serves as a strategic response to modern healthcare challenges, emphasising its efficiency, accessibility, and patient-centred nature.

Need for the study: The need for this study arises from the escalating demands on healthcare systems, especially during the COVID-19 pandemic. It aims to understand the adoption of telemedicine practices across European Union (EU) countries and their impact on healthcare sustainability.

Methodology: This study employs hierarchical and K-Means clustering to analyse EU citizens’ attitudes towards teleconsultations during COVID-19. Principal component analysis (PCA) is used for data compression and insight extraction. Data is sourced from Eurofound’s 2020 and 2021 surveys, involving extensive participant responses across the EU.

Findings: The study’s findings reveal significant shifts towards digital healthcare solutions, such as an increase in online consultations and prescriptions. It identifies different patterns of telemedicine use across EU countries, influenced by socioeconomic and geographical factors. These findings offer insights into future healthcare policy and strategy development.

Practical implications: The findings provide valuable insights into the shifts in telemedicine adoption in the EU, highlighting the significance of economic and sociological factors in healthcare trends. This study stresses the importance of customising healthcare strategies to suit the unique needs and digital capabilities of different countries.

This systematic review aimed to identify and categorize applications using Fast Healthcare Interoperability Resources (FHIR) to support activities outside of direct healthcare provision.

A systematic electronic search was performed, and 53 studies were included after the selection process.

The results show that FHIR is being used to support (1) clinical research (i.e. clinical research based on interventional trials, data interoperability to support clinical research and advanced communication services to support clinical research), (2) public health and (3) medical education. Despite the FHIR potential to support activities outside of direct healthcare provision, some barriers were identified, namely difficulties translating the proposed applications to clinical environments or FHIR technical issues that require further developments.

This study provided a broad review of how FHIR is being applied in clinical activities outside of direct clinical care and identified three major domains, that is, clinical research, public health and medical education, being the first and most representative in terms of number of publications.

The purpose of our research on blockchain technology is to unveil its immense potential, understand its applications and implications and identify opportunities to revolutionize existing systems and processes. This research aims to inspire the creation of new innovative solutions for industries. By harnessing blockchain technology, organizations can pinpoint key areas that could significantly benefit from its use, such as streamlining operations, providing secure and transparent digital solutions and fortifying data security.

This study presents a robust multi-criteria decision-making framework for assessing blockchain drivers in selected Indian industries. We initiated with an extensive literature review to identify potential drivers. We then sought the opinions of experts in the field to validate and refine our list. This meticulous process led us to identify 26 drivers, which we categorized into five main categories. Finally, we employed the Best-Worst Method to determine the relative importance of each criterion, ensuring a comprehensive and reliable assessment.

The authors have ranked the blockchain drivers based on their degree of importance using the Best-Worst Method. This study reveals the priority of BC implementation, with the retail industry identified as the most in need, followed by the Banking and Healthcare industries. Various critical factors are identified where blockchain technology could help reduce costs, increase efficiency and enable new innovative business models.

While this study acknowledges potential bias in driver assessment relying on literature and expert opinions, its findings carry significant practical implications. We have identified key areas where blockchain technology could be transformative by focusing on select industries. Future research should encompass other industries and real-world case studies for practical insights that could delve into the adoption challenges and benefits of blockchain technology in many other industries, thereby amplifying the relevance of our findings.

Blockchain is a groundbreaking, innovative technology with immense potential to revolutionize industries. Past research has explored the benefits and challenges of blockchain implementation in specific industries or sectors. This creates a gap in research regarding systematically classifying and ranking the importance of blockchain across different Indian industries. Our research seeks to address this gap by using advanced multi-criteria decision-making techniques. We aim to provide a comprehensive understanding of the significance of blockchain technology in critical Indian industries, offering valuable insights that can inform strategic decision-making and drive innovation in the country’s business landscape.

Economic evaluations often utilise individual-patient data (IPD) to calculate probabilities of events based on observed proportions. However, this approach is limited when interest is in the likelihood of extreme biomarker values that vary by observable characteristics such as blood glucose in gestational diabetes mellitus (GDM). Here, instead of directly calculating probabilities using the IPD, we utilised flexible parametric models that estimate the full conditional distribution, capturing the non-normal characteristics of biomarkers and enabling the derivation of tail probabilities for specific populations. In the case study, we used data from the Born in Bradford study (N = 10,353) to model two non-normally distributed GDM biomarkers (2-hours post-load and fasting glucose). First, we applied fully parametric maximum likelihood to estimate alternative flexible models and information criteria for model selection. We then integrated the chosen distributions in a probabilistic decision model that estimates the cost-effective diagnostic thresholds and the expected costs and quality-adjusted life years (QALYs) of the alternative strategies (‘Testing and Treating’, ‘Treat all’, ‘Do Nothing’). The model adopts the ‘payer’ perspective and expresses results in net monetary benefits (NMB). The log-logistic and Singh-Maddala distributions offered the optimal fit for the 2-hours post-load and fasting glucose biomarkers, respectively. At £13,000 per QALY, maximum NMB with ‘Test and Treat’ (−£330) was achieved for a diagnostic threshold of fasting glucose >6.6 mmol/L, 2-hours post-load glucose >9 mmol/L, identifying 2.9% of women as GDM positive. The case study demonstrated that fully parametric approaches can be implemented in healthcare modelling when interest lies in extreme biomarker values.

The goal of this article is to provide an overview of healthcare entrepreneurship, both in terms of its current trends and future directions.

The article engages in a systematic review of extant research on healthcare entrepreneurship using the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) as the review protocol and bibliometrics or scientometrics analysis as the review method.

Healthcare entrepreneurship research has fared reasonably well in terms of publication productivity and impact, with diverse contributions coming from authors, institutions and countries, as well as a range of monetary and non-monetary support from funders and journals. The (eight) major themes of healthcare entrepreneurship research revolve around innovation and leadership, disruption and technology, entrepreneurship models, education and empowerment, systems and services, orientations and opportunities, choices and freedom and policy and impact.

The article establishes healthcare entrepreneurship as a promising field of academic research and professional practice that leverages the power of entrepreneurship to advance the state of healthcare.

The article offers a seminal state of the art of healthcare entrepreneurship research.

The main purpose of this study is to determine the accounting analytics technology (AAT) adoption among manufacturing small and medium-sized enterprises (SMEs) based on the extended technology acceptance model (TAM).

The quantitative research approach with online surveys was used to collect data from 219 accounting managers among manufacturing SMEs in Jordan. To test the suggested research model, partial least squares structural equation modeling was used.

The findings indicated that all direct paths were found to be significant in the hypothesized directions. Ultimately, the results also revealed that perceived usefulness has mediated the relationship between perceived ease of use and intention to use AAT, and hence all direct and indirect hypotheses were accepted.

This research has successfully extended the TAM model in the context of AAT adoption among Jordanian manufacturing SMEs by including new factors along with the original factors of the TAM model, particularly in the postpandemic era.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

Artificial intelligence (AI) applications are increasingly used for day-to-day operations in healthcare. Each has a relatively limited scope or task, and several find application in managerial and organizational processes. More and more, AI and machine learning (ML) devices have received US FDA approval in the last decade. This chapter covers the main AI applications in healthcare, with a focus on organizational AI solutions (administrative AI), the main AI developers, their investment and real-world data and case studies in healthcare and other sectors. AI can be applied in resource management and procurement, resource allocation, clinical case management, staff work shift scheduling and handling of emergencies. AI applications are becoming ubiquitous in hospital (e.g. emergency room and operating theatre) and outpatient settings (e.g. ambulatory care and dentistry clinics). Their implementation is expected to bring direct benefits for patient care and satisfaction. This chapter gives a broad definition of AI in healthcare settings, with a focus on administrative applications and their use in case study data.

This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

All 46 factors were assessed as “Very Important” through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are “AI ability to learn from patient data”, “insufficient data privacy measures for patients”, “availability of technical support and maintenance services”, “physicians’ acceptance of AI in healthcare”, “reliability and uptime of AI systems” and “ability to reduce medical errors”.

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders’ comprehension of the factors challenging the integration of AI technologies.