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Medical diagnosis and treatment processes exhibit a high degree of variability, as during the process execution, healthcare professionals can decide on additional steps, change the execution order or skip a task. Process models can help to document and to discuss such processes. However, depicting variability in graphical process models using standardized languages, such as Business Process Model and Notation (BPMN), can lead to large and complicated diagrams that medical staff who do not have formal training in modeling languages have difficulty understanding. This study proposes a pattern-based process visualization that medical doctors can understand without extensive training. The process descriptions using this pattern-based visualization can later be transformed into formal business process models in languages such as BPMN.

The authors derived patterns for expressing variability in healthcare processes from the literature and medical guidelines. Then, the authors evaluated and revised these patterns based on interviews with physicians in a Danish hospital.

A set of business process variability patterns was proposed to express situations with variability in hospital treatment and diagnosis processes. The interviewed medical doctors could translate the patterns into their daily work practice, and the patterns were used to model a hospital process.

When communicating with medical personnel, the patterns can be used as building blocks for documenting and discussing variable processes.

The patterns can reduce complexity in process visualization. This study provides the first validation of these patterns in a hospital.

At the beginning of the 21st century, multiple and diverse social entities, including the public (consumers), private and nonprofit healthcare institutions, government (public health) and other industry sectors, began to recognize the limitations of the current fragmented healthcare system paradigm. Primary stakeholders, including employers, insurance companies, and healthcare professional organizations, also voiced dissatisfaction with unacceptable health outcomes and rising costs. Grand challenges and wicked problems threatened the viability of the health sector. American health systems responded with innovations and advances in healthcare delivery frameworks that encouraged shifts from intra- and inter-sector arrangements to multi-sector, lasting relationships that emphasized patient centrality along with long-term commitments to sustainability and accountability. This pathway, leading to a population health approach, also generated the need for transformative business models. The coproduction of health framework, with its emphasis on cross-sector alignments, nontraditional partner relationships, sustainable missions, and accountability capable of yielding return on investments, has emerged as a unique strategy for facing disruptive threats and challenges from nonhealth sector corporations. This chapter presents a coproduction of health framework, goals and criteria, examples of boundary spanning network alliance models, and operational (integrator, convener, aggregator) strategies. A comparison of important organizational science theories, including institutional theory, network/network analysis theory, and resource dependency theory, provides suggestions for future research directions necessary to validate the utility of the coproduction of health framework as a precursor for paradigm change.

This article aims to provide a new paradigm for thinking about disability, which can be applied to other social groups, historically invisible and whose rights have been violated. The Model of Communication and Legitimate Acknowledgement of Disability (MCLAD) tries to break with the logic of continuing to add terms and euphemisms around the issue. The author proposes a new line to think about relationships in democratic societies. Taking the step from inclusion to acknowledgment does not imply another way of naming the disability, but rather addressing the problem from concrete practices of recognition. In order to arrive at the proposal of the MCLAD, the author will make a journey that addresses how disability has been understood throughout history, according to the study of different authors.

Disability has been perceived over time in many different ways, which led some authors to build models in order to explain certain social approaches to the subject. This article traces a journey from the first model to the present. In turn, it proposes a new one: the MCLAD, which is characterized by a paradigm shift: moving from inclusion to acknowledgment. To substantiate this, three categories are presented: acknowledgment, distance and vulnerability. The different theories and concepts that support the model will also be presented. The purpose of the MCLAD is to deepen the idea of empowering people with disabilities as part of today’s diverse societies and closing historically constructed gaps which are still in force.

The MCLAD proposes three categories: acknowledgment, distance and vulnerability. In turn, in each of them, there is a link between three axes: person with disability/society/state, analyzing the dynamics of these relationships presented, will provide us with the necessary elements to understand the proposed turnaround.

Although the different models will be presented according to the chronological order of definition over time, all of them still coexist today, in many cases in hybrid and naturalized ways in social practices. Recognizing what practices and conceptions are behind each model, allows us to recognize and resignify the ways of communicating toward people with disabilities (PWD) and on the issue of disability. It also allows other specific recognition practices, such as the legitimization of public policies from the laws that protect them.

To replace the paradigm of inclusion for that of acknowledgment and to recognize how the three categories (acknowledgment, distance and vulnerability) are linked with the three issues (PWD – society and state) allowing specific relationship and practises of legitimate or not acknowledgement. When the author affirms that the MCLAD implies a paradigm shift, the author means that it provides some elements from legitimate acknowledgment to complement aspects which inclusion does not address, and that the other models did not take into account. These are: the self-acknowledgment of people with disabilities and the sense of responsibility linked to empowerment; vulnerability as a category of reconciliation, which is typical of every human being; the contribution of the Phenomenology of the Among to think about how relationships and practices actually occur in society and, finally, the role of the state, which must watch over all its citizens, avoiding the distance between discourse (laws) and practices and, above all, avoiding exclusion from the system due to lack of monitoring of actions.

It should be noted that the MCLAD starts from the idea of language as a constructor of realities and conceives communication as an enabler of the acknowledgment of the other, who is also subject to rights. At the same time, it vindicates the voice of people with disabilities as protagonists (“Nothing about us without us”) and fosters the need for PWD themselves to be active in their struggles, promulgating legitimate acknowledgment. At the same time, it points out that the empowerment of PWD implies not only that they are aware of their rights but also that they themselves know and fulfill their duties within the democratic societies of which they are a part of and which, at least discursively, are regulated by laws. In other words, being empowered is also being responsible for living in society.

The main contribution that the MCLAD has to offer is to replace the paradigm of inclusion for that of acknowledgment. And, throughout the path followed in this article, an attempt has been made to establish that the turnaround is not to capriciously install a new concept (acknowledgment), but to demonstrate that the new paradigm involves three categories that sustain and support a model that seeks to be the basis for effective public policies, for a society that values diversity and for people who feel worthy and contribute to dignify others.

This chapter qualitatively explored the impact of including parent liaisons (i.e., parents with lived experience caring for a child with complex needs, who support other caregivers in navigating child and family needs) in a case conferencing model for children with complex medical/social needs. Case conferences are used to address fragmented care, shared decision-making, and set patient-centered goals. Seventeen semi-structured interviews were conducted with clinicians and parent liaisons to assess the involvement of parent liaisons in case conferencing. Two main themes included benefits of parent liaison involvement (10 subthemes) and challenges to parent liaison involvement (5 subthemes). Clinicians reported that liaison participation and support of patients reduced stress for clinicians as well as family members. Challenges to liaison involvement included clinical team/parent liaison communication delays, which were further exacerbated by the COVID-19 pandemic. Parent liaison involvement in case conferences is perceived to be beneficial to children with complex needs, their families, and the clinical team. Integration of liaisons ensures the familial perspective is included in clinical goal setting.

This paper aims to investigate how value drivers of internet medical business model affect value creation through a configurational approach. The internet medical business model (IMBM) is such a business model that integrates online and offline medical services with the driving force of internet technologies covering prediagnosis, in-diagnosis and postdiagnosis. The outbreak of COVID-19 and the support of national policies have boosted the development of internet health care. However, there are still many challenges in practice, such as the unclear innovation path, as well as difficulties in landing and profiting. Academic research has not yet provided sufficient theoretical insights. Therefore, to better explain and guide practice, it is urgent to clarify the innovation path and mechanism of value creation for IMBM.

Based on the sample of 58 internet medical firms in China, this paper adopts fuzzy-set qualitative comparative analysis (fsQCA) to explore the configurational effects of IMBM’s value drivers on value creation.

Building on the business model canvas and the characteristics of internet health care, five value drivers of IMBM are identified, namely, functional value proposition, emotional value proposition, user involvement, resource capabilities and connection properties. And the five value drivers form three configurations, which are, respectively, labeled as resource-driven configuration, user-operated configuration and product-combined configuration. From the perspective of the integration of traditional and emerging theories, such as resource-based view, internet economics and value cocreation, each configuration leads to value creation and improves value results with different mechanisms behind it.

First, combined with the business model canvas and the characteristics of internet health care, this paper identifies five value drivers of IMBM, thus improving the relevant research on internet health care. Second, based on the configurational effects, this paper discusses the mechanism behind the configurational effects of IMBM’s value drivers on value creation, thus expanding relevant research on the value creation of business models. Third, applying fsQCA and combining the advantages of qualitative research and quantitative research, this paper adds to the configurations of IMBM’s value drivers that achieve high-value results.

Sharing project information is critical for the success of medical crowdfunding campaigns. However, few users share medical crowdfunding projects on their social networks, and the sharing behavior of medical crowdfunding projects on social networking sites has not been well studied. Therefore, this study explored the factors and potential mechanisms influencing users’ sharing behaviors on networking sites.

A research model was developed based on the attribution-affect model of helping and social capital theory. Data were collected using a longitudinal survey. Partial least squares structural equation modeling was used to analyze the collected data. We conducted post hoc analyses to validate the results of the quantitative analysis.

The analysis results verified the effects of perceived external attribution, perceived uncontrollable attributions, and perceived unstable attributions on sympathy and identified the effect of sympathy and social characteristics of medical crowdfunding users on sharing behavior.

This research provides a comprehensive theoretical understanding of users’ sharing behavior characteristics and provides implications for enhancing the efficiency of medical crowdfunding activities.

Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education.

This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF).

According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI.

By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education.

The finding of this study should be considered when successful implementation of AI in education is in progress.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

This study aims to explore the consumer perceptions of cognition and intention to visit pilot zone of international medical tourism as emerging, developed medical tourism destinations.

Using a survey-based quantitative method, based on a survey of 439 tourists who have cross-border travel experience, the partial least squares approach was performed to test the hypotheses.

The results show that internal factors had a stronger influence on destination image compared to external factors. Among different factors, preferential policies had the greatest impact on intention to visit. Perceived quality had a stronger effect on intention to visit than preference. Geographical distance had a varied effect, with those furthest away in Northeast China showing greater intention to visit compared to closer regions.

This study explores the impact of multidimensional destination perception on medical tourists’ behavioural intention in emerging destinations by integrating the push-pull theory and theory of planned behaviour and tests how geographical distance affects intention to visit emerging destinations. Using China international medical tourism pilot area as a typical case of medical tourism emerging destinations for empirical analysis. This research offers guidance for branding and marketing strategies, contributes to a deeper understanding of medical tourists’ destination choices, enriches the theoretical explanation of emerging destination choice in medical tourism and provides valuable insights for destination recovery.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.