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The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

The primary aim of this research was to investigate the mediating effect of the decision-making structure on the relationship between perceived environmental uncertainty and hospital performance.

Online and manual survey questionnaires were used to collect data in this study. The target population of this study consists of all middle managers within 11 COVID-19 referral hospitals in Surabaya. A total of 189 responses were collected, however, 27 incomplete responses were excluded from the final dataset. Data was analyzed using SEM-PLS.

The study's findings indicate that decision-making structure plays a role in mediating the link between perceived environmental uncertainty and hospital performance assessed via the Balanced Scorecard, highlighting the significance of flexible decision-making processes during uncertain periods. Moreover, based on our supplementary test, respondents' demographic characteristics influence their perceptions of hospital performance.

Hospital administrators can consider the significance of decision-making structures in responding to environmental uncertainties like the COVID-19 pandemic. By fostering adaptable decision-making processes and empowering middle managers, hospitals may enhance their performance and resilience in challenging situations. Additionally, based on supplementary tests, it is found that differences in the perception of the three Balanced Scorecard perspectives imply that hospitals categorized as types A, B, C, and D should prioritize specific areas to improve their overall performance.

This research adds substantial originality and value to the existing body of knowledge by exploring the interplay between decision-making structures, environmental uncertainty, and hospital performance. It contributes to the literature by specifically focusing on the Covid-19 pandemic, a unique and unprecedented global crisis.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

Health tourism is a relatively new branch of international tourism that has developed more rapidly than other tourism sectors in recent years. This study aimed to investigate the effect of government supports on health tourism development by considering the mediating role of internal and external infrastructures.

The study population consisted of all experts of two hospitals in Iran, which are frequently visited by foreign tourists (N = 151). A questionnaire, developed by combining standard and researcher-made questionnaires, was used to collect the data. The confirmatory factor analysis (CFA) model was developed in SmartPLS 3 to test the hypotheses.

The main hypothesis test results indicated that government supports positively affect the development of health tourism. Internal and external infrastructures were also found to mediate the relationship between government supports and health tourism development. In addition, the sub-hypothesis test results showed that internal and external infrastructures are positively affected by government supports, which puts forth the development of health tourism. As the results explained, the most important aspects of internal infrastructures affected by government support were health service quality, cost of health services and applying advanced medical technologies, respectively. Also, different aspects of external infrastructures affected by government supports are as follows: economic, infrastructures and cultural factors.

This study is the first of its kind to examine the impact of both medical and non-medical factors on health tourism and signifies the crucial role of governments in the development of health tourism.

The library is one of the critical foundations of informing and educating the public, and it should fulfil its function in the community, which is disseminating knowledge effectively. The library must have a dynamic space and an appropriate atmosphere to do this. Also, librarians should assist clients outside of stressful work environments. This study aimed to examine the association between organizational climate and job burnout in the hospital and college libraries of Shahid Beheshti University of Medical Sciences.

This study is a cross-sectional survey. The population includes all librarians working in the college and hospital libraries of Shahid Beheshti University of Medical Sciences. Two questionnaires, including organizational climate “ClimateQUAL” and job burnout “Maslach and Jackson”, were applied to collect data. It was hypothesized that there is a correlation between the organizational climate and the burnout of librarians.

The findings revealed that the organizational climate and job burnout are not in a favourable situation, with average scores of 4.70 and 2.50, respectively. Furthermore, there is a significant statistical relationship between organizational climate and job burnout (p-value<0.001).

Knowledge of the significant relationship between the organizational climate and job burnout in medical libraries may encourage policymakers to make more efforts to provide their staff with a better climate and less burnout. Regarding that it is crucial to enhance the existing condition in medical libraries, the findings of the present study suggest that policymakers should be conscious of improving the current condition, which could lead to promoting patrons’ satisfaction and productivity in the mentioned libraries.

Drawing upon the stimulus-organism-response model, this study aims to explore the impact of soft aspects of service quality on revisit intention through the mechanism of perceived empathy.

For the examination of the hypothesized relationships, the study adopts structural equation modelling to analyse the data of 562 respondents (i.e. 281 family members and 281 inpatients).

The empirical results suggest that service quality increased family member empathy perception, which, in turn, improved inpatients’ revisit intentions.

Past studies have focused on the roles of overall service quality. The authors have extended the literature by examining the specific but important aspect of service quality and its effects on emotional response. Importantly, the study explains that the affective reactions of a patient’s family, fastened with perceived empathy, have a central role in influencing the patients’ subsequent reactions. Moreover, the prior studies collected the data either from hospital employees or patients. However, in the present study, the authors used a unique sample (family members as well as patients) to have a deeper understanding. Thus, the study enhances the literature on the stimuli-response (i.e. service quality – revisit intentions) relationship in the context of service marketing in general and health care in specific. Important academic and managerial contributions and recommendations for future research are discussed.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The purpose of this study is to assess the attitudes of individuals living in Belgium and in Emirdag in regard to patient rights and the effect of cultural differences in developing the attitude. This study measures the attitudes of two groups of people living in different geographies, “individuals living in Emirdag and Individuals emigrated from Emirdag to Belgium,” and yet having the same ethnic origin, common social and cultural capital.

Implementing quantitative research, the data for the current study was obtained from a total of 1,043 participants, who were administered the Likert type “Patient Rights Attitude Scale” along with demographic questionnaire.

The total score mean of the scale is 126.09 ± 15.21 in the Emirdag group, whereas in the Belgian group, it is 129.78 ± 13.356. While the attitude with the highest mean score about patient rights in both groups observed under “the right to seek medical attention” and “the right to consent in medical and drug researches” items and thusthese items are considered as a common denominator, the two groups differed in the scale items that received the lowest average.

It is important to determine the effects of attitudes toward patient rights and cultural differences on the development of attitudes, to determine the problems in the delivery of health services and health service procurement.

The structures that direct the choices and decisions of individuals in critical subjects such as organ transplantation and euthanasia, and thus having different practices in the doctrine, can be affected by individuals and even society’s rules, beliefs and values. It was concluded that immigrant participants’ dominating culture and related beliefs, rules and values play a role in the making of decision and choice.

This study aims to analyze the way Indonesian and the US laws regulate the reality and implications of legal issues regarding telemedicine, including the protection of citizens in using telemedicine.

This normative legal study used secondary data comprising primary and secondary legal materials based on the law as a norm. The normative legal method was used because the data were based on laws and regulations, reports, journals and research governing telemedicine in Indonesia and the USA.

The results showed similarities between Indonesia and the USA regarding health services as part of protecting human rights. The differences in implementing telemedicine are from a legal aspect. The legal comparison of telemedicine implementation between the two countries resulted in differences in regulation, informed consent, medical records, practice licenses and medical prescriptions.

This study discussed telemedicine’s legal aspects in Indonesia and the USA.

This study aimed to determine the implementation and guidelines used by the USA to be considered for adoption by Indonesia for legal certainty.

The results showed that legal threats could be a weakness of law in Indonesia. Therefore, the government should develop guidelines about telemedicine to accommodate citizens’ legal protection and certainty.

This study is original and could be used as a reference for policymakers in Indonesia and the USA in protecting citizens using telemedicine services. The findings provide a perspective based on Health Law in Indonesia.

The study aims to investigate the nurse job enjoyment and their participation in medical affairs to enchance the quality of patient care in Saudi hospitals. In addition, this study also investigates the management team's role in patient care quality through the mediating effects of nurse job enjoyment and participation in medical affairs.

The present study used a self-administered survey questionnaire to collect data from registered nurses in Saudi hospitals. In this study, 600 survey questionnaires were distributed online (Google Forms) and received 266 valid responses (44.33% response rate). In addition, SmartPLS-4.0 was applied to validate the research constructs and test the hypotheses via partial least squares structural equation modelling (PLS-SEM).

The study's findings indicate that the job enjoyment of the nurses and participation in medical affairs have positive and significant effects on the quality of patient care. In addition, the research findings also suggest that the management team of the hospitals has a significant indirect influence on the quality of patient care through the medicating effects of nurse job enjoyment and participation in medical affairs.

The findings of this study also offer various practical implications. This study showed the direct impact of the management team on job enjoyment, medical affairs and patient care quality. Therefore, hospital authorities and policymakers may emphasise clear communication, collaboration, respect and trust for the effective management team in providing higher-quality patient care. The present study suggests that hospital policymakers should strive to create a positive work environment, provide adequate resources, foster team spirit, offer incentives and allow flexible scheduling to ensure higher job enjoyment and increase nurse participation in medical affairs.

This study adds to the growing body of knowledge by investigating the effects of the management team, job enjoyment and nurses' participation in medical affairs on patient care quality. This study also enhances the theoretical depth by exploring the mediating impact of job enjoyment in predicting the relationships between the management team and the quality of care provided to patients. The present study provides guidelines for healthcare service providers or practitioners to focus on the nurses' job enjoyment and their engagement in medical activities to continuously improve the quality of patient care in the hospitals.