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This study aims to summarize studies that compared the performance of health-care institutions led by leaders with medical background versus those with no medical background.

A systematic search was conducted on three databases: PubMed, Ovid Medline and Google Scholar to identify relevant peer-reviewed studies using the keywords “performance,” “impact,” “physician,” “medical,” “doctor,” “leader,” “healthcare institutions” and “hospital.” Only quantitative studies that compared the performance of health-care institutions led by leaders with medical background versus non-medical background were included. Articles were screened and assessed for eligibility before the relevant data were extracted to summarize, appraise and make a narrative account of the findings.

A total of eight studies were included, four were based in the USA, two in the UK and one from Germany and one from the Arab World. Half of the studies (n = 4) reported overall better health-care institutional performance in terms of hospital quality ranking such as clinical effectiveness and patient safety under leaders with medical background, whereas one study showed poorer performance. The remaining studies reported mixed results among the different performance indicators, especially financial performance.

While medical background leaders may have an edge in clinical competence to manage health-care institutions, it will be beneficial to equip them with essential management skills to optimize leadership competence and enhance organizational performance.

The exclusive inclusion of quantitative empirical studies that compared health-care institutional performance medical and non-medical leaders provides a clearer link between the relationship between health-care institutional performance and the leaders’ background.

This paper examines the factors that influence behavioral intention in the context of medical tourism in Türkiye. With the growing popularity of Türkiye as a destination for medical tourism, it is essential to understand the key determinants influencing individuals’ intention to engage in medical tourism activities.

This study used the Extended Theory of Planned Behavior (E-PBT) variables, which include attitude, subjective norm and perceived behavioral control, to measure their influence on behavioral intention. In addition, a customer experience scale was used to assess the influence of dimensions such as education, entertainment, aesthetics and escapism on behavioral intention. The research instrument was validated by expert review, and data were collected using purposive sampling. A total of 420 surveys were deemed suitable for analysis.

The path analysis revealed that attitudes and perceived behavioral control positively impacted behavioral intention within the domain of medical tourism in Türkiye. Conversely, subjective norms did not have a significant positive effect on behavioral intention. Furthermore, it was observed that the dimensions of entertainment, escape and aesthetics positively affected behavioral intention. However, education and aesthetics did not significantly influence behavioral intentions.

The results of this paper have practical implications for marketers and policymakers in the medical tourism industry in Türkiye. Understanding the factors that drive individuals’ behavioral intentions can assist in developing effective marketing strategies to attract and serve potential medical tourists. Medical tourism providers can enhance their services by optimizing attitudes and perceived behavioral control while emphasizing entertainment and escapism for their clientele.

This research focuses on uncovering factors influencing individuals’ behavioral intentions in medical tourism in Türkiye. Using the E-PBT framework and exploring the dimensions of the customer experience scale, this study aimed to understand the driving forces behind individuals’ decisions to engage in medical tourism.

An efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.

The developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.

The RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).

The proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

The future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.

In addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.

This study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.

The present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.

Despite the unparalleled importance of value within healthcare, value-based models remain underutilised in the procurement of medical devices. Research is needed to understand what factors incentivise standard, low-priced device purchasing as opposed to value-adding devices with potentially higher overall health outcomes. Framed in agency theory, we examine the conditions under which different actors involved in purchasing decisions select premium-priced, value-adding medical devices over low-priced, standard medical devices.

We conducted 2 × 2 × 2 between-subjects scenario-based vignette experiments on three UK-based online samples of managers (n = 599), medical professionals (n = 279) and purchasing managers (n = 449) with subjects randomly assigned to three treatments: (1) cost-saving incentives, (2) risk-sharing contracts and (3) stronger (versus weaker) clinical evidence.

Our analysis demonstrates the harmful effects of intra-organisational cost-saving incentives on value-based purchasing (VBP) adoption; the positive impact of inter-organisational risk-sharing contracts, especially when medical professionals are involved in decision-making; and the challenge of leveraging clinical evidence to support value claims.

Our results demonstrate the need to align incentives in a context with multiple intra- and inter-organisational agency relationships at play, as well as the difficulty of reducing information asymmetry when information is not easily interpretable to all decision-makers. Overall, the intra-organisational agency factors strongly influenced the choices for the inter-organisational agency relationship.

We contribute to VBP in healthcare by examining the role of intra- and inter-organisational agency relationships and incentives concerning VBP (non-) adoption. We also examine how the impact of such mechanisms differs between medical and purchasing (management) professionals.

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.

Waiting time, as an important predictor of queue abandonment and patient satisfaction, is important for resource utilization and patient experience management. Medical institutions have given top priority to reforming the appointment system for many years; however, whether the increased information transparency brought about by the appointment scheduling mechanism could improve patient waiting time is not well understood. In this study, the authors examine the effects of information transparency in reducing patient waiting time from an uncertainty perspective.

Leveraging a quasi-natural experiment in a tertiary academic hospital, the authors analyze over one million observational patient visit records and design the propensity score matching plus the difference in difference (PSM-DID) model and hierarchical linear modeling (HLM) to address this issue.

The authors confirm that, on average, improved information transparency significantly reduces the waiting time for patients by approximately 6.43 min, a 4.90% reduction. The authors identify three types of uncertainties (resource, process and outcome uncertainty) in the patient visit process that affect patients' waiting time. Moreover, information transparency moderates the relationship between three sources of uncertainties and waiting time.

The authors’ work not only provides important theoretical explanations for the patient-level factors of in-clinic waiting time and the reasons for information technology (IT)-enabled appointment scheduling by time slot (ITASS) to shorten patient waiting time and improve patient experience but also provides potential solutions for further exploration of measures to reduce patient waiting time.

The pandemic slowed down most industries globally, but health care maintained its steady growth, and for Pharma, it was a boom. Considering the comparatively better economic condition of India than other developed countries post-COVID-19, and the Russia–Ukraine war, multinational manufacturers of medical equipment companies are focusing on securing the maximum share of wallets from India through partnerships with health care service providers. The study tried to analyze the impact of the strategic key account management (KAM) partnership between multinational medical technology (MedTech) companies and health care service providers, from a global perspective. For the study, primary data were collected through questionnaires survey, and secondary data through a review of literature. The chi-square was tested using IBM SPSS software, and based on the results, three null hypotheses were rejected and one was accepted. Secondary data reveal that the Indian health care sector is highly competitive from a global perspective which can be observed by Nos of Venture Capital (VC) investment in health care, massive growth in medical tourism, and huge investment by international pharmaceutical companies in India. This partnership will help to develop more clinical packages through a clinical trial in India in a cost-effective way which will drastically reduce the manufacturing cost of high-tech medical equipment giving them an edge in global competition and also improving return on investment (ROI) for the partners.

The childbirth process is a complex and vital event that requires careful analysis and improvement. This experience can shape a woman's perspective on motherhood and even affect her mental health. Healthcare providers must prioritize improving the birth experience for women. In this interdisciplinary research, a combination of business process modeling (BPM) and medicine have been used with the aim of realizing an improved delivery experience and increased maternal satisfaction.

The data collection of this study was done by observing 518 childbirth processes and interviewing the chief of labor, chief residents, and midwives in the obstetrics and gynecology department of a hospital in Tehran from October 2022 to February 2023.

The research has been done in four main stages. The first phase is to model the primary process and sub-processes of normal vaginal delivery (NVD). The second phase is validation using expert confirmation and process mining (PM). The third phase is the analysis of the causes of maternal dissatisfaction in labor. The fourth phase of the heuristics redesigning and improving the process, in which for the first time three new categories have been presented including hospital-based, patient-based, and medical technique-based results show BPM intervention effect can be far-reaching in improving patient care and optimizing operational efficiency.

This study is one of only a few to adopt a process-oriented perspective to show how BPM can be used in clinical processes and has specifically examined an essential clinical process, i.e. childbirth.

1. Developing business process management (BMP) applications in a medical special process related to childbirth as interdisciplinary research.

2. A combination of qualitative and quantitative techniques contains engineering software and management approaches for a Case study, Implementation of BPM lifecycle in the women's hospital in Iran, Tehran, for a clinical process, which is called, normal vaginal delivery (NVD) process for fetal expulsion normally.

3. Modeling NVD clinical process and sub-process for the first time by BPMN2.0 notations in visual paradigm (VP) software and Validation of the made model with process mining (PM), by Disco process mining software. This was done through event log collection from HIS at the hospital.

4. Improving the childbirth process by redesigning heuristics and Introducing two new categories special for clinical process improvement for the first time.

5. Clinical process improvement heuristics obtained in this research are not consistent with the previous seven categories presented in previous studies such as Marlon Dumas' book. Therefore, we have introduced two new heuristics to redesign clinical processes compatible with medical centers, including hospital-based, patient-based, and medical technique-based.

6. Providing a framework for clinical process modeling and improvement containing steps and tools.

Developing business process management (BMP) applications in a medical special process related to childbirth as interdisciplinary research.

A combination of qualitative and quantitative techniques contains engineering software and management approaches for a Case study, Implementation of BPM lifecycle in the women's hospital in Iran, Tehran, for a clinical process, which is called, normal vaginal delivery (NVD) process for fetal expulsion normally.

Modeling NVD clinical process and sub-process for the first time by BPMN2.0 notations in visual paradigm (VP) software and Validation of the made model with process mining (PM), by Disco process mining software. This was done through event log collection from HIS at the hospital.

Improving the childbirth process by redesigning heuristics and Introducing two new categories special for clinical process improvement for the first time.

Clinical process improvement heuristics obtained in this research are not consistent with the previous seven categories presented in previous studies such as Marlon Dumas' book. Therefore, we have introduced two new heuristics to redesign clinical processes compatible with medical centers, including hospital-based, patient-based, and medical technique-based.

Providing a framework for clinical process modeling and improvement containing steps and tools.

Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers.

This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor.

This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models.

The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics.