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This study aims to investigate the combined impact of pharmaceutical marketing strategies, specifically free drug samples and interactions with medical representatives, on physicians’ attitudes and prescribing behavior in Jammu and Kashmir, India.

To achieve this objective, data were collected from a sample comprising 425 physicians. A six-point forced-choice Likert scale was used for data collection, and cluster sampling techniques were used in the study design.

The study reveals significant insights into pharmaceutical marketing’s influence on physician behavior. Free drug samples exert a positive influence on both physician attitudes and prescribing behavior. Physicians’ attitudes, in turn, significantly affect their prescribing decisions. Mediation analysis demonstrates that free drug samples indirectly influence prescribing behavior through shifts in physician attitudes. Effective medical representatives amplify this influence, as stronger associations between free drug samples and physician attitudes are observed when representatives are more effective.

This research contributes by unraveling the intricate dynamics between marketing strategies, physician attitudes and prescribing behavior in Jammu and Kashmir, India. It underscores the importance of ethical pharmaceutical marketing practices, highlighting the substantial role of free drug samples and the crucial function of effective medical representatives in shaping physician behavior. Ultimately, this study sheds light on how responsible pharmaceutical marketing and adept medical representatives can improve health-care outcomes in the region.

This study aims to investigate the role of information normalization in online healthcare consultation, a typical complex human-to-human communication requiring both effectiveness and efficiency. The globalization and digitization trend calls for high-quality information, and normalization is considered an effective method for improving information quality. Meanwhile, some researchers argued that excessive normalization (standardized answers) may be perceived as impersonal, repetitive, and cold. Thus, it is not appreciated for human-to-human communication, for instance, when patients are anxious about their health condition (e.g. with high-risk disease) in online healthcare consultation. Therefore, the role of information normalization in human communication is worthy to be explored.

Data were collected from one of the largest online healthcare consultation platforms (Dxy.com). This study expanded the existing information quality model by introducing information normalization as a new dimension. Information normalization was assessed using medical templates, extracted through natural language processing methods such as Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA). Patient decision-making behaviors, namely, consultant selection and satisfaction, were chosen to evaluate communication performance.

The results confirmed the positive impact of information normalization on communication performance. Additionally, a negative moderating effect of disease risk on the relationship between information normalization and patient decision-making was identified. Furthermore, the study demonstrated that information normalization can be enhanced through experiential learning.

These findings highlighted the significance of information normalization in online healthcare communication and extended the existing information quality model. It also facilitated patient decision-making on online healthcare platforms by providing a comprehensive information quality measurement. In addition, the moderating effects indicated the contradiction between informational support and emotional support, enriching the social support theory.

The success of an Electronic Health Record (EHR) system is determined by the numerous facilitators and obstacles that influence physicians' intentions toward using these technologies. This study examines physicians' intentions to use EHR by applying the extended technology readiness and acceptance model (TRAM) factors, the result demonstrability, colleagues' opinions, perception of external control, and organizational support.

Convenience sampling was used to collect data from physicians in Egypt (n = 520). To evaluate the model's hypotheses, this study used the partial least squares structural equation modeling (PLS-SEM) method with WarpPLS.7.

The results revealed that positive TR factors (innovativeness and optimism) positively affect perceived usefulness and ease of use, while negative TR factors (discomfort and insecurity) negatively impact perceived usefulness and ease of use. Furthermore, the result demonstrability and colleagues' opinions positively influence perceived usefulness, while the perception of external control and organizational support positively influence perceived ease of use. In addition, significant relationships between perceived ease of use and usefulness and adoption intention were identified.

This is the first study to apply the TRAM to understand physicians' adoption intentions to use EHR systems. Moreover, this study determined the different roles of positive and negative TR affecting physicians' cognition regarding using EHR systems.

Efficiency standards, similar to industrial measures like overall equipment effectiveness (OEE), are being used in healthcare systems more and more. Performance indicator models applied to machines assume a constant completion time. However, for human resources, the completion time of a task may vary depending on the stress experienced. This study seeks to bridge this gap by integrating the human behavior of the physician into the performance evaluation.

The paper presents a new algorithm called PerfoBalance that is intended to distribute waiting-patient values among doctors. By maximizing each physician’s stress zones, this method helps to improve their performance as a whole. A thorough case study with medical professionals is carried out to confirm the effectiveness of the suggested methodology. The PerfoBalance algorithm is used in a variety of contexts to divide waiting-patient values among doctors and optimize stress zones.

Experimental results demonstrate a significant improvement in physician efficiency when implementing the PerfoBalance algorithm. The algorithm strategically selects stress zones that contribute to higher performance rates for physicians by optimizing waiting-patient values.

By addressing the undervaluation of human performance difficulties in current efficiency models used in the healthcare industry, this research constitutes a significant contribution to the field. With its launch, the PerfoBalance algorithm offers a fresh viewpoint on waiting-patient value allocation and stress zone management in healthcare settings, hence representing a powerful method for increasing physician productivity.

The brain drain challenge has become a cankerworm confronting not only the public health-care sector in Nigeria but almost all sectors of the national economy. This study aims to explore the push factors responsible for brain drain incidence among the migrated, JAPA, physicians to other global work environments. The study investigates the mediating role of capacity development among migrated, JAPA, physicians between the health-care infrastructural deficit and brain drain syndrome.

This study used a survey cross-sectional research design to examine the 214 migrated physicians in four notable perceived countries (UK, USA, Canada and Australia). The study used a probability sampling strategy to survey a self-administered online research instrument. The study adapted a battery of scales from several authors to measure the relevant constructs of this study. Hierarchical multiple regression was used to examine factors that provoke the incidence of brain drain burden among the JAPA Physicians. While Macro Hayes Process was used to investigate the mediating role of capacity development among migrated physicians.

The study revealed from the “JAPA” physicians that working conditions are turbulent and utterly poor which led to the incidence of brain drain. The study indicated that poor remuneration and benefits are the predominant reason for JAPA physicians to European countries and USA/UK. The findings of the study demonstrated that restricted opportunities and poor standard of living in the country were additional factors responsible for the brain drain of Nigerian physicians to other international countries. The outcome of the study also illustrated that inadequate infrastructure and facilities are the dominant variables that pushed physicians to foreign nations. It was revealed that there is a toxic mix of several issues that led to a brain drain albatross among the migrated physicians from Nigeria. The last part of the study indicated that physicians’ capacity development was a game changer that would discourage brain drain incidence and establish motivation for working in Nigeria's public health-care sector.

The study has given a direction for providing succinct solutions to the cankerworm of brain drain that has depleted the Nigerian public health-care industry. It has proffered a possible trajectory that will reverse the JAPA syndrome among the professional health-care workforce. This will not only benefit the public health-care personnel but also be significant for all the human capital across all the sectors of the national economy of Nigeria.

Today, e-government (electronic government) applications have extended to the frontiers of health-care delivery. E-Nabız contains personal health records of health services received, whether public or private. The use of the application by patients and physicians has provided efficiency and cost advantages. The success of e-Nabız depends on the level of technology acceptance of health-care service providers and recipients. While there is a large research literature on the technology acceptance of service recipients in health-care services, there is a limited number of studies on physicians providing services. This study aims to determine the level of influence of trust and privacy variables in addition to performance expectancy, effort expectancy, social influence and facilitating factors in the unified theory of acceptance and use of technology (UTAUT) model on the intention and behavior of using e-Nabız application.

The population of the study consisted of general practitioners and specialist physicians actively working in any health facility in Turkey. Data were collected cross-sectionally from 236 physicians on a voluntary basis through a questionnaire. The response rate of data collection was calculated as 47.20%. Data were collected cross-sectionally from 236 physicians through a questionnaire. Descriptive statistics, correlation analysis and structural equation modeling were used to analyze the data.

The study found that performance expectancy, effort expectancy, trust and perceived privacy had a significant effect on physicians’ behavioral intentions to adopt the e-Nabız system. In addition, facilitating conditions and behavioral intention were determinants of usage behavior (p < 0.05). However, no significant relationship was found between social influence and behavioral intention (p > 0.05).

This study confirms that the UTAUT model provides an appropriate framework for predicting factors influencing physicians’ behaviors and intention to use e-Nabız. In addition, the empirical findings show that trust and perceived privacy, which are additionally considered in the model, are also influential.

This chapter reviews the evidence on the role of physicians in shaping inequalities in access to and utilisation of healthcare. The authors examine three types of physician decisions that can influence inequalities in access and utilisation: location decisions, decisions to work in the public and/or private sector, and decisions or behaviours in the doctor–patient encounter. For each, the authors summarise the issues and empirical evidence on possible policies to help reduce inequalities in access. Future research to reduce inequalities should focus on changes to health systems that influence physician decisions, such as health insurance expansions, the public–private mix and financial incentives, as well as physician training and policies for a more diverse physician workforce.

The advent of online live streaming platforms (OLSPs) and online health communities (OHCs) has expedited the integration of traditional medical services with Internet new media technology. Since the practice of physicians conducting live streaming is a relatively new phenomenon, the potential cross-platform effects of such physicians’ live streaming have not received adequate attention.

This study collected data from 616 physicians specializing in cardiology, obstetrics and gynecology and neurology between April and November 2022 on Live.Baidu.com and WeDoctor.com. It constructed a panel data set comprising a total of 4,928 observations over an 8-month period and validated the model using empirical analysis with the fixed-effects method.

We find evidence of cross-platform influence in online healthcare. Physicians’ live streaming behavior (whether live or not and the heat of their streams) on OLSPs positively impacts both their consultation and reputation on OHCs. Additionally, physicians’ ability positively moderates the relationships between live streaming heat and their performance (in terms of consultation volume and reputation) on OHCs. However, ability does not moderate the relationship between physicians’ live streaming status (live or not) and their performance (in terms of consultation and reputation) on OHCs. Furthermore, the attractive appearance of the physicians also significantly moderates the impact in a positive way.

This is one of the pioneering studies on physicians’ live streaming. The study offers vital guidance for physicians and patients utilizing dual platforms and holds significant reference value for platform operators (such as OLSPs and OHCs) aiming to optimize platform operations and for the government in policy formulation and industry regulation.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

In the digital age, the spread of online behavior and real-world information leads to social contagion. This study aims to investigate the contagion phenomenon of online physician choice and then discuss its potential influence on the sub-specialization process in the healthcare service industry. In specific, this study aims to propose the basic mechanism of infection and immunity as follows – exposure to antigen may lead to an immune response, and the success of the immune response may depend on the provision of appropriate immune signaling.

Data collected from haodf.com including 4 disease types and 247 physicians from 2008 to 2015 were used to test the proposed hypotheses. Panel vector autoregression method was utilized to analyze the panel data.

The obtained result shows that social contagion of physician choice over disease type is salient on e-consultation platforms, indicating that physicians associated with/on haodf.com are concentrating on an even narrower type of disease. Disclosing more simple signals (physician history orders) results in more disease concentration for that physician in the future. In contrast, disclosing more detailed signals (physician-contributed knowledge or physician reviews) leads to less disease concentration.

This finding implies that physician-contributed knowledge and physician reviews may act as immune signal which will tend to trigger a success immune response. This study not only suggests managers should be careful about the double-edged sword effect of online physician choice contagion but also provides the useful approaches to promote or restrain such a contagion in a flexible way.