The learning experiences and career development expectations of Chinese nursing master's degree students: A qualitative investigation.

AIM: To explore the learning experiences and career development expectations of nursing master's degree students. BACKGROUND: With increasing demands for improved quality of life, there is a growing need for nursing graduate programs in clinical care. However, the existing training programs for nursing master's students in China require improvement. It is essential to analyze students' learning experiences, perceptions of the current status of nursing and expectations of the nursing profession to enhance and develop university training programs. DESIGN: A descriptive qualitative study employing semi-structured interviews. METHOD: Semi-structured interviews were conducted with 14 nursing master's degree students. The data obtained were analyzed using Colaizzi's seven-step phenomenological analysis method. The study adhered to the consolidated criteria for reporting qualitative research (COREQ). RESULTS: Three main themes emerged from the analysis-Theme 1: Career expectations, encompassing motivations for pursuing the nursing master's program and career aspirations; Theme 2: Study experiences, including nursing professional ability, nursing management ability, self-awareness ability and moral literacy; Theme 3: Occupational dilemmas, encompassing the current challenges and coping strategies. CONCLUSION: The learning experiences of nursing master's students in universities are closely linked to their training programs. A disparity exists between students' career expectations and the actual employment landscape for nursing master's degree students.

Green hospitals face to climate change: Between sobriety and resilience.

Climate change poses a critical challenge to global health, influencing social and environmental determinants such as housing, air and water quality, and food security. This article explores the profound impact of climate change on health, projecting an additional 250,000 annual deaths from various climate-related diseases between 2030 and 2050. Healthcare systems significantly contribute to global carbon emissions. The concept of the "Green Hospital" is introduced as a paradigm shift in healthcare, focusing on optimizing resource efficiency and minimizing environmental impact. This concept encompasses renewable energy integration, natural lighting, sustainable materials, green roofs, and smart building management systems. Several challenges remain major, such as medical waste management, water conservation, chemical use, pollution, and plastic usage in healthcare settings. Moreover, obstacles to green hospital initiatives should be resolved, including system redundancy, regulatory compliance, operational demands, financial constraints, and cultural resistance. Conclusively, an urgent reformation of healthcare systems is needed to align with eco-friendly and sustainable practices, highlighting the necessity to reduce CO2 emissions and manage resources and waste more effectively to meet the evolving health needs of a growing and aging global population.

Digital economy and high-quality development of the healthcare industry.

The high-quality development of the healthcare industry is of great significance for improving people's health and promoting the construction of a harmonious society. This paper focuses on the relationship between the development of China's digital economy and the high-quality development of the healthcare industry. Based on the panel data of 30 provinces in China from 2011 to 2020, this paper empirically studies whether the development of the digital economy promotes the high-quality development of the healthcare industry. This study finds that the development of digital economy has significantly promoted the high-quality development of the medical and health industry. The results of this study are still valid after a series of robustness tests including variable substitution, sample adjustment, and endogenous problem mitigation. Heterogeneity analysis shows that the effect of this policy is more significant in the eastern region and southern areas. The results of spatial econometric analysis show that the development of digital economy has obvious spatial spillover effect. The research in this paper can provide reference for developing countries to enhance the development level of digital health industry and improve people's lives.

Medical occupation preference under the influence of the COVID-19 pandemic: The role of risk and altruistic preferences.

We examine the influence of the COVID-19 pandemic on medical occupation preference, focusing on Wuhan, China. We conducted a survey of 5686 respondents in China regarding the influence of the COVID-19 pandemic on medical occupation preference. We also conducted a complimentary survey in the UK with 1198 respondents, as well as a field experiment in Wuhan with 428 first and second-year medical students. We find a significant negative impact of the pandemic on the willingness to let a loved one choose a medical occupation. Individuals who were heavily influenced by the pandemic, that is, Wuhan residents, especially medical workers, express significantly lower medical occupation preference. Further analysis from Sobel-Goodman mediation tests reveals that around half of the total negative effect can be mediated by enhanced risk aversion and reduced altruism. The UK survey and the field experiment with medical students in Wuhan reinforce these findings. Our results suggest a shift in medical workers' risk- and altruistic-preferences has led to a reduced medical occupation preference. Non-medical workers and students who are more altruistic and risk-seeking are more likely to choose a medical occupation.

On the bright side of market concentration in a mixed-oligopoly healthcare industry.

We describe the healthcare industry as a mixed oligopoly, where a public and two private providers compete, and examine the effects of a merger between the two private healthcare providers on prices, quality, and welfare. When the price and (eventually) quality of the public provider are regulated, the cost synergies required for the merger to increase consumer welfare are less significant than in a setting with only profit-maximizing providers. When, instead, the public provider can adjust its policy to the rivals' behavior and maximizes a weighted sum of profits and consumer surplus (i.e., it has 'semi-altruistic' preferences), the merger is consumer surplus increasing if the public provider is sufficiently altruist, in some cases even absent efficiencies. These results suggest that ignoring the role and objectives of the public sector in the healthcare industry may lead agencies to reject mergers that, while would decrease consumer welfare in fully privatized industries, would increase it in mixed oligopolies.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine.

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Major Determinants of Innovation Performance in the Context of Healthcare Sector.

Through the innovation network (IN) and the use of artificial intelligence (AI), this study aims to look into the innovation performance (IP) of the healthcare industry. Digital innovation (DI) is also tested as a mediator. For the collection of data, cross-sectional methods and quantitative research designs were used. To test the study hypotheses, the SEM technique and multiple regression technique were used. Results reveal that AI and the innovation network support the attainment of innovation performance. The finding demonstrates that the relationship between INs and IP links and AI adoption and IP links is mediated through DI. The healthcare industry plays a vital role in facilitating public health and improving the living standards of the people. This sector's growth and development are largely dependent on its innovativeness. This study highlights the major determinants of IP in the healthcare industry in terms of IN and AI adoption. This study adds to the literature's knowledge via an innovative proposal in which the mediation role of DI among IN-IP and AI adoption-innovation links is investigated.

Patent Acquisitions in the Healthcare Industry: An Analysis of Learning Mechanisms.

The healthcare industry at large is used as a case study to suggest a methodological technique for evaluating patent citation networks to analyze cross-country creativity/knowledge flows. It intends to provide insight on the following research issues: (a) how to examine cross-national creative/learning flows; and (b) have nations with present patent owners profited from patent acquisitions? The research field at hand is currently under-explored, justifying the motivation for conducting this investigation, even though it has economic relevance in innovation patterns worldwide. The analysis of over 14,023 firms has shown that: (a) owners have acquired patents across borders, and (b) acquired patents (granted between 2013 and 2017) are cited by later patents (2018-2022). The methodology and findings are transferable to other industries. They can be used by managers and policymakers to (a) assist businesses in predicting innovation trajectories and (b) assist governments in designing and putting into action more effective policies that foster patented innovations in sectors that are deemed to be relevant to the national interest, thanks to the adoption of a new, complementary theoretical viewpoint that merges the micro- and macro-economic perspectives of citation flows.

Team diversity, conflict, and trust: Evidence from the health sector.

The current study aims to determine the impact of diversity and intra-team trust on conflict within the health sector of Pakistan. This study also measures the moderating role of trust in the relationship between diversity and conflict among team members. Data was collected using personally administered questionnaires from 61 teams, including 377 respondents working in 4 public sector hospitals in Pakistan, which were selected using a simple random sampling technique. The results revealed that diversity (as a composite) positively influences task conflict, while its two components-surface-level diversity and deep-level diversity-are associated positively with task conflict. Moreover, the results also lead to an exciting finding that trust among team members could reduce the positive influence of diversity on team members' conflict. The implications for theory and practitioners are presented along with the avenues for future research directions.

Application of Metaverse Service to Healthcare Industry: A Strategic Perspective.

This study is to explore a state of the art in metaverse service that is an emerging issue in applying it to the healthcare industry. The purpose of this study is to provide applicable strategic scenarios for effective metaverse service planning and implementation in healthcare settings. This study is focused on metaverse service as a business model. Thus, related literatures of metaverse service are reviewed in various aspects in healthcare industry. An exploratory approach is used to analyze current qualitative data characterizing healthcare metaverse service business positions and derive applicable strategies from business trends of current metaverse services. Several cases are examined based on the data obtained from various sources of healthcare and other related industries. This study synthesizes finding results and suggests applicable strategies of metaverse service in the healthcare industry. This study will facilitate strategic decision-making and policy-making processes to pursue a business opportunity development through an application of a metaverse service in healthcare and similar settings.

A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection.

COVID-19 is a global pandemic that mostly affects patients' respiratory systems, and the only way to protect oneself against the virus at present moment is to diagnose the illness, isolate the patient, and provide immunization. In the present situation, the testing used to predict COVID-19 is inefficient and results in more false positives. This difficulty can be solved by developing a remote medical decision support system that detects illness using CT scans or X-ray images with less manual interaction and is less prone to errors. The state-of-art techniques mainly used complex deep learning architectures which are not quite effective when deployed in resource-constrained edge devices. To overcome this problem, a multi-objective Modified Heat Transfer Search (MOMHTS) optimized hybrid Random Forest Deep learning (HRFDL) classifier is proposed in this paper. The MOMHTS algorithm mainly optimizes the deep learning model in the HRFDL architecture by optimizing the hyperparameters associated with it to support the resource-constrained edge devices. To evaluate the efficiency of this technique, extensive experimentation is conducted on two real-time datasets namely the COVID19 lung CT scan dataset and the Chest X-ray images (Pneumonia) datasets. The proposed methodology mainly offers increased speed for communication between the IoT devices and COVID-19 detection via the MOMHTS optimized HRFDL classifier is modified to support the resources which can only support minimal computation and handle minimum storage. The proposed methodology offers an accuracy of 99% for both the COVID19 lung CT scan dataset and the Chest X-ray images (Pneumonia) datasets with minimal computational time, cost, and storage. Based on the simulation outcomes, we can conclude that the proposed methodology is an appropriate fit for edge computing detection to identify the COVID19 and pneumonia with higher detection accuracy.

Developing Green Healthcare Activities in the Total Quality Management Framework.

This study examines the effectiveness of green healthcare activities in hospitals based on the total quality management (TQM) framework. The proposed research model and associated hypotheses were tested using structural equations modeling based on the data collected from 261 employees at general hospitals in South Korea. The results of the study revealed that the role of top management is essential for the successful implementation of green healthcare activities through motivating employees for their active participation in the program, providing continuous education and training on the importance of environmental sustainability, and diligent monitoring of the progress at the organization level. The study findings provide theoretical and practical implications on strategic approaches to planning and implementing green healthcare activities in hospitals for the greater good.

Aligning the American Health Information Management Association Entry-level Curricula Competencies and Career Map With Industry Job Postings: Cross-sectional Study.

BACKGROUND: The field of health information management (HIM) focuses on the protection and management of health information from a variety of sources. The American Health Information Management Association (AHIMA) Council for Excellence in Education (CEE) determines the needed skills and competencies for this field. AHIMA's HIM curricula competencies are divided into several domains among the associate, undergraduate, and graduate levels. Moreover, AHIMA's career map displays career paths for HIM professionals. What is not known is whether these competencies and the career map align with industry demands. OBJECTIVE: The primary aim of this study is to analyze HIM job postings on a US national job recruiting website to determine whether the job postings align with recognized HIM domains, while the secondary aim is to evaluate the AHIMA career map to determine whether it aligns with the job postings. METHODS: A national job recruitment website was mined electronically (web scraping) using the search term "health information management." This cross-sectional inquiry evaluated job advertisements during a 2-week period in 2021. After the exclusion criteria, 691 job postings were analyzed. Data were evaluated with descriptive statistics and natural language processing (NLP). Soft cosine measures (SCM) were used to determine correlations between job postings and the AHIMA career map, curricular competencies, and curricular considerations. ANOVA was used to determine statistical significance. RESULTS: Of all the job postings, 29% (140/691) were in the Southeast, followed by the Midwest (140/691, 20%), West (131/691,19%), Northeast (94/691, 14%), and Southwest (73/691, 11%). The educational levels requested were evenly distributed between high school diploma (219/691, 31.7%), associate degree (269/691, 38.6%), or bachelor's degree (225/691, 32.5%). A master's degree was requested in only 8% (52/691) of the postings, with 72% (42/58) preferring one and 28% (16/58) requiring one. A Registered Health Information Technologist (RHIT) credential was the most commonly requested (207/691, 29.9%) in job postings, followed by Registered Health Information Administrator (RHIA; 180/691, 26%) credential. SCM scores were significantly higher in the informatics category compared to the coding and revenue cycle (P=.006) and data analytics categories (P<.001) but not significantly different from the information governance category (P=.85). The coding and revenue cycle category had a significantly higher SCM score compared to the data analytics category (P<.001). Additionally, the information governance category was significantly higher than the data analytics category (P<.001). SCM scores were significantly different between each competency category, except there were no differences in the average SCM score between the information protection and revenue cycle management categories (P=.96) and the information protection and data structure, content, and information governance categories (P=.31). CONCLUSIONS: Industry job postings primarily sought degrees, with a master's degree a distant fourth. NLP analysis of job postings suggested that the correlation between the informatics category and job postings was higher than that of the coding, revenue cycle, and data analytics categories.

Methodological Guidelines for Systematic Assessments of Health Care Websites Using Web Analytics: Tutorial.

With the growing importance of communicating with the public via the web, many industries have used web analytics to provide information that organizations can use to better achieve their goals. Although the importance of health care websites has also grown, the health care industry has been slower to adopt the use of web analytics. Web analytics are the measurement, collection, analysis, and reporting of internet data used to measure direct user interaction. Our objective is to provide generalized methods for using web analytics as key performance metrics to evaluate websites and outline actionable recommendations for improvement. By deconstructing web analytic categories such as engagement, users, acquisition, content, and platform, we describe how web analytics are used to evaluate websites and how improvements can be made using this information. Engagement is how a user interacts with a website. It can be evaluated using the daily active users to monthly active users (DAU/MAU) ratio, bounce rate, pages viewed, and time on site. Poor engagement indicates potential problems with website usability. Users pertains to demographic information regarding the users interacting with a website. This data can help administrators understand who is engaging with their website. Acquisition refers to the overall website traffic and the method of traffic, which allows administrators to see how people are accessing their website. This information helps websites expand their methods of attracting users. Content refers to the overall relevancy, accuracy, and trustworthiness of a website's content. If a website has poor content, it will likely experience difficulty with user engagement. Finally, platform refers to the technical aspects of how people access a website. It includes both the internet browsers and devices used. By providing detailed descriptions of these categories, we have identified how web administrators can use web analytics to systematically assess their websites. We have also provided generalized recommendations for actionable improvements. By introducing the potential of web analytics to augment usability and the conversion rate, we hope to assist health care organizations in better communicating with the public and therefore accomplishing the goals of their websites.

Learning From Agility, Partnership and Innovation During the Covid-19 Pandemic: A Perspective From Industry.

Two years after the COVID-19 pandemic started, the world continues to adapt to the profound effects that Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has had on our lives. As the global crisis took hold, many looked to the medical technology/device industry for guidance and solutions. All while the industry itself, was disrupting its own processes and activities. In order to evolve and deliver accelerated innovation the industry had to be agile, resilient and collaborative with the broader healthcare community and technology partners. Now comes a time when we will start to see what changes were temporary and which ones will become part of the new process, but one thing is certain, we will not be going back to where we were pre-pandemic.

A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions.

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0.

Prioritizing the factors affecting adoption of e-commerce using multi-criteria decision making techniques in Tehran hospitals in 2021.

Background: The global use of the Internet has led to the growth of e-commerce in industries. Similarly, in health care, e-commerce is essential to meeting the high expectations of patients and providing high-quality and affordable health care services in clinics, hospitals, and other health centers. Purpose: This study aimed to prioritize the factors influencing the adoption of e-commerce in hospitals of Tehran (Iran( in 2021 using multi-criteria decision-making techniques. Method: The independent variables included organizational, contextual, environmental, and technological factors, while the dependent variable was e-commerce acceptance. To answer the research question, the data were collected via documentary research method (secondary data) and survey (primary data). The instrument used for the survey was a pairwise comparison questionnaire that was completed by 186 experts selected via random sampling method, based on Morgan's table, and with respect to inclusion and exclusion criteria. Using these instruments, the factors affecting the adoption of e-commerce were assessed using multi-criteria decision-making techniques and the AHP method. Results: The results of the prioritization of factors affecting the adoption of e-commerce in Tehran hospitals, from the experts' point of view, showed that the technological criterion (weight: 0.31918) is the most important factor, followed by organizational (weight: 0.30291), contextual (weight: 0.20346), and environmental (weight: 0.17445) factors. The consistency coefficient of the model was 0.021142. Conclusion: The findings indicate that it is possible for doctors, nurses, patients, and medical centers to exploit the benefits of e-commerce in primary care, environmental, financial, organizational, human-related, and technological factors in healthcare.

The Impact of telemedicine during the COVID-19 pandemic in Nigeria: A review

Background: Telemedicine practice is a rapidly evolving aspect of medicine in developed countries, though resource-limited countries like Nigeria are yet to embrace it fully. Moreover, the COVID-19 pandemic has limited patients' visits to hospitals in addition to the social distancing measures deployed by the government. In addition, with the challenges of a limited number of doctors, among other difficulties, it has become important that a radical approach to patient care and treatment should be explored. Methods: A comprehensive literature review of original articles was done using an internet search. Words such as Telemedicine, COVID-19, Pandemic, Requirements, History, Benefits, and Challenges were searched on Google scholar, EMBASE, PubMed, Medline, Web MD, and Scopusto check for various articles published or any probable link. The references of the relevant articles were searched. Results: The practice of telemedicine has evolved over the years. Also, the global telemedicine market has grown exponentially and is expected to grow even further in the next five years. Unfortunately, this exciting narrative is not obtainable in developing countries like Nigeria. Indeed, some healthcare providers in Nigeria need to prepare for this new technological advancement, and consequently, they struggle to evolve their practice to adopt this new technology. Furthermore, there is disinterest from most of the end users, particularly the patients. Conclusion: Modifications must involve telemedicine services with a view of getting ready and well-organized in the event of any future pandemic such as COVID-19, in addition to harnessing the benefits of this service in the future.