The ChatGPT application on quality management: a comprehensive review

BackgroundWorldwide, the organisation of diabetes care is changing. As a result general practices and diabetes teams in hospitals are becoming part of new organisations in which multidisciplinary care programs are implemented. In the Netherlands, 97 diabetes care groups and 104 outpatient clinics are working with a diabetes care program. Both types of organisations aim to improve the quality of diabetes care. Therefore, it is essential to understand the comprehensive elements needed for optimal quality management at organisational level. This study aims to assess the current level of diabetes quality management in both care groups and outpatient clinics and its improvement after providing feedback on their quality management system and tailored support.Methods/designThis study is a before-after study with a one-year follow-up comparing the levels of quality management before and after an intervention to improve diabetes quality management. To assess the status of quality management, online questionnaires were developed based on current literature. They consist of six domains: organisation of care, multidisciplinary teamwork, patient centeredness, performance management, quality improvement policy and management strategies. Based on the questionnaires, respondents will receive feedback on their score in a radar diagram and an elucidating table. They will also be granted access to an online toolbox with instruments that proved to be effective in quality of care improvement and with practical examples. If requested, personal support in implementing these tools will be available. After one year quality management will be measured again using the same questionnaire.DiscussionThis study will reveal a nationwide picture of quality management in diabetes care groups and outpatient clinics in the Netherlands and evaluate the effect of offering tailored support. The operationalisation of quality management on organisational level may be of interest for other countries as well.

Relationships between quality management, innovation and performance: A literature systematic review

It is possible to quantify the impact of medical care in specialised medical centers by measuring patient satisfaction. Modern quality management systems use this process as a part of result evaluation in the practice. This study describes an investigation of the influence of quality-management measures on patient satisfaction in ten specialist medical practices. One hundred patient questionnaires were issued between 2010 and 2011 in ten medical specialists practices before and after improvement of the quality management system. The evaluation of the patients were analysed using a Utest. By correlation analysis correlations between specific quality management activities and items of the patient questionnaire were identified. The results of the study indicate a generally positive reaction of patients to the improvement in the quality management of selected items. But in the totality of all practices, no clear improvement in patient satisfaction could be measured. There were weak but statistically significant correlations between specific quality-management-measure improvements and improvement of the patient questionnaire items identified. No significant improvements in patient satisfaction, which can be attributed directly to improvements in quality management, have been found by the study. But there were clues to correlations of specific quality-improvements and patient satisfaction issues. These can be used for directed quality management measures in practice and as a controlling tool of practice management regarding the evaluation of patients inside and on on-line doctor review sites.

To assess the change in level of diabetes quality management in primary care groups and outpatient clinics after feedback and tailored support. This before-and-after study with a 1-year follow-up surveyed quality managers on six domains of quality management. Questionnaires measured organization of care, multidisciplinary teamwork, patient centeredness, performance results, quality improvement policy, and management strategies (score range 0-100%). Based on the scores, responders received feedback and a benchmark and were granted access to a toolbox of quality improvement instruments. If requested, additional support in improving quality management was available, consisting of an elucidating phone call or a visit from an experienced consultant. After 1 year, the level of quality management was measured again. Of the initially 60 participating care groups, 51 completed the study. The total quality management score improved from 59.8% (95% CI 57.0-62.6%) to 65.1% (62.8-67.5%; P < 0.0001). The same applied to all six domains. The feedback and benchmark improved the total quality management score (P = 0.001). Of the 44 participating outpatient clinics, 28 completed the study. Their total score changed from 65.7% (CI 60.3-71.1%) to 67.3% (CI 62.9-71.7%; P = 0.30). Only the results in the domain multidisciplinary teamwork improved (P = 0.001). Measuring quality management and providing feedback and a benchmark improves the level of quality management in care groups but not in outpatient clinics. The questionnaires might also be a useful asset for other diabetes care groups, such as Accountable Care Organizations.

The core idea of quality work is to create enduring quality by providing users with reliable, easy-to-use, and surprising products. In the Internet+ era, user demand for quality has changed with the times. Customers are no longer satisfied with the requirements of basic quality functions, and more in pursuit of ease of use, pleasure and even identity. Even if the product has "satisfied with the specifications" in quality, users are still dissatisfied and express their dissatisfaction through the Internet. If we can use the Internet, big data, artificial intelligence and other technologies to collect and analyze user expectations or dissatisfaction with product quality and find improvement directions, then quality management will see great progress. Based on this, we can create a new type of quality big data analysis and management system, which we can call the Product Satisfaction Index (PSI) management system. Through the analysis and transformation of PSI, the customer’s textual expectations are turned into numerical quantifiable indicators. Through the management of PSI indicators, quality big data mining and analysis are performed to improve the product to meet customer expectations. In advancing the product satisfaction index (PSI) indicator management system, we should fully implement the definition of PSI indicators, strategic position, data analysis methods, closed-loop management models, review and continuous improvement, and data platforms. Visualize customer needs, analyze the matching between product characteristics and customer needs, and promote continuous improvement of PSI indicators through closed-loop management. The new PSI index management method opens up new ideas and methods for quality performance management, and helps build high-quality development and create customer satisfaction.

The adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMBs) has emerged as a key strategy for improving quality management. This article discusses the main benefits and challenges of implementing AI in this context. A systematic review of the academic literature and relevant business reports was carried out, in order to collect and analyze empirical evidence on the application of AI in the quality management of SMEs, obtaining as results that the main benefits of the implementation of AI in the quality management of SMEs include: Improved data-driven decision-making, process automation and error reduction, early detection of quality issues, customization of products and services, and resource optimization and cost reduction. On the other hand, the main challenges were: Lack of knowledge and skills in AI, high implementation costs, concerns about data security and privacy, resistance to cultural change, and integration of AI with existing systems and processes, concluding that the implementation of AI in SMB quality management offers significant benefits, but it also entails challenges that must be addressed strategically. SMEs that manage to overcome these challenges will be able to take advantage of the competitive advantages offered by AI, improving their efficiency, quality and adaptability in an increasingly dynamic business environment.

This study aims to systematically review the ethical and legal discussions regarding the utilization of artificial intelligence (AI) for patient triage and resource allocation in emergency medicine, and to identify the current state of discussions, their limitations, and future research directions. A comprehensive literature search was conducted following scoping review methodology. Relevant literature published after January 2020 was searched in the Web of Science, Scopus, CINAHL, PubMed, and Cochrane Library databases. Based on a PCC (population, concept, and context) framework (emergency patients/medical staff; triage, resource allocation; and emergency medicine with AI application), a final selection of 27 articles was analyzed. The selected literature raised various ethical and legal issues related to the introduction of AI triage systems and AI utilization in emergency medicine, including data privacy, algorithmic bias, automation dependency, accountability, and explainability. In response to these issues, human-centered design, implementation of explainable AI, establishment of regulatory frameworks, continuous verification and evaluation, and ensuring human-in-the-loop were discussed as major solutions. However, discussions on the risks of "persuasive AI" that could mislead users, ethical issues of generative AI, and social validation and patient and public involvement were found to be insufficient. Ethical and legal discussions regarding AI in emergency medicine are evolving toward seeking concrete solutions at technical, institutional, and relational dimensions. However, in-depth research on ethical challenges, such as reflecting the specificity of rapidly developing AI and the values of emergency medicine, is urgently required.

This scoping review examines the integration of artificial intelligence (AI) tools into scientific education practices in school settings. Following the PRISMA statement guidelines, a literature search was conducted in the Web of Science and Scopus databases, identifying 2892 articles published between 2020 and 2024. After applying the eligibility criteria, 75 primary studies with empirical data demonstrating the outcomes of school‐based scientific practices using AI tools were selected. The studies were coded based on the type of AI, the type of scientific practice, AI competencies, and other contextual and pedagogical aspects. The results indicate that the most frequently used tools by students are computer vision, natural language processing and data mining. Most studies focus on secondary education levels, combining strategies such as project‐based learning, scientific inquiry and the STEM approach. Although the evidence suggests that these tools enhance scientific skills like data interpretation and computational thinking, limitations were identified, including the use of low‐reliability or paid software and a lack of representation of ethical competencies. Finally, the review highlights the need to strengthen interdisciplinary collaboration and the design of curricular programmes that integrate AI through an ethical framework. The findings are discussed to provide guidance for pedagogy, public policies and future research. Context and implications Rationale for this study: This study was conducted to examine current evidence on how AI tools are being integrated into K‐12 scientific practices. Why the new findings matter: The findings presented are relevant for analysing current trends, identifying research gaps and exploring opportunities to update interdisciplinary pedagogical frameworks and digital literacy curriculum policies. Implications for educational practitioners, policy makers and researchers: For education practitioners, this study provides a planning model with practical examples for integrating AI into K‐12 scientific practices. For policy makers, it highlights the urgency of updating curricular frameworks and public education policies to ensure equitable access to trustworthy technologies, as well as initial and ongoing teacher training in AI, while fostering interdisciplinary collaboration, particularly between science and mathematics. For researchers, there is a clear need to expand empirical studies that examine scientific practices in virtual and hybrid modalities, in underexplored levels such as elementary education, and that take into account ethical competencies in the use of AI.

Purpose The purpose of this study is to analyze the relationship between lean manufacturing and corporate environmental performance. Scholarly literature has extensively addressed the relationship between those two areas but empirical papers present mixed and inconsistent results, calling for further analysis to establish a clearer understanding of the actual relationship and to identify the causes of conflicting findings across studies. Given the importance of sustainable practices in the current business landscape, this paper aims to provide a comprehensive overview of this relationship through a meta-analysis of previous research, with a focus on integrating quantitative findings to shed light on the potential impact of lean manufacturing on environmental performance and report its intensity with Pearson’s correlation coefficient. Design/methodology/approach This paper analyzes the data from 29 primary studies published between 2001 and 2022 that have empirically measured the relationship between lean manufacturing and corporate environmental performance and that have been identified in the Web of Science and SCOPUS databases through an exhaustive review of the literature. To integrate previous empirical results and evaluate the evidence for the lean manufacturing’s influence on environmental performance, a meta-analytic methodology was adopted through the Hedges–Olkin random effect approach, based on correlations. Findings Main findings support the notion that a significant, positive, rather moderated, relationship exists between lean manufacturing and environmental performance, with an overall correlation coefficient r¯ = 0.25. The result seems robust, as several tests confirm that publication bias is unlikely. Overall, various lean practices are correlated to varying degrees with different measures of environmental performance. The environmental efficiency of external lean practices is higher than that observed for internal practices and quality management has a more pronounced effect than other internal lean practices. The 2008 global crisis did not change these relationships and the impact of lean manufacturing on environmental performance does not seem to be moderated by the degree of environmental awareness of the country where applied. Research limitations/implications The results conclude that a significant, positive relationship exists between lean manufacturing and environmental performance (r¯ = 0.25). This study tests certain factors that exhibit varying effect sizes and moderate the overall outcome, highlighting that the environmental efficiency of external lean practices is higher than that observed for internal practices. In addition, it suggests a more pronounced effect of quality management among other internal lean practices. Practical implications This study provides companies with an opportunity to align their operational strategies with environmental sustainability goals. Understanding that various lean practices exhibit diverse levels of correlation with multiple measures of environmental performance, decision-makers can prioritize their efforts and apply the lean practices that have a stronger effect on the desired environmental outcomes to improve their environmental impact. Conversely, managers are aware that certain lean practices have a week relationship with some environmental performance so they can avoid overestimating environmental benefits of lean manufacturing. Finally, results underscore the importance of organizational commitment to environmental sustainability. Originality/value It is, to the best of the authors’ knowledge, the first meta-analytic study to investigate the strength of the association between lean manufacturing and environmental performance and to test whether various lean practices are correlated to different measures of environmental performance. It fills this gap in the literature and therefore it represents a valuable contribution to the field. In addition, this paper explores certain factors that moderate the overall outcome.

Objective The global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care.

The objective of the present systematic review is to identify which artificial intelligence (AI) approaches have been used to successfully detect voice disorders. The review examines studies involving patients with non-neurological voice disorders and controls, where AI was applied to detect voice disorders. The primary outcome of interest is the accuracy of these AI models. Additionally, this review demonstrates how the procedures of conducting a systematic review can be supported by AI. Studies were eligible for inclusion if they implemented an AI approach to detect non-neurological voice disorders from healthy voice samples. A comprehensive search was conducted using PubMed/MEDLINE, Science Direct, Web of Science, EBSCO, and Scopus databases. Risk of bias was assessed via the Quality Assessment Tool for Diagnostic Accuracy Studies. The occurrences of the most common AI techniques utilized in the literature are presented, and a summary of their abilities to accurately detect a voice disorder is reported. In total, 79 publications met the inclusion criteria. These studies included patient recordings from a variety of voice databases. The most common AI techniques implemented were Support Vector Machines (SVMs) (n=28) and Convolutional Neural Networks (CNNs) (n=22). The mean accuracy of the modelsin detecting voice disorders was 92% across all studies. Nine studies reported 100% accuracy, and 32studies reported between 95%and99%. Strengths of the evidence include high accuracies across diverse models and datasets. Limitations include a limited variety of datasets and a trend of hyperoptimization without sufficient external validation. Clinicians and researchers should recognize that while current AI models show promise, future studies should prioritize robust external validation and more representative datasets.

Variation in Results of Three Biology‐Focused Search Engines: A Case Study Using North American Tree Species

This study examines the role of artificial intelligence in enhancing quality management practices within Algerian startups, focusing specifically on how AI-driven business intelligence contributes to competitive advantage through quality improvements in emerging market contexts. The research employs quantitative methodology, utilizing structural equation modeling (CB-SEM) to analyze data collected from 357 Algerian startups. The study implements a comprehensive measurement framework incorporating quality management practices, AI implementation status, and competitive advantage indicators, validated through confirmatory factor analysis. The analysis reveals that quality management supported by artificial intelligence has a moderate positive impact on competitive advantage (correlation coefficient 0.346, p &lt; 0.001). Organizations implementing AI-enabled quality management systems achieved a 52.4% improvement in overall quality metrics. Customer response capability scored highest among quality dimensions (mean score 2.86), while product-market alignment showed room for improvement (mean score 2.53). The research identified three critical areas of AI integration success: quality control automation, predictive quality management, and customer response systems. The study provides actionable insights for startups in emerging markets implementing AI-driven quality management systems. The findings suggest a staged approach to technology adoption, emphasizing the importance of foundational quality management practices before advanced AI integration. Results indicate that successful implementation requires balanced investment in both technological infrastructure and organizational capabilities. While Algerian startups demonstrate awareness of and commitment to AI-enabled quality management, with 50.8% showing a positive disposition toward adoption, actual implementation remains at moderate levels. The study highlights significant opportunities for enhancement in quality management through strategic AI integration, particularly in emerging market contexts where technological infrastructure and resource constraints present unique challenges.

This is based on the evaluation of the management quality theory, by integrating the related theory; we present a comprehensive Six Sigma quality management theory for highway engineering quality supervision and management. This theory uses the total quality management theory to realize the full responsibility of the main participation, and it uses Six Sigma quality management theory to guide the various stages and key processes of quality management, and it uses fuzzy comprehensive evaluation method to achieve the evaluation of the entire project and the various processes. This essay provides the working program which uses this theory and the corresponding quality management content.

Quality management covers every corner of hospital services,which upholds full-process quality management,continuous quality improvement and smooth management with patients at the center.By means of building a 3-tier quality management framework at the hospital,development of quality management criteria,appraisal indicators and documentation and use of quality management tools,quality management defects were identified from a third-party perspective.This effort has significantly improved quality of care at the hospital,and elevated the management execution and general service quality of the hospital. Key words: Quality management; System; Service quality; Medical safety; Continuous improvement