The transformative impact of cloud computing on modern healthcare services: A technical analysis

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into Enterprise Resource Planning (ERP) systems represents a paradigmatic shift in organizational digital transformation strategies. This research investigates the revolutionary impact of AI/ML integration within SAP ecosystems, examining performance improvements, operational efficiency gains, and strategic advantages. Through comprehensive analysis of implementation patterns across diverse industry sectors, this study presents empirical evidence demonstrating significant enhancements in predictive analytics, automated decision-making, and resource optimization. The findings reveal that organizations implementing AI-enabled SAP solutions experience an average 35% improvement in operational efficiency, 42% reduction in manual processing time, and 28% increase in predictive accuracy for demand forecasting. This research contributes to the growing body of knowledge on intelligent ERP systems and provides practical insights for enterprise digital transformation initiatives.

Background: Nursing students, as future healthcare professionals, must understand how artificial intelligence can impact patient care and clinical decision making. Aim: To assess nursing students perspective about integration of artificial intelligence into health care services. Study design: Descriptive correlational, design was utilized. Setting: was conducted at Faculty of Nursing, Assiut university. Subject: 551, nursing students from the final academic year = 291 and 260 nursing student from internship year. Methods: Data were collected using : I- Personal data form, II- Nursing students awareness and knowledge regarding integration of AI into healthcare services, and III- Perception about integration of AI into healthcare services, IV- Nursing students attitude toward integration of AI into healthcare services. Results: the study revealed that nearly three fourths of study sample aware regarding to their level of total awareness toward AI (73.3%) , and two thirds of them are knowledgeable about AI, (66.6%) And more than half of the total studied nursing students have high level of total perception(51.4%), less than half of the total studied nursing students have moderate level of total perception(46.6. And (68.4%) of the total studied nursing students have positive attitude toward AI. Conclusion: there was a high statistical significant positive association among students knowledge, perception and attitude regarding AI integration in health care services at (P< 0.001). Recommendation: Encourage further researches into the long- termed impact of AI training on nursing practices, clinical decision-making, patient care, and healthcare outcomes. Key words: Integration of AI, awareness, knowledge, perception, attitude.

The contemporary context of population aging, itsthe population's different health and disease characteristics, and the growing incorporation of technologies by healthcare systems have highlighted the need to adjust the healthcare structure as a whole. The defense of a democratic and sustainable system reveals the importance of understanding how changes in healthcare take place. The current article aims to contribute to the understanding of innovation in healthcare services. The study's results indicate that the existence of certain knowledge gaps means that public policies tend to overlook a whole rangeseries of innovations normally associated with social changes, with a consequentwith an impact on human development, social cohesion, equality, and equity, allcentral issues that are central toin the field of collective public healthcare field. The article concludes that the lack of a mature theoretical framework negatively impacts the formulation of such policies, further aggravated in Brazil by growing differences in quality and access between population segments that depend on the public and private healthcare systems. Resumo: O contexto contemporâneo de envelhecimento e as características de saúde e doença da população, aliados à crescente incorporação tecnológica nos sistemas de saúde têm pontuado a necessidade de adequação da estrutura de atenção. A defesa de um sistema democrático e sustentável evidencia a importância de entender como são estabelecidos os processos relacionados às mudanças na saúde. Neste cenário, o presente artigo visa aprofundar o entendimento sobre inovação nos serviços de saúde. Os resultados da pesquisa indicaram que lacunas no conhecimento levam políticas públicas a negligenciarem uma série de inovações normalmente associadas a mudanças sociais com impacto no desenvolvimento humano, coesão social, igualdade e equidade, temas centrais ao campo da saúde coletiva. E conclui que a falta de um referencial teórico maduro tem impactos deletérios para a formulação dessas políticas, quadro agravado no Brasil, onde são observadas diferenças crescentes na qualidade e disponibilidade de acesso entre os segmentos populacionais dependentes dos sistemas públicos e privados.

Electronic patient records and innovation in health care services

Robotic process automation (RPA) is transforming the healthcare industry through the automation of jobs, optimisation of workflows, and improvement of operational efficiency. This chapter examines the revolutionary capacity of robotic process automation (RPA), emphasising its various uses, advantages, and ethical implications. Although RPA enhances the efficiency of resource allocation and enhances the quality of patient care, it is crucial to tackle concerns such as job displacement and data privacy. Empirical case studies provide concrete evidence of the tangible effects of robotic process automation (RPA) on the delivery of healthcare services. Anticipating and adopting the progress of robotic process automation (RPA) with ethical accountability holds the potential for a more streamlined and patient-focused healthcare system.

In recent years, Thailand and many countries around the world have faced challenges caused by inequity in accessing healthcare services, chronic disease management, demographic structural changes and an increased number of disabled populations. These facts have amplified dire needs for innovative healthcare services not only for treatments but also for prevention and public health promotion. To expedite the development of such innovations in community-based healthcare, ambulatory care and inpatient services, healthcare experts, technologists, and key stakeholders have teamed up at both the national and international level to develop a blueprint for implementing better and smarter healthcare services for both consumers and healthcare providers in Thailand. Three important business processes healthcare services reimbursement, patient referral and consultation and decision support processes linking sickness and population data are discussed to demonstrate the usability of the blueprint. The blueprint is in various stages of deployment in Thailand.

Background: The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. The aim is to synthesize the evidence on the use of artificial intelligence in the organization of nursing care. Methods: A scoping review was carried out based on the Joanna Briggs Institute methodology, following the PRISMA-ScR guidelines, in the MEDLINE, CINAHL Complete, Business Source Ultimate and Scopus® databases. We used ProQuest—Dissertations and Theses to search gray literature. Results: Ten studies were evaluated, identifying AI-mediated tools used in the organization of nursing care, and synthesized into three tool models, namely monitoring and prediction, decision support, and interaction and communication technologies. The contributions of using these tools in the organization of nursing care include improvements in operational efficiency, decision support and diagnostic accuracy, advanced interaction and efficient communication, logistical support, workload relief, and ongoing professional development. Conclusions: AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.

The integration of artificial intelligence and automation technologies is fundamentally transforming trade promotion management and sales processes within SAP's Customer Relationship Management and S/4HANA Sales and Distribution modules. This transformation spans multiple functional areas, including automated approval workflows, dynamic fund allocation, smart claim processing, advanced sales forecasting, demand sensing, ROI optimization, intelligent order processing, conversational AI, and robotic process automation. Organizations implementing these technologies have experienced substantial improvements in operational efficiency, forecast accuracy, promotional effectiveness, and overall return on investment. Key enablers include multi-dimensional data analysis, external data integration, continuous learning capabilities, and causal factor analysis. Success factors include data quality management, comprehensive change management, and phased implementation approaches. Future developments in edge computing, quantum computing, and extended reality promise to further enhance these capabilities, creating significant competitive advantages for early adopters.

Purpose: Complaint Management (CM) is significant in improving the quality of healthcare service delivery. There is a dearth of knowledge regarding CM in healthcare service in Ghana. This paper aims at exploring the drivers, inhibitors, implications and influence of leadership on effective CM in healthcare service delivery.&#x0D; Methodology: A qualitative methodology was used. Data were collected through in-depth semi-structured interviews with 20 purposively selected informants. Data were transcribed and analysed thematically.&#x0D; Findings: The evidence suggests that there are some notable drivers, inhibitors, and implications of CM in healthcare service delivery. The study found that customer complaint, customer satisfaction, customer feedback, customer loyalty, customer retention, quality assurance, competitive advantage, compensation, culture, and speedy recovery are the drivers to effective CM in healthcare service delivery. However, regarding the factors that inhibit effective CM in healthcare service delivery, the study found insufficient skilled personnel, inadequate technology, lack of infrastructure, inadequate resources/funds and lack of oversight responsibility or control, lack of accountability, unresponsiveness, and unacceptance of mistakes/complaint. The study provides insight that effective CM has significant implications on customer satisfaction, customer loyalty, customer retention/commitment, institutional reputation/image, organisational performance, profitability, and good staff attitude. The results of the research corroborate the perception that leadership style influences the effectiveness of CM in healthcare service delivery.&#x0D; Recommendation: The study strongly recommend the establishment of CM department with the requisite logistics and accessories to promote effective handling of complaint. Additionally, it endorses training programmes on building strategies to enhance effective CM which will go a long way toward allowing service providers to form strong emotional bonds with their patients. The focus of such a program should be on instilling in frontline staff who deal with disgruntled/dissatisfied customers the necessity of adopting the study's findings into their complaint handling process.

This research paper explores the effectiveness of fundamental and technical analysis in predicting stock market returns. While fundamental analysis emphasizes the intrinsic value of stocks based on economic indicators, financial statements, and broader macroeconomic trends, technical analysis focuses on historical price trends, trading volume, and market psychology to anticipate future movements. Drawing on recent academic studies, practitioner insights, and comparative case analyses across different markets, this paper critically evaluates the predictive power, inherent limitations, and practical relevance of each approach under varying market conditions. The findings suggest that while each methodology offers unique strengths, combining both analytical frameworks may provide a more robust and holistic investment strategy—particularly in volatile, uncertain, or information-asymmetric markets. Additionally, the integration of artificial intelligence and machine learning with these traditional methods is highlighted as a promising advancement in enhancing forecasting accuracy and decision-making efficiency in modern financial markets. Keywords: Fundamental analysis, technical analysis, stock returns, market prediction, investment strategies.

Background:&#13;\n&#13;\nChildren with cerebral palsy often have numerous health care needs and require complex multidisciplinary health care. Holistic family centred care that is inclusive of the viewpoints of parents and children is regarded as best practice for health care service delivery for children with cerebral palsy and their families. However, dissatisfaction with care is an ongoing issue for parents of children with cerebral palsy, despite theoretical changes to health care service delivery practices toward a more family centred approach. In addition, current guidelines and standards of care for this population have been developed by health professionals without, or with little, involvement of parents. Parents' perceptions of their child's health care needs may differ from that of health professionals. If the existing guidelines and standards do not reflect the needs of the child and the family due to the lack of parental input, this may be part of the reason why parents of children with cerebral palsy continue to be dissatisfied with their child's care. No previous researchers have investigated how parents of children with cerebral palsy experience health care for their child.&#13;\n&#13;\nAim:&#13;\n&#13;\nThe aim of this study is to explore parents' experiences of health care for their children with cerebral palsy living in a regional area of Queensland, Australia. From this exploration, the aim of this study is to develop a conceptual model that explains parents' experiences and can be used to inform future clinical health care service delivery practices and guidelines and standards of care for children with cerebral palsy and their families.&#13;\n&#13;\nMethods:&#13;\n&#13;\nThis study is a qualitative exploratory study using grounded theory methodology, and is underpinned by a relativist approach and social constructivist viewpoint. The study design was selected based on the purposes and clinical context of the study, as well as my positioning as a health professional with a background in physiotherapy who has provided health care to children with cerebral palsy and their families. Participants were parents of children with cerebral palsy living in a regional area of Queensland, Australia, and were recruited using convenience and theoretical sampling. Data collection and analysis was conducted concurrently following the grounded theory method of constant comparative analysis. Two focus groups and eight in-depth interviews were conducted with eleven parents over a ten month period. Data was analysed using systematic grounded theory methods including coding, memoing, sorting and diagramming. A conceptual model was developed to explain parents' experiences. The final conceptual model was compared and contrasted with relevant literature, enabling identification of how the study findings contributed to current understandings of health care service delivery for children with cerebral palsy and their families.&#13;\n&#13;\nResults:&#13;\n&#13;\nParents living in a regional Queensland area experience health care for their children with cerebral palsy as an constant but evolving process of "making the most of their body and their life". The core category of "making the most of their body and their life" is how parents' experience the overall purpose for health care in the life of their child and family. Key categories of the conceptual model that represent important aspects of care for parents of children with cerebral palsy include learning as you go, navigating the systems, meeting needs through partnership, being empowered or disempowered, and finding a balance. These categories resonate with and support key principals of current approaches to health care service delivery for this population including family centred care, the concept of a partnership between parents and their children's health care providers, and empowerment. However, the findings of the study also identify new understandings of parents' experiences of health care for their children with cerebral palsy. These understandings include the temporal nature of the needs of the child and family evolving over time, the concept of being empowered or disempowered through the interactive processes of health care, the importance of having a community or network of supports to receiving information and guidance, and the importance of the attitudes and values of health care workers and services to parents' experiences of their child's health care.&#13;\n&#13;\nConclusion:&#13;\n&#13;\nHealth care workers and services should take into consideration parents' experiences of health care for their children with cerebral palsy and the impact it has on them, their child and their families. Modifications to health care service delivery practices to better reflect parents' experiences of health care and important aspects of care, as well as the development of new models of care that incorporate the conceptual model developed from this study, could potentially assist in improving parental satisfaction with care for their children with cerebral palsy. Further research exploring children's experiences of health care, as well as comparison of experiences in different geographical areas, with different models of service delivery, and with health professionals and policy makers, is required to develop a broader theory around health care service delivery for children with cerebral palsy and their families.

Integrating Artificial Intelligence (AI) with Secure Access Service Edge (SASE) architecture represents a significant advancement in enterprise network security and management. This article examines the transformative impact of AI technologies on SASE implementations, focusing on enhanced threat detection capabilities, dynamic network optimization, and automated compliance management. The article comprehensively analyzes AI-driven security features, including real-time threat detection, zero-trust implementation, and privacy-preserving security measures. Through extensive evaluation of deployment scenarios and performance metrics, our research demonstrates significant improvements in security effectiveness and operational efficiency, including a 90% reduction in threat detection time, a 45% decrease in network latency, and a 65% reduction in manual configuration tasks. The article highlights the system's capability to maintain robust security while preserving data privacy through advanced encrypted traffic analysis techniques. The article addresses current integration challenges and provides insights into successful implementation strategies. This article contributes to the growing body of knowledge on AI-enhanced network security architectures and provides valuable insights for organizations seeking to modernize their security infrastructure while maintaining operational efficiency and compliance with evolving regulatory requirements.

Background: The integration of Artificial Intelligence (AI) and automation in healthcare has emerged as a transformative force, reshaping the modern medical environment. These technologies promise to enhance operational efficiency, improve diagnostic accuracy, and optimize patient care. However, their adoption also raises ethical, technical, and operational challenges. Objective: This systematic review aims to explore the impact of AI and automation on the healthcare sector, focusing on their applications, benefits, challenges, and future potential. Methods: A systematic search was conducted across major databases, including PubMed, Scopus, and Web of Science, to identify peer-reviewed studies published between 2016 and 2024. Studies were selected based on their relevance to AI and automation in medical environments, using predefined inclusion and exclusion criteria. Data were extracted and analyzed to identify key themes. Results: The review identified significant advancements in AI-driven diagnostics, robotic-assisted surgeries, workflow automation, and predictive analytics. These technologies have demonstrated measurable improvements in clinical outcomes, operational efficiency, and cost reduction. However, challenges such as data privacy, ethical concerns, and integration issues persist. Conclusion: AI and automation are revolutionizing healthcare by streamlining processes, enhancing patient outcomes, and addressing resource limitations. Despite current challenges, their potential to transform the medical environment is undeniable. Further research and multidisciplinary collaboration are essential to overcome barriers and fully harness these technologies.

The collapse of the Soviet Union in 1989 substantially increased the numbers of refugees and immigrants to the United States from the former Soviet Union. Little research has been conducted with this population although studies found that immigrant's access to health care services are based on patterns of utilization in their countries of origin. The purpose of this study was to learn about the experiences of immigrant women from three former Soviet Republics (Belarus, Russia, and Ukraine) with women's health care services. Three focus groups of women were formed; ages 20-30, 37-46, and 60 and above. A focus-group guide was used to learn about their health care experiences. These immigrant women did access health care services based on patterns of utilization in their countries of origin. Greater understanding of immigrant populations' cultural patterns of health care utilization is needed to improve access and delivery of health care services to these populations.

Healthcare Applications and Services in Converged Networking Environments