Search Results (52)

Healthcare Information Systems (HISs) are essential for improving care quality, managing chronic diseases, and supporting clinical decision-making. Despite significant investments, HIS implementations often fail due to the complexity of healthcare environments. Maturity Models (MMs) have emerged as tools to guide organizational improvement by assessing readiness, process efficiency, technology adoption, and interoperability. This study presents a comprehensive literature review identifying 45 Maturity Models used across various healthcare domains, including telemedicine, analytics, business intelligence, and electronic medical records. These models, often based on Capability Maturity Model Integration (CMMI), vary in structure, scope, and maturity stages. The findings demonstrate that structured maturity assessments help healthcare organizations plan, implement, and optimize HIS more effectively, leading to enhanced clinical and operational performance. This review contributes to an understanding of how different MMs can support healthcare digital transformation and provides a resource for selecting appropriate models based on specific organizational goals and technological contexts. Full article

Background: The integration of artificial intelligence (AI) into healthcare research has the potential to enhance research capacity, streamline protocol development, and reduce barriers to engagement. Medway NHS Foundation Trust identified a plateau in homegrown research participation, particularly among clinicians with limited research experience. A generative AI-driven chatbot was introduced to assist researchers in protocol development by providing step-by-step guidance, prompting ethical and scientific considerations, and offering immediate feedback. Methods: The chatbot was developed using OpenAI’s GPT-3.5 architecture, customised with domain-specific training based on Trust guidelines, Health Research Authority (HRA) requirements, and Integrated Research Application System (IRAS) submission protocols. It was deployed to guide researchers through protocol planning, ensuring compliance with ethical and scientific standards. A mixed-methods evaluation was conducted using a qualitative-dominant sequential explanatory design. Seven early adopters completed a 10-item questionnaire (5-point Likert scales), followed by eight free-flowing interviews to achieve thematic saturation. Results: Since its launch, the chatbot has received an overall performance rating of 8.86/10 from the seven survey respondents. Users reported increased confidence in protocol development, reduced waiting times for expert review, and improved inclusivity in research participation across professional groups. However, limitations in usage due to free-tier platform constraints were identified as a key challenge. Conclusions: AI-driven chatbot tools show promise in supporting research engagement in busy clinical environments. Future improvements should focus on expanding access, optimising integration, and fostering collaboration among NHS institutions to enhance research efficiency and inclusivity. Full article

The application of Artificial Intelligence and Machine Learning in the supply chain fields is significantly changing the way businesses manage their operations, forecast their demand, manage their inventory, optimize their logistics, and increase their level of resilience. This research explores, through a bibliometric literature review, how the integration of these technologies can support the implementation of a demand-driven supply chain approach in the global healthcare and pharmaceutical supply chains, which are facing remarkable challenges in ensuring demand-driven operations, especially in light of sudden disruptions such as the COVID-19 pandemic. Full article

The integration of artificial intelligence (AI) technologies is reshaping diverse domains of human activity, including natural resource management, urban and rural planning, agri-food systems, industry, energy, education, and healthcare. However, the impact of AI on the sustainability of local ecosystems remains insufficiently systematized. This highlights the need for a comprehensive review that considers spatial, sectoral, and socio-economic characteristics of regions, as well as interdisciplinary approaches to sustainable development. This study presents a scoping review of 198 peer-reviewed publications published between 2010 and March 2025, focusing on applied cases of AI deployment in local contexts. Special attention is given to the role of AI in monitoring water, forest, and agricultural ecosystems, facilitating the digital transformation of businesses and territories, assessing ecosystem services, managing energy systems, and supporting educational and social sustainability. The review includes case studies from Africa, Asia, Europe, and Latin America, covering a wide range of technologies—from machine learning and digital twins to IoT and large language models. Findings indicate that AI holds significant potential for enhancing the efficiency and adaptability of local systems. Nevertheless, its implementation is accompanied by notable risks, including socio-economic disparities, technological inequality, and institutional limitations. The review concludes by outlining research priorities for the sustainable integration of AI into local ecosystems, emphasizing the importance of cross-sectoral collaboration and scientific support for regional digital transformations. Full article

Over the past five years, the application of artificial intelligence (AI) including its significant subset, machine learning (ML), has significantly advanced pharmaceutical procedures in community pharmacies, hospital pharmacies, and pharmaceutical industry settings. Numerous notable healthcare institutions, such as Johns Hopkins University, Cleveland Clinic, and Mayo Clinic, have demonstrated measurable advancements in the use of artificial intelligence in healthcare delivery. Community pharmacies have seen a 40% increase in drug adherence and a 55% reduction in missed prescription refills since implementing artificial intelligence (AI) technologies. According to reports, hospital implementations have reduced prescription distribution errors by up to 75% and enhanced the detection of adverse medication reactions by up to 65%. Numerous businesses, such as Atomwise and Insilico Medicine, assert that they have made noteworthy progress in the creation of AI-based medical therapies. Emerging technologies like federated learning and quantum computing have the potential to boost the prediction of protein–drug interactions by up to 300%, despite challenges including high implementation costs and regulatory compliance. The significance of upholding patient-centred care while encouraging technology innovation is emphasised in this review. Full article

The usage of digital twins (DTs) is growing across a wide range of businesses. The health sector is one area where DT use has recently increased. Ultimately, the concept of digital health twins holds the potential to enhance human existence by transforming disease prevention, health preservation, diagnosis, treatment, and management. Big data’s explosive expansion, combined with ongoing developments in data science (DS) and artificial intelligence (AI), might greatly speed up research and development by supplying crucial data, a strong cyber technical infrastructure, and scientific know-how. The field of healthcare applications is still in its infancy, despite the fact that there are several DT programs in the military and industry. This review’s aim is to present this cutting-edge technology, which focuses on neurology, as one of the most exciting new developments in the medical industry. Through innovative research and development in DT technology, we anticipate the formation of a global cooperative effort among stakeholders to improve health care and the standard of living for millions of people globally. Full article

This paper investigates the transformative potential of integrating technical and methodological tools such as GraphQL, openEHR, Redis, and Pervasive Business Intelligence in healthcare. Modern healthcare systems face data silos, interoperability, and efficient data communication challenges. The integration of these technologies offers innovative solutions to address these challenges. GraphQL, known for its flexible data retrieval capabilities, simplifies data communication and integration. openEHR, a standards-based approach to healthcare data management, fosters interoperability through a unified data model. Redis, a scalable data storage and caching system, enhances application performance and real-time data processing. Pervasive Business Intelligence empowers healthcare analytics, aiding data-driven decision-making by enabling an integrated Electronic Health Record. This paper explores these technologies’ benefits, integration possibilities, and synergies. The practical implications of this integration are demonstrated through a real-world case study. The findings underscore the potential to revolutionize healthcare data management, communication, and analysis, improving patient care and operational efficiency. Full article

Background—Diabetes is a rapidly spreading chronic disease that poses a significant risk to individual health as the population grows. This increase is largely attributed to busy lifestyles, unhealthy eating habits, and a lack of awareness about the disease. Diabetes impacts the human body in various ways, one of the most serious being diabetic retinopathy (DR), which can result in severely reduced vision or even blindness if left untreated. Therefore, an effective early detection and diagnosis system is essential. As part of the Kingdom of Saudi Arabia’s Vision 2030 initiative, which emphasizes the importance of digital transformation in the healthcare sector, it is vital to equip healthcare professionals with effective tools for diagnosing DR. This not only ensures high-quality patient care but also results in cost savings and contributes to the kingdom’s economic growth, as the traditional process of diagnosing diabetic retinopathy can be both time-consuming and expensive. Methods—Artificial intelligence (AI), particularly deep learning, has played an important role in various areas of human life, especially in healthcare. This study leverages AI technology, specifically deep learning, to achieve two primary objectives: binary classification to determine whether a patient has DR, and multi-class classification to identify the stage of DR accurately and in a timely manner. The proposed model utilizes six pre-trained convolutional neural networks (CNNs): EfficientNetB3, EfficientNetV2B1, RegNetX008, RegNetX080, RegNetY006, and RegNetY008. In our study, we conducted two experiments. In the first experiment, we trained and evaluated different models using fundus images from the publicly available APTOS dataset. Results—The RegNetX080 model achieved 98.6% accuracy in binary classification, while the EfficientNetB3 model achieved 85.1% accuracy in multi-classification, respectively. For the second experiment, we trained the models using the APTOS dataset and evaluated them using fundus images from Al-Saif Medical Center in Saudi Arabia. In this experiment, EfficientNetB3 achieved 98.2% accuracy in binary classification and EfficientNetV2B1 achieved 84.4% accuracy in multi-classification, respectively. Conclusions—These results indicate the potential of AI technology for early and accurate detection and classification of DR. The study is a potential contribution towards improved healthcare and clinical decision support for an early detection of DR in Saudi Arabia. Full article

The integration of systems for Adaptive Business Intelligence (ABI) in the healthcare industry has the potential to revolutionize and reform the way organizations approach data analysis and decision-making. By providing real-time actionable insights and enabling organizations to continuously adapt and evolve, ABI has the potential to drive better outcomes, reduce costs, and improve the overall quality of patient care. The ABI Interoperability System was designed to facilitate the usage and integration of ABI systems in healthcare environments through interoperability resources like Health Level 7 (HL7) or Fast Healthcare Interoperability Resources (FHIR). The present article briefly describes both versions of this software, learning about their differences and improvements, and how they affect the solution. The changes introduced in the new version of the system will tackle code quality with automated tests, development workflow, and developer experience, with the introduction of Continuous Integration and Delivery pipelines in the development workflow, new support for the FHIR pattern, and address a few security concerns about the architecture. The second revision of the system features a more refined, modern, and secure architecture and has proven to be more performant and efficient than its predecessor. As it stands, the Interoperability System poses a significant step forward toward interoperability and ease of integration in the healthcare ecosystem. Full article

The transition to digital business systems has revolutionized organizational operations, driven by the integration of advanced technologies such as artificial intelligence (AI). This integration indicates a shift, redefining traditional practices and enhancing efficiency across diverse sectors such as finance, healthcare, and manufacturing. This study explores the impact of AI on auditing through a systematic literature review to develop a conceptual framework for auditing practices. The theoretical implications show the transformative role of AI in redefining auditors’ roles, shifting from retrospective examination to proactive real-time monitoring. Moreover, managerial contributions stress the benefits of AI integration, enabling informed decision-making in risk analysis, financial management, and regulatory compliance. Future research should explore AI’s influence on auditing efficiency, performance, regulatory challenges, and auditor adaptation. Overall, this study underlines the importance for organizations to embrace AI integration in auditing practices, fostering innovation, competitiveness, and resilience. Full article

While artificial intelligence has successful and innovative applications in common medicine, could its application facilitate research on rare diseases? This study explores the application of artificial intelligence (AI) in orphan drug research, focusing on how AI can address three major barriers: high financial risk, development complexity, and low trialability. This paper begins with an overview of orphan drug development and AI applications, defining key concepts and providing a background on the regulatory framework of and AI’s role in medical research. Next, it examines how AI can lower financial risks by streamlining drug discovery and development processes, analyzing complex data, and predicting outcomes to improve our understanding of rare diseases. This study then explores how AI can enhance clinical trials through simulations and virtual trials, compensating for the limited patient populations available for rare disease research. Finally, it discusses the broader implications of integrating AI in orphan drug development, emphasizing the potential for AI to accelerate drug discovery and improve treatment success rates, and highlights the need for ongoing innovation and regulatory support to maximize the benefits of AI-driven research in healthcare. Based on those results, we discuss the implications for traditional and AI-powered business in the drug industry. Full article

With the rapid development of information technology, digital health technologies have become increasingly prevalent in the field of healthcare. In this study, business intelligence (BI) techniques were combined with research-based prediction models to increase the efficiency and quality of healthcare practices. A data scenario involving 200 older adults with various measurements, including health beliefs, social support, self-efficacy, and disease duration, was used to establish a medication adherence prediction model in a BI system. A regression model, logistic regression model, tree model, and score-based prediction model were used to predict medication adherence among older adults. The developed BI-based prediction model has visualization, real-time feedback, and data updating functionality. These features enhanced the effectiveness of prediction models in clinical practice. Healthcare professionals can incorporate the proposed system into their care practice for health assessments and management, and patients can use the system to manage themselves. The developed BI-based care system can also be used to achieve effective communication and shared decision-making between care managers and patients. Further empirical studies integrating prediction models into the proposed BI system for assessment, management, and decision-making in healthcare practice are warranted. Full article

In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into the field of customer relationship management (CRM), bringing benefits as well as difficulties to businesses. AI, particularly in the context of CRM, employs machine learning (ML) and deep learning (DL) techniques to extract knowledge from data, recognize trends, make decisions, and learn from mistakes with minimal human intervention. Successful firms have effectively integrated AI into CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots and virtual assistants, and voice and speech recognition. AI-driven chatbots, one of the AI-powered CRM systems, arose as a disruptive approach to customer service, and as such, unfolded with economic and managerial ramifications in CRM. Given the literature’s focus on other AI-driven systems, there is an obvious need for an investigation of industry applications and the implications of AI-driven chatbots in CRM. The purpose of this study is to explore and elucidate the economic and managerial implications of AI-powered chatbots within CRM systems. This investigation aims to provide a comprehensive understanding of how these technologies can enhance customer interactions, streamline business processes, and impact organizational strategies. To reach this goal, this study conducts a comparative qualitative analysis based on many interviews with experts and contributors in the field. Interviews with CRM specialists yielded insights into the use of AI-driven chatbots in CRM and their impact on the industry. The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance. In addition, AI chatbots have proven useful in a variety of industries, including retail and tourism. Nonetheless, there were limitations to its usage in the healthcare system, particularly in terms of ethical problems. Full article

In an era of swift technical advancement, the confluence of digital technology, security, and the digital economy bears substantial implications. This research aims to investigate the complex interplay among patient rights, genetic testing, and cloud computing, with a particular emphasis on the legal contexts that govern these fields. Individuals must possess the ability to properly interact with health-related information and understand the economic components of digital platforms. Genetic testing and cloud computing are two areas where these literacies overlap, presenting distinct difficulties and opportunities. Legal considerations cover a wide range of issues, from data privacy and security to regulatory compliance and intellectual property rights. There are also implications for long-term economic growth, particularly in the area of health and well-being. A special economic zone exists at the Hainan Free Trade Port. In addition, this research explores how digital technologies may improve healthcare while considering the security precautions and ethical issues that must be taken to promote sustainable development through genetic testing. It also looks at how genetic data can be used to provide individualized economic outcomes and the roles that artificial intelligence and privacy play in these intertwined domains. The emergence of Web 2.0 has brought about a significant transformation in the digital realm, enabling individuals, businesses, and communities to leverage cutting-edge technologies for benefits in the social, economic, and environmental spheres, and advance sustainable progress. This study examines the opportunities and challenges presented and offers insights into the development of strong legal frameworks and moral standards, as well as the responsible application of these innovations for the benefit of society as a whole. This research will highlight how crucial it is to foster a more sustainable future through digital inclusivity, cooperative problem-solving, data-driven decision-making, and worldwide sustainable practices. Full article

Artificial Intelligence (AI) applications streamline workflows, automate tasks, and require adaptive strategies for effective integration into business processes. This research explores the transformative influence of AI on various industries, such as software engineering, automation, education, accounting, mining, legal services, and media. We investigate the relationship between technological advancements and the job market to identify relevant skills for individuals and organizations for implementing and managing AI systems and human–machine interactions necessary for actual and future jobs. We focus on the essential adaptations for individuals and organizations to flourish in this era. To bridge the gap between AI-driven demands and the existing capabilities of the workforce, we employ the Rapid Review methodology to explore the integration of AI in businesses, identify crucial skill sets, analyze challenges, and propose solutions in this dynamic age. We searched the Scopus database, screening a total of 39 articles, of which we selected 20 articles for this systematic review. The inclusion criteria focused on conference papers and journal articles from 2020 or later and written in English. The selected articles offer valuable insights into the impact of AI on education, business, healthcare, robotics, manufacturing, and automation across diverse sectors, as well as providing perspectives on the evolving landscape of expertise. The findings underscore the importance of crucial skill sets, such as technical proficiency and adaptability, to successfully adopt AI. Businesses respond strategically by implementing continuous skill adaptation and ethical technology to address challenges. The paper concludes by emphasizing the imperative of balanced skill development, proactive education, and strategic integration to navigate the profound impact of AI on the workforce effectively. Full article