Analytical Integration Research Articles

Big Data in Pharmacy: Transforming Patient Care with Analytics

The integration of big data analytics in pharmacy is revolutionizing patient care by providing insights that enhance medication management and therapeutic outcomes. By leveraging vast amounts of health data, pharmacists can deliver more personalized treatment and optimize drug therapy. Despite the potential benefits, existing methods in pharmacy often suffer from data silos, lack of interoperability, and insufficient analytical capabilities, leading to suboptimal patient outcomes and inefficient medication management. These limitations hinder the effective utilization of data for real-time decision-making in patient care. To address these challenges, it propose the Patient Care Transformation using Big Data Analytics (PCT-BDA) framework, which facilitates a holistic approach to patient data integration and analysis within smart grid systems. This framework enables seamless data flow between various healthcare entities, improving collaboration and fostering a comprehensive view of patient health profiles. The proposed method emphasizes real-time analytics, predictive modeling, and machine learning algorithms to enhance decision-making processes in medication management. By utilizing big data analytics, pharmacists can better predict patient responses, identify potential drug interactions, and tailor therapy plans to individual needs. Preliminary findings from the implementation of the PCT-BDA framework indicate significant improvements in patient outcomes, including reduced medication errors, enhanced adherence to treatment protocols, and overall increased satisfaction with pharmacy services. This innovative approach highlights the transformative potential of big data analytics in optimizing patient care in the pharmacy sector.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Exploring advanced techniques in process automation and control: A generic framework for oil and gas industry applications

This paper explores advanced techniques in process automation and control, offering a generic framework tailored for applications within the oil and gas industry. The framework addresses the growing need for operational efficiency, safety, and sustainability in an industry characterized by complex, high-risk processes. The adoption of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Industrial Internet of Things (IIoT) has revolutionized process control systems, enabling real-time monitoring, predictive maintenance, and optimization of production assets. Advanced Process Control (APC) techniques, including model predictive control (MPC), multivariable control systems, and data-driven analytics, play a pivotal role in enhancing production, minimizing downtime, and reducing operational costs. This generic framework leverages the integration of AI and big data analytics for optimizing control strategies, ensuring accurate decision-making and improving operational responsiveness to changing conditions. Additionally, the paper examines how automated control systems mitigate risks associated with human error, while enhancing safety protocols by providing early warnings and real-time insights into equipment performance. Automation also supports the industry's shift towards more sustainable practices by improving energy efficiency, minimizing environmental impact, and enabling remote monitoring in hazardous locations. Through case studies and examples, the paper illustrates the successful implementation of these techniques in upstream, midstream, and downstream operations, highlighting the significant role of digital transformation in driving innovation. The proposed framework can be adapted to diverse oil and gas settings, serving as a foundation for developing scalable, flexible, and sustainable automation solutions. By outlining the key components and benefits of advanced process automation and control, this study contributes to the ongoing discourse on how technological advancements can reshape operational strategies in the oil and gas sector. Keywords: Process Automation, Control Systems, Oil And Gas Industry, Artificial Intelligence (AI), Machine Learning (ML), Industrial Internet of Things (IIoT), Advanced Process Control (APC), Model Predictive Control (MPC), Predictive Maintenance, Digital Transformation, Sustainability.

Digital Twin Used in Real-Time Monitoring of Operations Performed on CNC Technological Equipment

This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and .NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources.

ANTHROPOLOGY OF THE FUTURE: HOW TECHNOLOGY WILL CHANGE OUR UNDERSTANDING OF HUMANITY IN 2025

As we venture into 2025, the intersection of anthropology and technology presents a fascinating frontier for understanding humanity. This article explores the transformative impact of emerging technologies on our perceptions and experiences of being human. Anthropology, traditionally focused on the study of human societies, cultures, and evolutionary development, is now increasingly intertwined with the rapid advancements in technology. (Lytvynova, L. V. 2004) The article delves into the evolution of anthropological studies in the context of technological innovation. Historically, anthropology has provided insights into the cultural and social dynamics of human societies. However, the advent of digital technologies, artificial intelligence, and biotechnology is reshaping these dynamics, prompting anthropologists to reconsider their frameworks and methodologies. The integration of big data analytics, for instance, allows for a more nuanced understanding of human behavior on a global scale, uncovering patterns and trends that were previously elusive. Next, the article examines how technologies such as virtual reality (VR) and augmented reality (AR) are revolutionizing anthropological research and education. These immersive technologies enable researchers to recreate and explore ancient civilizations, offering new perspectives on historical and cultural contexts. Moreover, VR and AR provide powerful tools for education, allowing students to engage with anthropological content in more interactive and impactful ways.

Machine learning and analytic hierarchy process integration for selecting a sustainable tractor.

Selecting the appropriate tractor for small-scale farms is a complex process due to the multitude of technical, environmental, and economic criteria that must be evaluated. This study addresses this challenge by integrating the Analytic Hierarchy Process (AHP) with machine learning (ML) to reduce the number of criteria and simplify the decision-making process. The research aims to determine the most relevant criteria aligned with sustainable development goals for selecting the right tractor, focusing on small farms in the Egyptian Delta. Four tractors, with horsepower ranging from 55 to 95, were evaluated based on inputs from forty-two governmental service providers in the study area. Initially, nine criteria were identified, encompassing key technical, environmental, and economic factors. These criteria were reduced to three-price, power, and maintenance costs by weights 0.142, 0.334, and 0.525, respectively-using Hierarchical Agglomerative Clustering with Euclidean distance. This reduction streamlined the selection process, making it more practical for farmers. Results show that the second tractor (T2), with a priority score of 0.326 and a normalized value of 33.4%, emerged as the optimal choice for small-scale farmers, outperforming the first tractor (T1) (28.7%) and third tractor (T3) (21%). Integrating AHP and ML simplifies tractor selection, ensuring sustainability, cost-efficiency, and operational effectiveness.

Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing

The rapid evolution of Advanced Driver Assistance Systems (ADAS) has transformed the automotive landscape, necessitating a shift towards more customer-centric development strategies. This paper explores the integration of predictive analytics with Customer Relationship Management (CRM) data to foster innovations in ADAS development. By harnessing insights from customer interactions, preferences, and feedback, manufacturers can anticipate market demands and tailor ADAS features to enhance user experience. Through a comprehensive analysis of case studies and industry practices, we demonstrate how predictive analytics can improve decision-making processes, facilitate the identification of emerging trends, and optimize resource allocation. The findings underscore the potential of leveraging CRM data to drive customer- focused innovations, ultimately resulting in enhanced vehicle safety, satisfaction, and competitive advantage in the automotive sector.

Application of Internet of Things (IoT) on Monitoring EC and pH Values for Fertigation System

This paper introduces a novel Internet of Things (IoT) based fertigation control system designed to enhance traditional agricultural practices in Malaysia, particularly in rural areas. The system integrates a fertigation approach, combining fertilization and irrigation, with smart devices and a NodeMCU ESP32 microcontroller to automate the tracking of data from liquid fertilizer mixtures. By utilizing EC and pH sensors, the system accurately monitors the nutrient concentration and pH levels of the fertilizer mix, with data displayed on an IoT platform for real-time observation and control. This approach mitigates common issues such as overfertilization and inefficient nutrient use, offering significant improvements over manual data collection methods. The system's performance was validated, showing successful monitoring and control of the fertilization process, leading to enhanced plant health and crop quality. Key findings indicate that the system's integration with IoT technology provides a more efficient, automated solution for managing fertilizer applications, reducing labor and time while ensuring optimal plant nutrient levels. The study also highlights the potential for future development, including the integration of additional sensors and advanced data analytics to optimize fertigation practices further. The implementation of this IoT-based system represents a significant advancement towards sustainable agriculture, offering practical benefits for Malaysian farmers and setting a foundation for further innovation in fertigation technology.

Optimizing contract negotiations in clinical research: Legal strategies for safeguarding sponsors, vendors, and institutions in complex trial environments

This paper examines the complexities of clinical trial contract negotiations, focusing on legal strategies and technological innovations that safeguard the interests of sponsors, vendors, and institutions. In an increasingly globalized and digital research environment, contract negotiations are pivotal to ensuring compliance, protecting intellectual property, and mitigating legal risks. The study’s purpose was to explore key components of clinical trial contracts, address the challenges involved, and assess future trends, particularly in light of evolving regulatory frameworks and technological advancements. Through a comprehensive review of legal literature and emerging technologies, the study identified crucial aspects of contract negotiations, including risk allocation, indemnification, intellectual property rights, and data privacy. Special attention was given to the role of technology—such as AI, blockchain, and contract management platforms—in streamlining the negotiation process. These tools have proven effective in reducing errors, enhancing collaboration, and ensuring regulatory compliance. The main findings revealed that while traditional contract structures remain integral, the adoption of digital tools and a patient-centric approach are critical in addressing new challenges posed by decentralized trials and cross-border collaborations. The study concluded that stakeholders must embrace flexible contract frameworks, incorporate robust data protection measures, and leverage technology to optimize negotiations. Recommendations include the integration of advanced analytics for decision-making, the adoption of blockchain for contract security, and a heightened focus on patient rights in contract structures to ensure ethical conduct and compliance in clinical research.

Harnessing big data for national security: A review of information systems and emerging technologies in the U.S

The rapid growth of big data and emerging technologies has fundamentally transformed the national security landscape, enabling governments to leverage vast amounts of information for decision-making, threat detection, and operational effectiveness. This review explores the integration of big data analytics into U.S. national security systems, focusing on the technological innovations that facilitate real-time data processing, secure communications, and predictive analytics. Key emerging technologies, such as artificial intelligence, blockchain, and the Internet of Things, are evaluated for their role in enhancing intelligence gathering, cybersecurity, and surveillance operations. Furthermore, the paper examines the foundational principles of big data in national security, detailing its data sources, types, and uses in both structured and unstructured forms. While these advancements present significant opportunities, they also pose challenges related to privacy, ethics, policy, and legal frameworks, which are critical for ensuring that the implementation of such technologies aligns with democratic values and security objectives. The paper concludes by highlighting future research directions, emphasizing the need for continued development in big data infrastructure, governance, and ethical considerations to fully harness its potential in protecting national interests.

Navigating the AI Wave: Overcoming Barriers and Unleashing the Potential of Artificial Intelligence in Transforming European Public Health

Abstract Artificial Intelligence (AI) is not just a future promise but an urgent necessity for modern public health. By providing invaluable insights into disease patterns, therapeutic interventions, and overall public health management, AI has the potential to revolutionise healthcare. To tackle essential public health functions effectively, harnessing AI must become a top priority. However, there is an urgent need for a cohesive strategy across Europe. Currently, varying readiness levels among European nations regarding AI adoption in health result in uneven progress across the continent. This disparity must be addressed to ensure all countries benefit equally from AI advancements. Recognising this, the World Health Organization European Regional Office launched a regional report on digital health in 2023. The report evaluated the integration of big data and advanced analytics, including AI, in health systems. Findings revealed that while 60% of Member States have a national data strategy, only 35% have a policy regulating big data and AI in health, and 38% lack both. This highlights a critical gap that must be filled urgently. Our upcoming session will address these challenges head-on. Organised to provide visionary insights, practical applications, and a landscape view of artificial intelligence in public health, the presentations will be followed by a roundtable discussion in which panellists will delve into practical challenges surrounding AI adoption. They will reflect on the profound impact AI could have on the future of European health systems and offer pragmatic and responsible steps forward, culminating in achievable recommendations for public health professionals. To enhance the session, we will utilise existing Generative AI tools to provide a real-time summary of the plenary and reinforce the call to action in alignment with panellists’ recommendations. This approach ensures that the session discusses the urgent need for AI in public health and actively demonstrates its practical applications. Moderators Natasha Azzopardi Muscat Director, Division of Country Health Policies and Systems, WHO Regional Office for Europe Dimitra Panteli Programme Manager/Senior Health Systems Analyst, European Observatory on Health Systems and Policies Facilitator Stefan Buttigieg Vice-President, EUPHA Digital health section Speakers/Panellists Martin McKee Professor of European Public Health, London School of Hygiene & Tropical Medicine, UK Katharina Ladewig Director, Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Germany Marco Marsella Director Digital, EU4Health and Health systems modernisation, Directorate-General for Health and Food Safety (DG SANTE), European Commission Keyrellous Adib Technical Officer Data Science and Digital Health, WHO Regional Office for Europe

FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization

ABSTRACTAnnual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free‐stream wind conditions. Physics‐based estimates of wind farm power production typically rely on low‐fidelity engineering wake models that approximate the steady‐state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed‐form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient‐based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.

Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high-throughput workflows, helping researchers recognize low-integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck─in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source Python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semiautonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Initially developed for integration with Thermo Orbitrap workflows, Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS.

Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review

In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, and their synergistic combinations with traditional optimization and simulation methods. By employing AI capabilities, logistics stakeholders can enhance decision-making processes, optimize resource utilization, and minimize environmental impacts. Moreover, this paper identifies and analyzes prominent challenges within sustainable logistics, such as reducing carbon emissions, minimizing waste generation, and optimizing transportation routes while considering ecological factors. Furthermore, the paper explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems, which hold immense potential for enhancing efficiency and sustainability. Finally, the paper outlines future research directions, emphasizing the need for further exploration of hybrid AI approaches, robust optimization frameworks, and scalable solutions that accommodate dynamic and uncertain logistics environments.

Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis

This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model’s adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences.

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

Optimizing pharmaceutical inventory management: A global framework for efficiency and cost reduction

Pharmaceutical inventory management is critical in ensuring continuous drug availability and cost efficiency in healthcare institutions globally. However, maintaining optimal inventory levels is challenging due to factors such as overstocking, stockouts, and global supply chain disruptions. This paper reviews best practices for efficient pharmaceutical inventory management, emphasizing the integration of data analytics and predictive models to forecast demand, reduce waste, and streamline procurement. It explores strategies for implementing unified inventory systems across diverse healthcare settings, balancing cost efficiency with uninterrupted drug availability. Additionally, the paper examines the impact of global supply chain disruptions on pharmaceutical supply and recommends solutions for building resilience. Through real-world examples and recommendations for future improvements, this review highlights the potential for enhanced global inventory management practices to reduce costs while ensuring the continuous availability of essential medications. Keywords: Pharmaceutical Inventory Management, Data Analytics, Cost Efficiency, Predictive Models, Supply Chain Resilience, Drug Availability

Advancing AML tactical approaches with data analytics: Transformative strategies for improving regulatory compliance in banks

The growing complexity of financial crimes necessitates advanced Anti-Money Laundering (AML) strategies that leverage data analytics to improve regulatory compliance in banks. As traditional AML methods face challenges in detecting sophisticated money laundering schemes, data analytics offers transformative solutions by enabling real-time monitoring, enhanced risk detection, and predictive analysis. This review explores the integration of data analytics in AML systems and its impact on regulatory compliance, focusing on strategies that banks can adopt to mitigate risks and adhere to evolving regulations. Data analytics empowers financial institutions to analyze vast amounts of transactional data, identifying suspicious patterns and anomalies with greater precision. Machine learning algorithms and artificial intelligence (AI) further enhance these capabilities by automating risk assessments, reducing false positives, and improving decision-making processes. Through predictive analytics, banks can anticipate emerging threats, adapting their AML strategies proactively to counter new money laundering techniques. A key advantage of data-driven AML approaches is the ability to streamline compliance processes. By automating Know Your Customer (KYC) procedures and cross-referencing data from multiple sources, banks can efficiently verify customer identities and monitor for unusual behavior. Additionally, the adoption of data analytics improves reporting accuracy, ensuring compliance with stringent regulatory frameworks such as the Financial Action Task Force (FATF) and the Bank Secrecy Act (BSA). This review highlights the transformative role of data analytics in enhancing AML efforts, emphasizing the importance of real-time data integration, predictive modeling, and automation. The shift from reactive to proactive AML approaches not only strengthens regulatory compliance but also fosters a culture of vigilance and risk management within banks. As financial institutions continue to embrace digital transformation, leveraging data analytics for AML will be crucial in combating financial crimes and maintaining compliance in an increasingly complex regulatory environment. Keywords: Anti-Money Laundering (AML), Data Analytics, Regulatory Compliance, Banks, Financial Crime, Machine Learning, Artificial Intelligence, Know Your Customer (KYC), Predictive Analytics, Risk Management.

Digital Transformation in Talent Acquisition: An Investigation into the Integration of Big Data Analytics and Management Information Systems for Optimal Hiring Strategies

The primary goal was to empirically investigate the relationships amongst Big Data Analytics (BDA), Management Information System (MIS), optimal hiring strategies, leadership support, and talent acquisition process. This research aimed to fill crucial gaps in the literature by examining how technology adoption and leadership support influence the formulation and effectiveness of optimal hiring strategies, finally impacting talent acquisition results in the Chinese business landscape. Quantitative research was employed, targeting experts and decision-makers in human resource departments across numerous industries in China. The research used a random sampling approach to acquire facts through an online questionnaire, examining responses from 168 participants. Regression and mediation analyses, facilitated by SPSS, were applied to discover the relationships and mechanisms among BDA, MIS, optimal hiring strategies, leadership support, and talent acquisition success. The results discovered a significant and positive relationship between BDA and MIS with the optimal hiring techniques. Moreover, optimal hiring techniques have proven an advantageous association with talent acquisition success. The research similarly showed the mediating role of optimal hiring strategies in translating the advantages of BDA and MIS into talent acquisition success. Leadership support emerged as a moderator, amplifying the relationship between optimum hiring techniques and talent acquisition success. This study contributes empirical proof to the underexplored intersection of leadership support, technology, and talent hiring strategies. The findings offer practical insights for companies aiming to navigate the complexities of recruitment, emphasizing the significance of strategic decision-making, technology integration, and management endorsement in achieving talent acquisition success.

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.