Sadiqur Rahman Chowdhury Urbi

The COVID-19 pandemic magnified persistent structural weaknesses within the U.S. healthcare workforce, intensifying psychological strain, professional exhaustion, and staff turnover. This scoping review synthesizes 40 peer-reviewed studies published between 2020 and 2024 to examine key drivers of post-pandemic burnout, identify system-level factors influencing workforce attrition, and assess the role of cultural competency in enhancing organizational resilience. Findings indicate that a lack of work-life balance, moral injury, and inadequate institutional support remain the dominant contributors to burnout. Thematic analysis highlights several effective retention strategies, including inclusive leadership, flexible scheduling, and trauma informed support systems. Cultural competence implemented through equity audits, diverse hiring panels, and culturally responsive mental health resources emerged as a critical moderator of employee engagement and psychological safety. Institutions that embedded cultural inclusion within governance frameworks reported notable improvements in job satisfaction, reduced attrition, and enhanced workforce cohesion. This study advocates for equity-centred leadership, the institutionalization of DEI practices, and a redefinition of burnout and retention as strategic, not peripheral, concerns. Policy recommendations include mandated DEI metrics, psychological safety standards, and intersectional workforce accommodations. Overall, this review positions cultural competency as a foundational pillar of post-pandemic workforce recovery and sustainability in American healthcare.

Innovation drives economic progress, but patent arrangements designed to encourage innovation increasingly conflict with market equality and consumer accessibility. This research investigates the ethical difficulties associated with intellectual property protection in various industries, notably with essential goods. Patent holders exert significant influence over markets, price, and accessibility, prompting questions regarding their ethical obligations beyond mere legal compliance. Pharmaceutical patents demonstrate these tensions very well. Government funding for research and lifesaving pharmaceuticals produced through public-private partnerships sometimes remains unattainable for individuals in the most critical situations. Technology patents reveal unique yet equally troubling patterns, as the protection of innovation evolves into commercial exploitation via strategic litigation and portfolio consolidation. These activities indicate that patent regimes may be compromising their fundamental goal of promoting innovation that benefits society. Current regulatory regimes inadequately address these ethical considerations. Patent law primarily focuses on technical originality and financial entitlements, sometimes neglecting wider societal consequences. This restricted focus allows for behaviors that may comply with legal criteria but violate ethical principles of fairness, accessibility, and public welfare. Market failures in patent protected sectors exemplify the costs linked to this approach. Reform requires the recognition that patent rights impose societal responsibilities. Potential solutions include public interest licensing for government-funded research, price restriction for essential patents, and strengthened antitrust enforcement against patent misuse. Innovation policy should go beyond safeguarding inventors to guarantee that innovation serves humanity's interests. This transformation necessitates new frameworks that align corporate incentives with public welfare, making patent systems responsible to the societies that provide them legitimacy.

Artificial Intelligence (AI) is reshaping the landscape of business analytics by enabling a shift from retrospective evaluation to predictive, autonomous, and strategic decision-making. This review critically examines the patterns of AI adoption across key U.S. sectors finance, healthcare, retail, and manufacturing highlighting sector-specific enablers, institutional readiness, regulatory landscapes, and performance implications. Employing a thematic qualitative framework supported by organizational maturity models and sectoral readiness matrices, the study uncovers both the transformative potential and systemic barriers to scalable AI integration. Issues such as data fragmentation, ethical ambiguity, skill deficits, and governance inconsistencies are identified as key obstacles. Cross-sector innovation spillovers are analyzed to demonstrate how transferable AI solutions can enhance organizational learning and adaptability. The study concludes with actionable recommendations for aligning technological investment with ethical governance, workforce capability, and regulatory compliance. These findings provide a policy-relevant knowledge base for industry leaders, regulators, and researchers aiming to implement sustainable, responsible, and context-sensitive AI strategies.

This review investigates how artificial intelligence (AI) and predictive analytics are reshaping hospital management by enhancing clinical decision-making, operational efficiency, and equitable healthcare delivery. People are increasingly applying AI-driven models to forecast patient demand, optimize workforce planning, improve diagnostic accuracy, and expand telemedicine, especially in the post-pandemic era. When implemented responsibly, predictive tools across healthcare systems can reduce readmissions, strengthen patient outcomes, and support resource utilization. Successful adoption is enabled by robust digital infrastructure, committed leadership, and cross disciplinary collaboration, yet barriers persist, including fragmented IT systems, interoperability gaps, algorithmic bias, and workforce skill shortages. Ethical and governance challenges centered on transparency, data protection, and accountability remain pivotal to sustainable clinical integration. Comparative insights reveal divergent adoption pathways: high-income countries leverage mature digital ecosystems, while low- and middle-income contexts are innovating with resource-sensitive applications to address workforce constraints and expand access. By synthesizing theoretical frameworks and global case studies, this review underscores that responsible AI adoption can accelerate hospital transformation. Policy recommendations emphasize that it is time for standardized validation, strengthened data ecosystems, inclusive telehealth expansion, and institutional capacity-building to advance efficiency, resilience, and equity in healthcare systems.

Efficient financial management is crucial for the sustainability and efficacy of contemporary healthcare systems. This analysis rigorously assesses cost containment initiatives implemented by Non-Profit (NFP) and For-Profit (FP) institutions and examines the financial consequences of avoidable hospital readmissions. The study does a narrative synthesis in accordance with PRISMA 2020 guidelines, analyzing 62 peer reviewed papers published from 2010 to 2025. The assessment classifies cost conservation measures into four principal themes: operational efficiency, technological investment, administrative reorganization, and value-based care delivery. It indicates that FP hospitals primarily employ assertive cost-reduction and revenue-maximization strategies, such as outsourcing, performance-based contracting, and digital billing optimization. NFP hospitals prioritize long-term sustainability by focusing on community-oriented initiatives, care integration, and workforce development. The analysis emphasizes that unnecessary hospital readmissions intensify cost burdens, particularly for disadvantaged populations, affecting reimbursement schemes and institution rankings. The results demonstrate the need to synchronize financial management with fairness, policy adherence, and health outcomes. Policy proposals highlight the integration of value-based incentives, the standardization of readmission penalties, and the assurance of openness in ownership-driven financial practices. The analysis indicates that a hybrid framework for cost reduction that balances efficiency, accountability, and equity is essential for promoting sustainable and equitable healthcare delivery across various institutional systems

The advent of AI-driven Automated Machine Learning (AutoML) has redefined business analytics, enabling organizations to automate data preprocessing, feature engineering, model selection, and hyperparameter optimization, thereby accelerating predictive modeling and expanding access to advanced analytics. This systematic narrative review synthesizes findings from 84 peer-reviewed and industry publications (2007–2024) sourced from Scopus, Web of Science, IEEE Xplore, Google Scholar, and SSRN. Applications across retail, finance, manufacturing, and healthcare demonstrate measurable impacts, including a 25% reduction in equipment downtime, 20% gains in customer engagement, and deployment cycle reductions of up to 70%. Core enabling methods such as neural architecture search, Bayesian optimization, and meta learning enhance predictive accuracy and operational efficiency, while sector specific adaptations improve compliance and contextual relevance. Key challenges include model interpretability, computational scalability, data quality, bias mitigation, and integration into existing business processes. Emerging solutions, including federated learning, causal AutoML, and hybrid neuro-symbolic architectures, aim to address these constraints while safeguarding ethical and regulatory alignment. Future research should prioritize domain-specific, resource-efficient, and transparent AutoML frameworks that balance automation with human oversight, fostering robust, explainable, and operationally viable decision-support systems.

Joining business analytics (BA) in bookkeeping has become progressively basic due to the rising request for data-driven decision-making in a quickly advancing budgetary environment. As bookkeeping experts confront challenges such as complex information sets and a growing mechanical scene, the part of BA in bookkeeping hones is becoming more noticeable. Despite its potential, the broad appropriation of BA remains conflicting, with numerous organizations, however, completely tackling its benefits. This survey synthesizes the existing writing on the crossing point of business analytics and bookkeeping, looking at the openings and challenges related to this integration. A comprehensive investigation of peer-reviewed articles, case considerations, and industry reports distinguishes key subjects, counting the effect of BA on decision-making, operational productivity, and vital arranging. The discoveries demonstrate that whereas BA has the potential to improve bookkeeping homes, its appropriation is ruined by components such as a need for skill, asset imperatives, and resistance to altering inside conventional bookkeeping situations. The survey emphasizes the expanding significance of counterfeit insights (AI) and machine learning in reshaping bookkeeping forms, recommending that future headways in advances will play a basic part in quickening BA appropriation. The think tank moreover investigates the suggestions for bookkeeping experts and organizations, advertising proposals for overcoming these selection boundaries. Generally, the survey concludes that overcoming these challenges through ventures in instruction, mechanical foundation, and altered administration techniques is fundamental for bookkeeping experts to completely capitalize on the benefits of business analytics within the advanced time.

In the digital age, as data is increasingly recognized as a strategic asset, business analytics (BA) has become a critical factor in organizational decision-making. From common decision support systems to advanced concentrated analytics platforms and AI used in modern business environments, this review provides an inspection of business analytics development. The integration of cloud computing and machine learning (ML) is one of the main topics tested in this article, emphasizing how these technologies change their business strategy. The review also examines the number of methods and tools created to process and analyze large data sets, such as real-time analytics, data visualization, and prediction analytics. The autonomous decision-making capabilities of AI-driven manufacturing systems and the growing importance of ethical issues in data analytics are two examples of how testing demonstrates the future path of business analytics. By emphasizing the essential role that business analytics plays as a navigation role in the sophistication of the digital economy, this assessment finds ways to provide deep information to researchers and students. The analysis further explores the security, data security, and data combinations that aren't structured in the decision-making processes. Finally, the paper assesses a complete perspective on how business analytics changes decisions and the amendment of the competitive environment of industries worldwide. This analysis emphasizes the importance of advanced analytics techniques to maintain a competitive advantage while businesses continue to change in a world that focuses on data.

Background: The integration of Health Analytics tools such as business analytics (BA) and human resource (HR) analytics tools in healthcare has the potential to improve pharmacological management and patient outcomes. This study explores how these analytical tools can optimize drug administration processes, reduce medication errors, and enhance workforce efficiency in hospital settings. Methods: A mixed-methods approach was employed, combining case studies with empirical data analysis from hospital records, including medication administration logs, adverse drug event reports, staffing schedules, and patient health outcomes. Predictive modeling and machine learning techniques were utilized to identify patterns and correlations. Results: The application of BA facilitated the early detection of discrepancies in medication dispensing, leading to a 15% reduction in adverse drug events. HR analytics contributed to improved staff scheduling, decreasing nurse fatigue and associated medication errors by 20%. The synergistic use of BA and HR analytics resulted in enhanced patient recovery rates and optimized pharmacological interventions. Conclusion: The strategic implementation of business and HR analytics in hospital management can significantly improve pharmacological practices and patient health outcomes. This integrative approach supports the journal's emphasis on biomedical research and health interventions by demonstrating the practical applications of analytics in enhancing therapeutic efficacy and patient care.