The Significance of AI in Evidence-based Practice in Healthcare (Published)
This paper examines the transformative potential of Artificial Intelligence (AI) in enhancing evidence-based practice (EBP) within healthcare. By leveraging AI-driven clinical decision support systems, natural language processing, and advanced diagnostic tools, the study explores how these technologies can streamline the synthesis and application of medical evidence to improve clinical decision-making and patient outcomes. Through a comprehensive literature review and analysis of case studies, we highlight the significant impact of AI on reducing administrative burdens, minimizing diagnostic errors, and enabling personalized care. In addition to these benefits, the paper also addresses key challenges such as ethical concerns, technical limitations, and potential biases. The findings underscore the need for continued interdisciplinary collaboration and the development of transparent and adaptive AI systems to ensure that these innovations effectively complement and enhance clinical workflows.
Keywords: Artificial Intelligence, Clinical Decision Support, Evidence-Based Practice, Healthcare, clinical data analysis, deep learning, natural language processing, reinforcement learning
Enhancing Healthcare Data Security with Cloud Identity Solutions (Published)
Healthcare organizations face unprecedented challenges in securing patient data while maintaining operational efficiency in an increasingly digital landscape. Cloud-based identity and access management solutions have emerged as critical infrastructure components for protecting sensitive medical information across distributed healthcare environments. These platforms address the complex requirements of modern healthcare delivery by implementing sophisticated authentication mechanisms, granular access controls, and comprehensive audit capabilities that align with stringent regulatory frameworks including HIPAA and GDPR. The technical architecture incorporates industry-standard protocols such as SAML, OAuth, and OpenID Connect while supporting healthcare-specific standards like HL7 FHIR for seamless interoperability. Advanced features including multi-factor authentication, risk-based access controls, and identity federation enable healthcare providers to secure access across multiple applications and organizational boundaries without impeding clinical workflows. Implementation strategies emphasize phased deployment approaches, automated lifecycle management, and continuous monitoring to ensure robust security postures while reducing administrative overhead. Real-world deployments demonstrate significant improvements in security metrics, operational efficiency, and regulatory compliance, positioning cloud identity solutions as foundational elements for future healthcare digital transformation initiatives.
Keywords: Authentication, Compliance, Healthcare, Identity, Security
Unifying Healthcare Through MDM: Paving the Way for Precision Medicine and Population Health (Published)
As healthcare systems generate increasingly complex datasets, from EHRs and genomic profiles to social and behavioral determinants, the need for an integrated, reliable data infrastructure has never been greater. This paper explores the critical role of Master Data Management (MDM) in addressing fragmentation and inconsistency in healthcare data, and its strategic application in advancing both precision medicine and population health. Through a synthesis of peer-reviewed research, industry case studies, and regulatory frameworks, the study demonstrates how MDM enables accurate patient identity resolution, data standardization, and semantic interoperability. These capabilities support the creation of unified patient records, which serve as the foundation for individualized treatment plans, chronic disease surveillance, and targeted public health interventions. The findings underscore MDM’s transition from a backend data utility to a strategic enabler of personalized and population-wide care.
Keywords: Healthcare, master data management (MDM), patient 360-degree view, population health, precision medicine
Testing Healthcare AI Algorithms with Quantum Computing: Enhancing Validation and Accuracy (Published)
Due to its capacity to handle information in fundamentally new ways, leading to computational powers that were previously unreachable, the multidisciplinary subject of quantum computing has recently grown and attracted significant interest from both academia and industry. Quantum computing has great promise, but how exactly it will change healthcare is still largely unknown. The potential of quantum computing to transform compute-intensive healthcare tasks like drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization is the primary focus of this survey paper, which offers the first comprehensive analysis of quantum computing’s diverse capabilities in improving healthcare systems. A new era in healthcare is on the horizon, thanks to quantum computing and AI coming together to transform complicated biological simulations, the processing of genetic data, and advances in drug development. Biological data may be extremely large and complicated, making it difficult for traditional computing tools to handle. This slows down and impairs the accuracy of medical discoveries. Combining the predictive power of AI with the exponential processing speed of quantum computers presents a game-changing opportunity to speed up biological research and clinical applications. The function of quantum machine learning in improving drug discovery molecular dynamics simulations powered by artificial intelligence is discussed in this article. Quickly modeling chemical interactions, analyzing drug-receptor binding affinities, and predicting pharmacokinetics with extraordinary precision are all possible with quantum-enhanced algorithms. To further improve disease progression prediction and therapeutic target identification, we also investigate quantum-assisted deep learning models for understanding complex biological processes like protein folding, epigenetic changes, and connections between metabolic pathways.
Keywords: AI, CNN, Healthcare, quantum computing, reinforcement learning
Revolutionizing Regulatory Compliance in Healthcare with Artificial Intelligence (Published)
The healthcare industry faces a significant challenge in maintaining regulatory compliance due to the constant changes of state and federal mandates. On average, more than 40 new mandates are issued each month per state alongside approximately 1 to 7 federal mandates, creating significant challenges for healthcare providers, payers, and other stakeholders. Manually tracking, interpreting, and implementing these changes is a complex and resource-intensive process, making it difficult for organizations to maintain full compliance [1, 2]. In 2022 alone, healthcare providers faced over 600 new and updated regulations, with significant fines and penalties for non-compliance [3]. Non-compliance can result in huge penalties, operational disruptions, and reputational damage [8]. This article explores how Artificial Intelligence (AI) can automate the compliance process, ensuring 100% adherence to regulatory requirements. We discuss the challenges of manual compliance, evaluate various Large Language Models (LLMs) for their effectiveness in detecting policy changes, and outline the implementation process for AI-driven solutions.
Keywords: Artificial Intelligence, Healthcare, revolutionizing regulatory compliance
Towards Integration of Ontologies in Healthcare (Published)
Digital health is facing many challenges. Nowadays the use of ontologies in health care has increased and is covering wide range domains in healthcare. Using ontologies may improve the semantic interoperability and also offer the possibility to gain knowledge from them. It is significantly important not only to implement ontologies in healthcare but to integrate them in order to benefit from different ontologies. In this paper, we provide a comprehensive overview of the importance, advantages and challenges in integrating ontologies through a semantic mapping scenario between two ontologies. Integration of ontologies may support the decision-making process of healthcare providers by deriving relationships between different sets of conditions, findings, signs or symptoms.
Keywords: Healthcare, Integration, Mapping, Ontology