In the digital age, organizations must continuously innovate to stay competitive, and employee well-being has emerged as a crucial factor in fostering this innovation. This study explores the interconnectedness between employee well-being and innovativeness, highlighting how both constructs play a pivotal role in enhancing organizational performance and longterm success. The research employs bibliometric analysis and semantic data mining techniques to uncover the key themes and trends within the fields of well-being and innovativeness. The study reveals that employee well-being is a multifaceted concept, encompassing physical health, mental health, job satisfaction, and life satisfaction, all of which significantly contribute to a positive work environment. Employees who experience high levels of well-being are more likely to engage in innovative activities, as they feel supported, valued, and psychologically safe. Innovativeness, in turn, is a key driver of organizational growth, with factors such as organizational culture, leadership, and resource availability influencing employees’ ability to generate and implement new ideas. This study finds that well-being and innovativeness are mutually reinforcing; organizations that prioritize well-being create a culture that encourages innovation, and innovative environments further enhance employee well-being. The research also underscores the importance of engagement, work-life balance, and leadership in bridging the gap between these constructs. Finally, the study proposes a conceptual framework to guide organizations in integrating well-being initiatives with innovation strategies, which can lead to enhanced employee creativity, motivation, and overall organizational success

Nanomedicine brings nanotechnology and Medicine together in order to develop novel therapies and improve existing treatments. In nanomedicine, atoms and molecules are manipulated to produce nanostructures of the same size as biomolecules for interaction with human cells. This procedure offers a range of new solutions for diagnoses and "smart" treatments by stimulating the body's own repair mechanisms

Research studies on drug utilization in inpatient settings serve as valuable tools for assessing drug prescribing trends, efficiency, and the cost-effectiveness of hospital formularies. Our current study focuses one valuating drug usage patterns, conducting, and assessing clinical outcomes using WHO indicators in healthcare facilities within Government hospitals. Materials and Methods: In a prospective study conducte data Government hospital in Tamil Nadu, data were systematically gathered from 800 prescriptions spanning from November 2025toApril2026.The WHO data collection tool was employed evaluate prescribing indicators. Patients who either passéda way or requested discharge age in st medical advice within the first 24hrof admission were excluded from the data set. The data analysis was carried out using Graph Pad Prism version 10. Results: The average number of drugs per encounter was 2.14. Antibiotics were prescribed in 71% of encounters, while injections were administered in 52%. A total of 80% of drugs were prescribed using generic names in the government care hospital. Regarding hospital stays, 27% of individuals were admitted within three days of treatment, and individual s aged 21to 40 accounted for more than 35%of the total hospital stays. Conclusion: The study demonstrated that cefotaxime was the most frequently prescribed antibiotic. The average number of drugs in this study was slightly over WHO standards. This research motivates clinicians to increase the use of generic drugs, may reduce the expenditures in health care without affecting the efficacy of the drug, and guide more clinicians towards prescribing generic drugs. However, injectable drugs are more prescribed when compared to other formulations. Further, we recommend studies that need more sample sizes and multicentre studies to estimate the overall prescribing practices of orthopedicward

The rapid integration of Artificial Intelligence (AI) and Large Language Models (LLMs) into healthcare has raised critical questions about their reliability in high-stakes clinical decision-making, particularly in prescription counselling and patient advice. This study evaluates the accuracy, safety, and clinical reliability of AI-generated medical prescriptions and medical advice by comparing standardized, high-risk clinical scenarios processed through leading AI models against the consensus of a panel of human healthcare professionals (physicians and clinical pharmacists), regarded as the clinical “gold standard.” Twenty real-world, high-risk clinical scenarios covering pediatric dosing, pregnancy and lactation safety, narrow therapeutic index drugs, drug–drug interactions, and medication storage were formulated and administered as raw, layperson-style prompts. Responses were rated by 173 evaluators on a five-point scale, and descriptive statistics (mean, median) together with chi-square tests of association were used to assess score distributions and the statistical significance of divergence from expected performance. The results reveal an overall discordance rate exceeding 30% between AI-generated responses and expert consensus, with the largest gaps observed in dosage and pharmacological precision (35%), emergency triage failure (28%), and hallucinated or fabricated clinical protocols (22%). Of the twenty scenarios, eleven showed statistically significant divergence in rating distribution (p < 0.05, chi-square test). While AI tools demonstrated clear strengths in administrative efficiency, health literacy translation, and patient education, they consistently underperformed in context-dependent judgment, emergency recognition, and individualized dosing. The study concludes that AI-generated prescriptive advice offers valuable supplementary support but cannot substitute for human clinical oversight, and recommends a mandatory human-in-theloop framework to safeguard patient safety and therapeutic efficacy.